U.S. patent application number 16/988916 was filed with the patent office on 2021-04-29 for bitumen production in paraffinic froth treatment (pft) operations with near infrared (nir) monitoring.
The applicant listed for this patent is FORT HILLS ENERGY L.P.. Invention is credited to Shawn VAN DER MERWE, Xiaoli YANG.
Application Number | 20210122982 16/988916 |
Document ID | / |
Family ID | 1000005328972 |
Filed Date | 2021-04-29 |
![](/patent/app/20210122982/US20210122982A1-20210429\US20210122982A1-2021042)
United States Patent
Application |
20210122982 |
Kind Code |
A1 |
YANG; Xiaoli ; et
al. |
April 29, 2021 |
BITUMEN PRODUCTION IN PARAFFINIC FROTH TREATMENT (PFT) OPERATIONS
WITH NEAR INFRARED (NIR) MONITORING
Abstract
Techniques described herein relate to producing bitumen while
monitoring various aspects of paraffinic froth treatment (PFT)
operations using near infrared (NIR) spectrometry and chemometric
analysis to continuously monitor and enable measurements of
physical and chemical properties of various streams in PFT
operations, which can be done in real time online and can
facilitate process control. NIR spectrometry can be used to acquire
NIR spectra measurements from a PFT process stream and the NIR
spectra measurements and chemometric analysis can, in turn, be used
to determine composition characteristics of the PFT process stream
as well as operational features of a PFT process unit. For example,
NIR spectra can be used to determine upward velocity in a PFT
settler to facilitate settler operation for diluted bitumen quality
control. NIR spectra can be obtained using reflectance or
transmission probes which can be positioned within particular phase
of a stratified PFT process stream.
Inventors: |
YANG; Xiaoli; (Calgary,
CA) ; VAN DER MERWE; Shawn; (Calgary, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
FORT HILLS ENERGY L.P. |
Calgary |
|
CA |
|
|
Family ID: |
1000005328972 |
Appl. No.: |
16/988916 |
Filed: |
August 10, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
15726115 |
Oct 5, 2017 |
10774268 |
|
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16988916 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C10G 2300/1033 20130101;
C10G 1/008 20130101; G01N 21/3577 20130101; C10G 33/04 20130101;
C10G 1/045 20130101; C10G 33/08 20130101; G01N 21/359 20130101;
G01N 2201/129 20130101; C10G 1/047 20130101; G01N 21/3504 20130101;
C10G 2300/208 20130101 |
International
Class: |
C10G 1/00 20060101
C10G001/00; C10G 33/04 20060101 C10G033/04; C10G 33/08 20060101
C10G033/08; C10G 1/04 20060101 C10G001/04; G01N 21/3504 20060101
G01N021/3504; G01N 21/3577 20060101 G01N021/3577; G01N 21/359
20060101 G01N021/359 |
Foreign Application Data
Date |
Code |
Application Number |
Oct 19, 2016 |
CA |
2946027 |
Claims
1. A paraffinic froth treatment (PFT) process, comprising: adding a
paraffinic solvent to bitumen froth to produce a solvent diluted
bitumen froth; in a froth separation unit (FSU), separating the
solvent diluted bitumen froth into a diluted bitumen overflow
stream withdrawn from a lighter phase zone comprising a solvent
diluted bitumen material, and a diluted tailings underflow stream
withdrawn from a heavier phase zone and comprising a diluted
tailings material; monitoring the solvent diluted bitumen material
by acquiring near infrared (NIR) spectral measurements from the
solvent diluted bitumen material within the FSU using an NIR probe
that remains within the lighter phase zone to determine upward
velocity within the lighter phase zone using a pre-determined
direct relationship between the upward velocity and a corresponding
NIR spectral profile, the pre-determined direct relationship being
obtained by determining a set of different upward velocities for a
set of test solvent diluted bitumen materials, and acquiring
respective NIR spectral measurements on the set of test solvent
diluted bitumen materials; and controlling at least one operating
condition of the PFT process based on the NIR spectral
measurements.
2. The PFT process of claim 1, wherein the controlled operating
condition comprises a feed rate of the diluted froth into the FSU,
a dosage of a process-aid, a flow rate of an overflow stream and/or
underflow stream from the FSU, or a solvent-to-bitumen (S/B) ratio
of a PFT process stream, or a combination thereof.
3. The PFT process of claim 1, wherein the NIR spectral
measurements are taken continuously and the upward velocity of the
solvent diluted bitumen material is continuously determined.
4. The PFT process of claim 1, wherein controlling the at least one
operating condition of the PFT process based on the NIR spectral
measurements comprises maintaining the solvent diluted bitumen
material within a pre-determined compositional property.
5. The PFT of claim 4, wherein maintaining the solvent diluted
bitumen material within the pre-determined compositional property
is performed at different inflow and outflow rates.
6. The process of claim 1, wherein the NIR spectral measurements
are acquired using at least one of a transmission probe or a
reflectance probe.
7. The process of claim 1, wherein acquiring respective NIR
spectral measurements on the set of test solvent diluted bitumen
materials comprises testing the set of test solvent diluted bitumen
materials over a range of wavelengths to obtain a series of upward
velocities.
8. A process for producing a bitumen product via a paraffinic froth
treatment (PFT) operation, comprising: adding a paraffinic solvent
to bitumen froth to produce a solvent diluted bitumen froth;
feeding the solvent diluted bitumen froth to a settling vessel to
separate the solvent diluted bitumen froth into a diluted bitumen
overflow stream withdrawn from a lighter phase zone comprising a
solvent diluted bitumen material, and a diluted tailings underflow
stream withdrawn from a heavier phase zone and comprising a diluted
tailings material; separating the diluted tailings underflow stream
into a recovered underflow solvent stream and a solvent recovered
tailings stream; separating the diluted bitumen overflow stream
into a recovered overflow solvent stream and the bitumen product;
acquiring near infrared (NIR) spectral measurements from the
solvent diluted bitumen material using a NIR probe that remains
within the lighter phase zone to determine an upward velocity
within the lighter phase zone as a settling characteristic of the
settling vessel.
9. The process of claim 8, further comprising determining water
content or solids content of the solvent diluted bitumen material
based on the upward velocity.
10. The process of claim 8, further comprising obtaining an instant
value of at least one of a water content or a solids content in the
solvent diluted bitumen material based on the upward velocity in
the lighter phase zone.
11. The process of claim 8, further comprising determining a
settling characteristic of the settling vessel other than the
upward velocity based on the NIR spectral measurements.
12. The process of claim 11, wherein the settling characteristic
comprises at least one of an interface location, an interface
movement or an interface composition.
13. The process of claim 8, wherein determining the settling
characteristic of the settling vessel based on the NIR spectral
measurements comprises developing an NIR calibration model, and
wherein the NIR calibration model is a multivariable calibration
model developed by: processing both laboratory measured data and
associated NIR data using a chemometric method; and using at least
one of quality assurance and quality control (QA/QC) analyses, a
multiple scatter correction data processing method, a first
derivative data processing method, a vector normalization data
processing method, and a combination thereof.
14. A paraffinic froth treatment (PFT) process, comprising: adding
a paraffinic solvent to bitumen froth to produce a solvent diluted
bitumen froth; in a froth separation unit (FSU), separating the
solvent diluted bitumen froth into a diluted bitumen overflow
stream withdrawn from a lighter phase zone comprising a solvent
diluted bitumen material, and a diluted tailings underflow stream
withdrawn from a heavier phase zone and comprising a diluted
tailings material; and acquiring near infrared (NIR) spectral
measurements from the solvent diluted bitumen material irrespective
of a location of an interface between the lighter phase zone and
the heavier phase zone to determine upward velocity within the
lighter phase zone using a pre-determined direct relationship
between the upward velocity and a corresponding NIR spectral
profile; wherein the pre-determined direct relationship is obtained
by determining a set of different upward velocities for a set of
test solvent diluted bitumen materials, and acquiring respective
NIR spectral measurements on the set of test solvent diluted
bitumen materials.
15. The PFT process of claim 14, wherein the NIR spectral
measurements are acquired using a NIR probe located within the FSU
and within the lighter phase zone.
16. The PFT process of claim 14, further comprising monitoring the
solvent diluted bitumen material according to the NIR spectral
measurements, and controlling at least one operating condition of
the PFT process based on the NIR spectral measurements.
17. The process of claim 14, further comprising correlating the
upward velocity with solids and water content of the solvent
diluted bitumen material.
18. The process of claim 17, wherein the solids and water content
of the solvent diluted bitumen material enables estimating the
location of the interface between the lighter phase zone and the
heavier phase zone.
19. The process of claim 14, further comprising obtaining an
instant value of at least one of a water content or a solids
content in the solvent diluted bitumen material based on the upward
velocity in the lighter phase zone.
20. The process of claim 14, further comprising controlling the
location of the interface between the lighter phase zone and the
heavier phase zone based on the NIR spectral measurements from the
solvent diluted bitumen material to achieve a pre-determined
compositional property of the solvent diluted bitumen material.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/726,115, filed on Oct. 5, 2017, which
claims priority to CA Patent No. 2946027, filed on Oct. 19, 2016,
the disclosures of which are hereby incorporated by reference in
their entirety.
TECHNICAL FIELD
[0002] The technical field generally relates to monitoring streams,
components or operational parameters in paraffinic froth treatment
(PFT) operations using near infrared (NIR) based techniques for
bitumen or heavy oil production.
BACKGROUND
[0003] Bitumen froth can be generated by separating oil sands
slurry into a bitumen froth component and a solids-enriched
tailings component, which may be performed in a flotation unit. The
bitumen froth still includes water and mineral solids that should
be removed to meet storage and pipeline criteria. In PFT, a
paraffinic solvent is added to the bitumen froth in order to dilute
bitumen components and help remove water and mineral solids.
Paraffinic solvent acts differently compared to naphthenic
solvents, notably in that paraffinic solvents induce precipitation
of asphaltenes which form flocs composed of asphaltenes, water and
solids. After adding paraffinic solvent to the bitumen froth, the
resulting diluted froth can be supplied to a settler vessel that
produces a diluted bitumen overflow and a tailings underflow
including asphaltenes. While removal of the heavier asphaltene
components from the diluted bitumen can have benefits, PFT
operations also have number of challenges due to the ability of
paraffins to precipitate asphaltenes.
[0004] In PFT operations, bitumen froth is diluted with solvent and
separated into diluted bitumen and a solvent diluted tailings
component in a froth separation unit (FSU), which can include two
or three settlers arranged in a counter-current configuration. The
diluted bitumen can then be supplied to a solvent recovery unit
(SRU) to produce recovered solvent and solvent recovered bitumen,
while the solvent diluted tailings component can be supplied to a
tailings solvent recovery unit (TSRU) to produce recovered solvent
and solvent recovered tailings. The solvent recovered tailings can
be further processed or can be supplied to a tailings disposal site
for settling.
[0005] In the context of PFT operations, there are challenges
related to monitoring various streams, components and operational
parameters, in order to implement process control strategies. For
example, the diluted bitumen should generally include less than 0.1
wt % water and less than 0.1 wt % of fine mineral solids in the
diluted bitumen overflow from the FSU. The solvent-to-bitumen (S/B)
ratio of the diluted bitumen is also an important parameter that
affects asphaltene precipitation and settling characteristics which
eventually affects the quality of bitumen product. Variable froth
composition and separation unit upsets can increase the likelihood
of off-specification streams and can reduce the efficiency of the
bitumen extraction process.
SUMMARY
[0006] In some implementations, there is provided a process for
producing a bitumen product via a paraffinic froth treatment (PFT)
operation, including: adding a paraffinic solvent to a bitumen
froth to produce a solvent diluted bitumen froth; feeding the
solvent diluted bitumen froth into a settling vessel wherein
solvent diluted bitumen material flows upward and forms a lighter
phase zone, and mineral solids and water settle downward and form a
heavier phase zone; withdrawing a diluted bitumen overflow from the
settling vessel; withdrawing a solvent diluted underflow from the
settling vessel; processing the diluted bitumen overflow to produce
the bitumen product; locating a near infrared (NIR) probe within an
upper hydrocarbon phase stratum of a stratified PFT process stream;
acquiring NIR spectral measurements from the NIR probe; and
adjusting the PFT operation for producing the bitumen product based
on the NIR spectral measurements.
[0007] Various other processes for producing a bitumen product are
also provided and can leverage NIR measurement techniques described
herein. In one example, a process producing a bitumen product via a
paraffinic froth treatment (PFT) operation includes: adding a
paraffinic solvent to a bitumen froth to produce a solvent diluted
bitumen froth; feeding the solvent diluted bitumen froth into a
settling vessel wherein solvent diluted bitumen material flows
upward and forms a lighter phase zone, and mineral solids and water
settle downward and form a heavier phase zone; withdrawing a
diluted bitumen overflow from the lighter phase zone in the
settling vessel; withdrawing a solvent diluted underflow from the
heavier phase zone in the settling vessel; processing the diluted
bitumen overflow to produce the bitumen product; acquiring near
infrared (NIR) spectral measurements from the solvent diluted
bitumen material; determining settling characteristics of the
settling vessel based on the NIR spectral measurements, wherein the
settling characteristics comprise upward velocity within the
lighter phase zone in the settling vessel; and adjusting the PFT
operation based on the settling characteristics.
[0008] Another example of a paraffinic froth treatment (PFT)
process includes adding paraffinic solvent to bitumen froth to
produce diluted froth; in a froth separation unit (FSU), separating
the diluted froth into a diluted bitumen stream and a diluted
tailings stream; separating the diluted tailings stream into a
recovered solvent stream and a solvent recovered tailings;
separating the diluted froth into a recovered solvent stream and a
bitumen product; and controlling at least one operating condition
of the PFT process based on at least one physicochemical
characteristic that is derived from near infrared (NIR) spectral
measurements obtained from at least one PFT process stream.
[0009] Various features of PFT processes and bitumen production
methods are described further herein. Such processes can employ one
or more NIR based measurement and adjustment techniques to enhance
operational performance.
[0010] In some implementations, there is provided a process for
monitoring a stream in a paraffinic froth treatment (PFT)
operation, comprising locating a near infrared (NIR) probe within
an upper hydrocarbon phase stratum of a stratified PFT process
stream; and acquiring NIR spectral measurements from the NIR
probe.
[0011] In some implementations, the NIR probe is located within a
horizontal section of a pipe section transporting the PFT process
stream. In some implementations, the NIR probe is located at or
proximate an inner wall of the pipe section. In some
implementations, the NIR probe is located within an upper
semi-circle section of the pipe section. In some implementations,
the NIR probe is located in spaced relation away from a top 12
o'clock location of the pipe section. In some implementations, the
NIR probe is located in between a 10 o'clock and an 11 o'clock
location of the pipe section. In some implementations, the pipe
section has a diameter of at least 6 inches, at least 8 inches, or
6 to 8 inches. In some implementations, the pipe section is spaced
away downstream from flow impediments and unit operations.
[0012] In some implementations, the PFT process stream comprises an
aqueous phase that forms a lower stratum. In some implementations,
the PFT process stream comprises a vapour component that
accumulates at a top region above the upper hydrocarbon phase
stratum.
[0013] In some implementations, the NIR probe is a reflectance
probe. In some implementations, the NIR probe is a transmittance
probe.
[0014] In some implementations, the PFT process stream comprises
bitumen froth, diluted bitumen froth, diluted bitumen overflow,
solvent diluted underflow, or solvent diluted tailings. In some
implementations, the PFT process stream is a diluted bitumen
overflow stream withdrawn from a first stage settling vessel that
is part of a two-stage froth separation unit (FSU).
[0015] In some implementations, the stratified PFT process stream
flows through a PFT process pipe and the NIR probe is located
within the PFT process pipe.
[0016] In some implementations, the stratified PFT process stream
flows through a bypass line and/or a slip stream line, and the NIR
probe is located within the bypass line and/or a slip stream
line.
[0017] In some implementations, there is provided a process for
monitoring a settling vessel in a paraffinic froth treatment (PFT)
operation, comprising acquiring near infrared (NIR) spectral
measurements from a diluted bitumen material produced by the
settling vessel; and determining upward velocity characteristics of
the settling vessel based on the NIR spectral measurements.
[0018] In some implementations, there is provided a process for
monitoring a settling vessel in a paraffinic froth treatment (PFT)
operation, comprising acquiring near infrared (NIR) spectral
measurements from an overflow material produced by the settling
vessel; and determining settling characteristics of the settling
vessel based on the NIR spectral measurements.
[0019] In some implementations, the settling characteristics
comprise upward velocity within the settling vessel. In some
implementations, the overflow material comprises a diluted bitumen
stream and the settling vessel comprises a first stage froth
separation vessel. In some implementations, the NIR spectral
measurements are obtained using an NIR probe located within the
settling vessel.
[0020] In some implementations, the process also includes
determining water content or solids content of the overflow
material based on the upward velocity. In some implementations,
determining the settling characteristics comprises developing an
NIR calibration model. In some implementations, the NIR calibration
model is a multivariable calibration model developed by: processing
both laboratory measured data and associated NIR data using a
chemometric method; and using at least one of quality assurance and
quality control (QA/QC) analyses, a multiple scatter correction
data processing method, a first derivative data processing method,
a vector normalization data processing method, and a combination
thereof.
[0021] In some implementations, there is provided a process for
monitoring paraffin content in a hydrocarbon-containing stream in a
paraffinic froth treatment (PFT) system, comprising acquiring near
infrared (NIR) spectral measurements from a hydrocarbon-containing
stream, wherein a paraffinic solvent concentration in the
hydrocarbon-containing stream is below 1000 ppm; and determining
the paraffinic solvent concentration in the hydrocarbon-containing
stream based on the NIR spectral measurements.
[0022] In some implementations, the hydrocarbon-containing stream
comprises a PFT start-up hydrocarbon. In some implementations, PFT
start-up hydrocarbon is diesel. In some implementations, the PFT
start-up hydrocarbon comprises aromatic hydrocarbons. In some
implementations, the hydrocarbon-containing stream comprises a
bitumen product stream. In some implementations, determining the
paraffinic solvent concentration comprises developing an NIR
calibration model. In some implementations, the NIR calibration
model is a multivariable calibration model developed by: processing
both laboratory measured data and associated NIR data using a
chemometric method; and using at least one of quality assurance and
quality control (QA/QC) analyses, a constant offsite elimination, a
straight line subtraction, a multiple scatter correction data
processing method, a first and second derivative data processing
method, and a combination thereof.
[0023] In some implementations, there is provided a process for
monitoring paraffin content in a hydrocarbon-containing stream in a
paraffinic froth treatment (PFT) system, comprising acquiring near
infrared (NIR) spectral measurements from a hydrocarbon-containing
stream; and determining the paraffinic solvent concentration in the
hydrocarbon-containing stream based on the NIR spectral
measurements.
[0024] In some implementations, there is provided a process for
monitoring a process-aid in a paraffinic froth treatment (PFT)
operation, comprising acquiring near infrared (NIR) spectral
measurements from a PFT process stream; and determining process-aid
dosage based on the NIR spectral measurements.
[0025] In some implementations, the PFT process stream is a diluted
bitumen overflow stream. In some implementations, determining the
process-aid dosage comprises developing an NIR calibration model.
In some implementations, the NIR calibration model is a
multivariable calibration model developed by: processing both
laboratory measured data and associated NIR data using a
chemometric method; and using at least one of quality assurance and
quality control (QA/QC) analyses, a multiple scatter correction
data processing method, a first derivative data processing method,
a vector normalization, and a combination thereof. In some
implementations, determining the process-aid dosage comprises:
developing a first NIR calibration model between the NIR spectral
measurements and a physicochemical characteristic of the PFT
process stream; and developing a second calibration model between
the physicochemical characteristic and the process-aid activity. In
some implementations, the physicochemical characteristic comprises
water content, mineral solids content and/or asphaltene aggregate
size.
[0026] In some implementations, there is provided a process for
monitoring a paraffinic froth treatment (PFT) operation, comprising
acquiring near infrared (NIR) spectral measurements from a PFT
process stream; and determining paraffinic solvent content,
asphaltene content, bitumen content, water content, and or solids
content of the PFT process stream based on the NIR spectral
measurements.
[0027] In some implementations, the PFT process stream is a diluted
bitumen stream. In some implementations, determining paraffinic
solvent content, asphaltene content, bitumen content, water
content, and or solids content of the PFT process stream comprises
developing an NIR calibration model. In some implementations, the
NIR calibration model is a multivariable calibration model
developed by: processing both laboratory measured data and
associated NIR data using a chemometric method; and using at least
one of quality assurance and quality control (QA/QC) analyses, a
multiple scatter correction data processing method, a first
derivative data processing method, a vector normalization, and a
combination thereof.
[0028] In some implementations, there is provided a process for
monitoring a paraffinic froth treatment (PFT) operation, comprising
acquiring near infrared (NIR) spectral measurements from a PFT
process stream; and determining at least one physicochemical
characteristic of the PFT process stream based on the NIR spectral
measurements.
[0029] In some implementations, the physicochemical characteristic
is at least one of density, solvent-to-bitumen ratio, component
concentration, flow velocity, and asphaltene agglomerate size. In
some implementations, the component concentration comprises at
least one of bitumen content, paraffinic solvent content,
asphaltene content, mineral solids content, water content,
soluble-water content and free-water content. In some
implementations, the PFT process stream is a bitumen froth stream,
a diluted bitumen froth stream, a diluted bitumen stream, a
recovered solvent stream, a bitumen product stream, or an underflow
tailings stream. In some implementations, acquiring the NIR
spectral measurements comprises directing a light source toward the
PFT process stream; capturing radiation emitted back after
interaction with the PFT process stream; and processing the
radiations captured after interaction with the PFT process stream
to provide the NIR spectral measurements.
[0030] In some implementations, determining physicochemical
characteristic of the PFT process stream comprises developing an
NIR calibration model. In some implementations, the NIR calibration
model is a multivariable calibration model developed by: processing
both laboratory measured data and associated NIR data using a
chemometric method; and using at least one of quality assurance and
quality control (QA/QC) analyses, a constant offsite elimination,
straight line subtraction, min-max normalization, vector
normalization, multiple scatter correction, a first or second
derivative, a combination thereof. In some implementations, the NIR
calibration model is developed using the following the steps:
collecting samples of diluted bitumen from a PFT process; measuring
density, solvent content, bitumen content and asphaltenes content
of each of the collected samples of diluted bitumen; taking NIR
measurements of each of the collected samples; compiling the
measured density, paraffinic solvent content, asphaltene content,
bitumen content, water content, and/or solids content with the
corresponding NIR measurements; developing a preliminary NIR
calibration model for density, paraffinic solvent content,
asphaltene content, bitumen content, water content, and/or solids
content prediction, using a chemometrics method; identifying and
removing outlier data to increase accuracy of the preliminary model
using a QA/QC analysis; identifying and removing additional outlier
data using a statistical tool to further increase accuracy of the
preliminary model; and improving the accuracy of the preliminary
model using multiple scatter correction, first derivative, vector
normalization, and/or a combination thereof.
[0031] In some implementations, there is provided a process for
monitoring a paraffinic froth treatment (PFT) operation, comprising
acquiring near infrared (NIR) spectral measurements from a PFT
process stream; and determining at least one physicochemical
characteristic of the PFT process stream based on the NIR spectral
measurements; and determining at least one parameter of an unit
based on the NIR spectral measurements.
[0032] In some implementations, the PFT process stream is a diluted
froth, or diluted bitumen overflow stream produced by a separator.
In some implementations, the separator comprises a gravity settling
vessel. In some implementations, the physicochemical characteristic
comprises a component concentration. In some implementations, the
component concentration comprises paraffinic solvent content,
bitumen content, asphaltene content, water content, or solids
content. In some implementations, the at least one parameter of the
separator comprises a flow characteristic within the separator. In
some implementations, the flow characteristic comprises an upward
flow velocity of diluted bitumen. In some implementations, there
process also includes determining water content or solids content
of the diluted bitumen overflow based on the determined upward flow
velocity of the diluted bitumen.
[0033] In some implementations, there is provided a process for
monitoring a hydrocarbon-containing stream used in a paraffinic
froth treatment (PFT) system, comprising obtaining near infrared
(NIR) spectral measurements of the hydrocarbon-containing stream,
which comprises a residual component ; and determining a residual
component content in the hydrocarbon containing stream using an NIR
calibration model correlating the NIR spectral measurements with
the residual component content in the hydrocarbon containing
stream, wherein the residual component content is below 1 wt % and
the standard deviation of the NIR calibration model is below 0.05
wt %.
[0034] In some implementations, the standard deviation of the NIR
calibration model is below 70 ppm. In some implementations, the
residual component comprises paraffinic solvent and the
hydrocarbon-containing stream comprises a bitumen product stream, a
hydrocarbon start-up stream, an aromatic hydrocarbon stream,
toluene, a bitumen-containing hydrocarbon mixture, or diesel. In
some implementations, the paraffinic solvent is pentane.
[0035] In some implementations, there is provided a process for
monitoring asphaltenes agglomerates size in a paraffinic froth
treatment (PFT) process stream, comprising obtaining near infrared
(NIR) spectral measurements of the PFT process stream; determining
an upward flow velocity of the overflow diluted bitumen using a NIR
calibration model correlating the NIR spectral measurements to the
upward flow velocity; and determining asphaltene agglomerate sizes
based on the estimated upward flow velocity.
[0036] In some implementations, there is provided a process for
monitoring a paraffinic froth treatment (PFT) operation, comprising
obtaining near infrared (NIR) spectral measurements of a PFT
process stream comprising a paraffinic solvent; and determining
soluble-water content in paraffinic solvent based on the NIR
spectral measurements; measuring total water content in the
paraffinic solvent; and determining non-soluble water content in
the paraffinic solvent by based on the determined soluble-water
content and the total water content. In some implementations, the
process also includes determining the non-soluble water content
comprises subtracting the soluble-water content from the total
water content. In some implementations, the total water content is
measured using laboratory titration techniques.
[0037] In some implementations, there is provided a process for
monitoring a paraffinic froth treatment (PFT) operation, comprising
obtaining near infrared (NIR) spectral measurements of a PFT
process stream comprising a paraffinic solvent; and determining
soluble-water content in paraffinic solvent based on the NIR
spectral measurements.
[0038] In some implementations, there is provided a process for
monitoring zone settling behaviour in a settling unit of a
paraffinic froth treatment (PFT) operation, comprising obtaining
near infrared (NIR) spectral measurements of material within the
settling unit via an NIR probe located within a settling chamber of
the settling unit; and determining a zone settling behaviour
characteristic within the settling chamber based on the NIR
spectral measurements.
[0039] In some implementations, the zone settling behaviour
characteristic comprises a velocity. In some implementations, the
zone settling behaviour characteristic comprises an upward flow
velocity. In some implementations, the zone settling behaviour
characteristic comprises an interface location, movement and/or
composition, the interface being defined between an upper
hydrocarbon phase fraction and a lower aqueous phase fraction.
[0040] In some implementations, the settling unit comprises a first
stage gravity settler of a froth separation unit (FSU). In some
implementations, the settling unit comprises a second stage gravity
settler of a froth separation unit (FSU).
[0041] In some implementations, there is provided a process for
controlling a paraffinic froth treatment (PFT) operation,
comprising monitoring the PFT operation as defined above or herein;
and adjusting at least one PFT process parameter based on the
determined non-soluble water content.
[0042] In some implementations, there is provided a paraffinic
froth treatment (PFT) process, comprising adding paraffinic solvent
to bitumen froth to produce diluted froth; separating the diluted
froth into a diluted bitumen stream and a diluted tailings stream;
separating the diluted tailings stream into a recovered solvent
stream and a solvent recovered tailings; separating the diluted
froth into a recovered solvent stream and a bitumen product; and
controlling the PFT process based on at least one physicochemical
characteristic that is derived from near infrared (NIR) spectral
measurements obtained from at least one PFT process stream.
[0043] In some implementations, the process also includes
monitoring the PFT operation as defined above or herein. In some
implementations, the process includes adjusting at least one
operating condition of the PFT process in response to the
physicochemical characteristic determined by NIR. In some
implementations, the adjusted operating condition comprises feed
rate of diluted froth into the froth separation unit (FSU), dosage
of process-aid, flow rate of an overflow and/or underflow stream,
or solvent-to-bitumen (S/B) ratio, or a combination thereof.
BRIEF DESCRIPTION OF DRAWINGS
[0044] FIGS. 1a to 1c are schematics illustrating parts of PFT
processes.
[0045] FIG. 2 is a graph comparing transmission and reflectance
spectra for NIR spectra over a period of time
[0046] FIG. 3 is a graph showing the correlation of two sets of
densities of diluted bitumen.
[0047] FIG. 4 is a graph showing the correlation of density vs. %
of bitumen and % of asphaltenes in diluted bitumen.
[0048] FIG. 5 is a graph showing the correlation of density vs. %
of solvent in diluted bitumen.
[0049] FIG. 6 is a graph showing the updated correlation of density
vs. % of bitumen and % of asphaltenes in diluted bitumen.
[0050] FIG. 7 is a graph showing the updated correlation of density
vs. % of solvent in diluted bitumen.
[0051] FIG. 8 is a graph showing a final correlation of density vs.
% of solvent in diluted bitumen.
[0052] FIG. 9 is a graph showing a correlation of density vs. % of
bitumen and % of asphaltenes in diluted bitumen.
[0053] FIGS. 10a and 10b are two graphs showing the relationship of
% of asphaltenes vs. % of solids and water in diluted bitumen.
[0054] FIG. 11 is a graph showing the relationship of % of solids
vs. % of Karl Fisher water (KFW) in diluted froth or diluted
bitumen in PFT settling tests.
[0055] FIG. 12 is a flow chart of NIR model building work
process.
[0056] FIG. 13 is three overlapped NIR spectra over a five minute
period with--Zoomed in.
[0057] FIG. 14 is a cross validation for an Initial calibration
model for S/B ratio.
[0058] FIG. 15 is a cross validation for a calibration model for
S/B ratio with QA/QC data points removed.
[0059] FIG. 16 is spectra of outlier data from sample 33 from Table
6 showing uncharacteristically shifting due to unknown factor.
[0060] FIGS. 17(a-e) are final NIR models for S/B ratio, density,
bitumen solvent, and asphaltenes contents.
[0061] FIGS. 18a and 18b are final NIR models for water content and
solids content.
[0062] FIGS. 19 to 21 are the comparison of NIR models for S/B
ratio with MSC Preprocessing (FIG. 19), for S/B ratio with 1st
derivative preprocessing--full (FIG. 20), for S/B with 1st
derivative preprocessing--lower (FIG. 21).
[0063] FIG. 22 is a group of spectra showing effective frequency
ranges on calibrating NIR spectra.
[0064] FIG. 23 is a picture of an optimization tool listing error
values for frequency ranges and rank
[0065] FIG. 24 is a graph showing the RMSECV for each rank.
[0066] FIG. 25 is a profile of prediction points shown in OPUS
graph.
[0067] FIG. 26 is a profile of predicted S/B ratio generated by NIR
S/B ratio model for Week C.
[0068] FIG. 27 is a profile of predicted asphaltenes content
generated by NIR asphaltenes model for Week C.
[0069] FIG. 28 is a graph of NIR predicted S/B ratio and density
minus outliers during week A, B and C.
[0070] FIG. 29 is a graph of NIR predicted bitumen, solvent,
asphaltenes contents minus outliers during week A, B and C.
[0071] FIGS. 30 to 33 are comparisons of the correlation between
density and S/B ratio for NIR prediction vs. lab data, for MSC
model (FIG. 31), 1D model (FIG. 32), MSC model with adjusted
asphaltenes content (FIG. 33), 1D model with adjusted asphaltenes
content (FIG. 34).
[0072] FIG. 34 is a cross validation for final flux model.
[0073] FIG. 35 is a graph of the NIR predicted flux during Week A,
B and C.
[0074] FIG. 36 is a group of NIR spectra of diluted bitumen
collected during Week A.
[0075] FIG. 37 is a comparison of NIR spectra of diluted bitumen
collected in the lowest flux and the highest flux during Week
A.
[0076] FIGS. 38a and 38b are cross validations of developed NIR
water and solids models based on theoretical water and solids
contents
[0077] FIGS. 39 to 47 are graphs showing predicted water or solids
during Week A, B and C.
[0078] FIG. 48 is a cross validation for a NIR model of critical
size of asphaltenes/water/solids agglomerates.
[0079] FIG. 49 is a graph showing predicted critical size of
asphaltenes/water/solids agglomerates in diluted bitumen for three
weeks.
[0080] FIGS. 50 to 53 are graphs showing Refractive Index (RI) data
vs. lab data for density and S/B, during week A, B and C.
[0081] FIG. 54 is a comparison of RI data and NIR data for density
for Week B and C.
[0082] FIG. 55 is a comparison of RI data and NIR data for S/B for
Week B and C.
[0083] FIG. 56 is a graph showing density for each measurement
method (RI, NIR and lab) for Week B and C.
[0084] FIG. 57 is a graph showing S/B ratio for each measurement
method (RI, NIR and lab) for Week B and C.
[0085] FIG. 58 is a group of NIR spectra showing variation of NIR
spectra before and after the asphaltenes/water/solids agglomerates
zone passed by NIR probe.
[0086] FIG. 59 is a PCA plot generated using Chemometric method
based on NIR spectra in zone settling study in PFT.
[0087] FIG. 60 is a group of NIR spectra of diluted bitumen with or
without a process-aid.
[0088] FIG. 61 is a graph of water content in diluted bitumen vs.
the dosage of a process aid addition.
[0089] FIG. 62 is a cross validation for NIR model of predicted
soluble water vs. literature value of soluble water in pentane.
[0090] FIG. 63 is comparison of predicted soluble water in pentane
with temperature vs. literature value.
[0091] FIG. 64 is NIR reflectance spectra of diluted bitumen,
diesel and pentane.
[0092] FIG. 65 is a cross validation for NIR model of <1%
pentane in diesel with SD of 0.0726%.
[0093] FIG. 66 is a cross validation for NIR model of
pentane-diesel mixing (0-100%) with SD at 1.81%.
[0094] FIG. 67 is a comparison between NIR predicted concentration
and the concentration of the sample prepared.
[0095] FIG. 68 is NIR spectra of less than 1000 ppm pentane in
diesel.
[0096] FIG. 69 is a cross validation for NIR model for <1000 ppm
pentane in diesel.
[0097] FIG. 70 is a graph for validation of predicted pentane
concentration by NIR model vs. GC results.
[0098] FIG. 71 is NIR spectra of <1000 ppm pentane in 10%
bitumen toluene solutions.
[0099] FIG. 72 is a cross validation for NIR model for <1000 ppm
pentane in 10% bitumen in toluene solution.
[0100] FIG. 73 is a graph for validation of predicted pentane
concentration by NIR model vs. GC results.
[0101] FIG. 74 is a graph showing the impact of the concentration
of NaCl and temperature on the solubility of water in pentane.
[0102] FIG. 75 is a schematic of two sample collection method.
[0103] FIG. 76 is a graph of asphaltenes solubility in diluted
bitumen vs. temperature.
[0104] FIGS. 77(a-d) are comparisons of lab results of two samples
collected at the same time frame.
[0105] FIG. 78 is a graph showing the correlation of the density of
diluted bitumen vs. bitumen content in diluted bitumen in two
methods.
[0106] FIG. 79 is a graph showing the correlation of the density of
diluted bitumen vs. S/B ratio of diluted bitumen in two
methods.
[0107] FIG. 80 is a graph showing the relationship between S/B
ratio of diluted bitumen and asphaltenes contents in bitumen in two
methods.
[0108] FIG. 81 is a graph showing the amount of asphaltenes
adjusted vs. S/B ratio of the sample collected in the cooling coil
method.
[0109] FIG. 82 is a block diagram showing order of the sample
analysis.
[0110] FIG. 83 is a diagram showing a setup of NIR to detect
composition of the supernatant in a settling test.
[0111] FIG. 84 is a graph showing transmission NIR spectra of
supernatant phase versus time in a settling test.
[0112] FIG. 85 is another graph showing transmission NIR spectra of
supernatant phase versus time in eight settling tests.
[0113] FIG. 86 is a graph showing water content in the supernatant
versus settling time.
DETAILED DESCRIPTION
[0114] The techniques described herein relate to processes of
producing bitumen from bitumen froth in conjunction with the use of
methods of monitoring various aspects of paraffinic froth treatment
(PFT) operations. In particular, the use of near infrared (NIR)
spectrometry and chemometric analysis to continuously monitor and
enable measurements of physical and chemical properties of various
streams in PFT operations, which can be done in real time online
and can facilitate process control. In addition, NIR spectrometry
can be used to acquire NIR spectra measurements from a PFT process
stream and the NIR spectra measurements and chemometric analysis
can, in turn, be used to determine both composition characteristics
of the PFT process stream as well as operational features of a PFT
process unit which may be upstream.
[0115] "PFT process stream" means any fluid stream involved in the
PFT process. More particularly, as shown in FIG. 1a, the PFT
process stream can include bitumen froth, diluted bitumen froth,
first or second stage overflow streams in the FSU, first or second
stage underflow streams in the FSU, recovered solvent from the SRU,
TSRU or VRU, diesel-containing streams used for start-up or
cleaning the PFT vessels or lines, bitumen product, or TSRU
tailings. The PFT process stream may be a two-phase fluid
containing a hydrocarbon phase and an aqueous phase, or a
single-phase fluid in some cases. In a preferred implementation,
the PFT process stream is diluted bitumen overflow from the first
stage settling vessel of the FSU.
[0116] Chemometrics is a method used for developing NIR calibration
models for chemical systems. Chemometric methods facilitate
processing laboratory or other data along with NIR spectral
measurements to provide a calibration baseline model (also called a
preliminary model). In some implementations, chemometric methods
are used to develop multivariable calibration models using
appropriate statistical tools, such as OPUS/QUANT Spectroscopy
Software (by Bruker.TM.), for example.
[0117] Bitumen content, solvent content, solvent-to-bitumen ratio
(S/B), density, and asphaltenes content can be determined using
strong NIR calibration models built using chemometric methods. In
addition, NIR spectral measurements have been used to estimate the
flux or upward velocity in the FSU settling vessel, certain water
contents, solids contents, certain residual component contents in
hydrocarbon-containing streams, and asphaltene agglomerates size
via the development of NIR calibration models showing suitable
correlations. In PFT operations, S/B ratio is an important
parameter to determine the amount of asphaltene precipitation and
the product quality, and is therefore usually used for both
operational control and product quality control. Therefore,
reliable NIR monitoring of S/B and asphaltenes content in diluted
bitumen have been developed for facilitating enhanced PFT process
control.
[0118] Monitoring the quality of the diluted bitumen is important
in PFT operations. In this regard, one benefit of NIR monitoring
online/inline is that it can significantly reduce turnaround time
when the product quality becomes off-specification, through
adjusting operational conditions to control product quality.
Reliable online/inline measurement can replace manual sampling,
reduce human errors related to laboratory analysis, and minimize
safety risks associated with sample collection.
[0119] In pilot tests, conventional NIR monitoring methods have not
been able to detect water and solids content at the low levels that
can occur in PFT operations. While conventional NIR models have not
been reliable regarding the determination of water content and
solids content because of poor lab analysis and sample handling
resulting in weak correlations, techniques described herein provide
water and solids models that allow continuous and online monitoring
of the diluted bitumen quality and other PFT process streams.
Extended study of bench scale settling tests and Stokes' Law have
been leveraged to provide reasonable prediction for water content
and solids content in diluted bitumen. NIR based techniques can
thus facilitate monitoring of the variation of water content and
solids content in diluted bitumen. As such, NIR based techniques
are a powerful tool for implementation of online/inline product
quality control and for online/inline operational control. Various
other relevant characteristics of PFT process streams and PFT unit
can be monitored via NIR based techniques, as will be explained in
detail below.
NIR Spectrometry and Process Control Implementations
[0120] Referring to FIG. 1a, various NIR based measurement and
monitoring techniques can be implemented in connection with PFT
operations. NIR probes can be implemented at various points in the
PFT process. For example, a first NIR probe may be provided to
determine composition of froth before dilution with the paraffinic
solvent. A second NIR probe may be provided to determine
composition of diluted froth as the feed of a froth separation unit
(FSU). A third NIR probe may be provided within an FSU vessel
(e.g., first stage separation vessel) to determine composition of
diluted froth and/or diluted bitumen, and to monitor the interface
between hydrocarbon phase and aqueous phase in the FSU. Fourth and
fifth NIR probes may be provided to determine composition and S/B
ratio of a diluted bitumen overflow stream produced by the FSU and
supplied to a solvent recovery unit (SRU). Additional NIR probes
may also be provided to measure physical and chemical properties of
other streams such as FSU tailings, tailings solvent recovery unit
(TSRU) tailings, TSRU recovered solvent, bitumen product, SRU
recovered solvent, or vapor recovery unit (VRU) solvent.
[0121] In some implementations, the NIR probes are used to obtain
NIR spectral data that can be used to monitor PFT process stream
compositions as well as operating parameters of PFT units, examples
of which will be further described below. The NIR probes and
associated analysers and controllers can be automated to provide
continuous data acquisition and control, or can be manual or
semi-manual to provide more periodic data acquisition and control.
The NIR probes can be installed to provide NIR online or at-line
measurements. The NIR probes can be used online, where the probes
are physically integrated on pipes located upstream or downstream
any unit of the PFT operation or with respect to slip streams. The
NIR probes can also be integrated within one or more vessels.
[0122] Transmission-type NIR probes (transmission probes) and/or
reflectance-type NIR probes (reflectance probes) can be used. It
was found that reflectance-type NIR probes provided sharp, clear,
stable spectra; while transmission probes were sensitive but
provided noisy data, as illustrated in FIG. 2.
[0123] It should be noted that the two different types of NIR
probes may be used for different applications within PFT, e.g., for
different concentration ranges. In some implementations, a
transmission probe can be selected to measure concentrations lower
than 1000 ppm. A reflectance probe, which is more robust and easier
to maintain but less sensitive, can be selected to measure
concentrations above 1000 ppm. In some implementations, one or both
types of probes may be present in a PFT process. For example, a
reflectance probe may be present to detect a high concentration
corresponding to a safety or upset limit, while a transmission
probe may be present to detect lower operational concentrations
that may be expected during normal operation (e.g., for
fine-tuning). For instance, pentane content in start-up hydrocarbon
fluids such as diesel, can be too low to be accurately measured
with reflectance NIR, and a transmission probe would be preferred.
Transmission and reflectance probes can be located at different
points in the PFT process to monitor different streams, or both
transmission and reflectance probes can be located at the same
point and optionally integrated within the same probe structure for
online implementation.
[0124] In some implementations, the PFT process includes multiple
NIR probes at different locations. The NIR based measurements can
facilitate online monitoring of relevant process characteristics
and optimizing the PFT process. Online NIR measurements can
facilitate rapid data acquisition of process variables that are
relevant to the control of PFT process stream quality, and thus can
avoid delays related to laboratory-based sampling and measurement
techniques.
[0125] The term "NIR measurements" as used herein, encompasses
spectral measurements such as NIR spectra. Depending on the type of
probe used, i.e. reflectance probe or transmission probe, NIR
spectra may be reflectance spectra or transmission spectra. In some
implementations, the NIR spectral measurements include at least one
NIR spectrum. It may include a plurality of NIR spectra; in such
scenarios, the NIR measurements may comprise an average NIR
spectrum derived from the plurality of NIR spectra.
[0126] In some scenarios, NIR based monitoring techniques can be
used to determine composition of various streams in the PFT
process. Based on the NIR derived compositions, it is possible to
control at least one operating condition of the PFT process, e.g.,
to improve or control quality of PFT process streams. For example,
the operating condition can include feed flow rate,
solvent-to-bitumen ratio, process-aid content, paraffinic solvent
composition, outlet flow rates, for any unit of the PFT process.
The operating condition can be adjusted such that the NIR derived
composition does not reach an upset limit.
[0127] In some implementations, the PFT product is diluted bitumen
from a FSU. However, techniques described herein in relation to the
monitoring and control of FSU and diluted bitumen can be adapted to
other PFT units and streams.
NIR Probe Orientation and Location
[0128] In some implementations, NIR spectral measurements of a PFT
process stream can be acquired by positioning the NIR probe within
a pipe section through which a two-phase PFT process stream flows.
In particular, the pipe section is selected and the NIR probe is
positioned within the pipe section to be in contact with a
stratified hydrocarbon phase of the PFT process stream, thereby not
being in contact with or acquiring spectral data from the aqueous
phase. The radiation source is directed at the hydrocarbon phase
and the detector receives the NIR radiation from the hydrocarbon
phase.
[0129] PFT process streams include hydrocarbon, mineral and aqueous
components, which may tend to stratify inline under certain
circumstances. Within the overall PFT system, there can be various
equipment, instrumentation and piping configurations that may
promote stratification or mixing of the two phases at different
points in the process. Valves, pipe bends, mixers, and the like
tend to cause the two phases to mix together, while straight
horizontal pipe sections can promote stratification of the
hydrocarbon phase and the aqueous phase to respectively form upper
and lower strata within the pipe section.
[0130] In some implementations, the NIR probe is installed online
in a pipe section at sufficient distance after a flow impediment
(e.g., valve, vessel or pipe bend), where the two phases
(hydrocarbon and aqueous) are stratified. The NIR probe location
and orientation is provided to ensure that it is analyzing the
hydrocarbon phase. In some implementations, the NIR probe can be
oriented toward the hydrocarbon phase to minimize or avoid exposure
to the water phase. The radiation source emitted by the probe is
directed toward the hydrocarbon phase of the PFT process
stream.
[0131] In some implementations, the NIR probe is installed in
straight horizontal pipe section where the PFT process stream is
stratified. In some implementations, the NIR probe is installed in
a horizontal pipe section spaced away from elbows, valves or
vertical sections where the flow regime would cause mixing and
destratification of the phases.
[0132] In some implementations, the NIR probe locations can be
based on CFD modelling regarding the separation of immiscible
systems, mathematical models, and/or empirical testing. CFD models,
for example, can help understand how immiscible systems flow in
pipes. For instance, it has been found that for large size lines
and high velocities in the line, the flow tends to be stratified in
the horizontal direction. According to tests, it was found that
separate layers formed at 6 to 8 pipe diameters downstream from a
turbulence point in the pipeline. This behavior allows placing the
probe in the upper region of the pipe section to ensure that the
probe stays within the hydrocarbon phase which is lighter than the
aqueous phase. In some implementations, the NIR probes that are
placed in-line can be located at least 6, 7, 8, 9 or 10 pipe
diameters downstream of a turbulence point.
[0133] In terms of the NIR probe location within the cross-section
of a pipeline, it can be useful to consider the pipe's circular
cross-section which has an upper region and a lower region
separated by a horizontal chord. The upper and lower regions can be
defined depending on the composition of the PFT process stream and
the degree of stratification, for example. Locations around the
pipe's cross-section will be described using a clock position
analogy below.
[0134] In some implementations, the 12 o'clock position is avoided
since non-condensable vapors that may be present can be at the top
of the pipe and could thus interfere with the NIR probe. In a
preferred implementation, the NIR probe is installed close to the
inner pipe wall between the 10 o'clock and the 11 o'clock position
(or the 1 o'clock and 2 o'clock position).
[0135] The position of the NIR probe can depend on the volumetric
proportions between the immiscible phases within the pipe section.
In some implementations, the PFT process stream is a diluted
bitumen overflow stream, which is substantially only hydrocarbon
phase (generally at least 98%) and thus the aqueous phase is minor.
In such implementations, the NIR probe may be placed in a position
chosen over a larger surface of the pipe, e.g., between the 7
o'clock position and the 5 o'clock position avoiding region around
the 6 o'clock position as well as the 12 o'clock position as
mentioned above. Nevertheless, since breakthroughs of the aqueous
phase into the hydrocarbon phase due to high flux may occur, the
NIR probe can be generally installed at the 9 o'clock position or
above.
[0136] In some implementations, the NIR probe can be installed on a
sample bypass loop or slip stream line. The NIR probe can thus be
isolated from the operational unit and pipeline, which can
facilitate removal, maintenance and/or trouble-shooting of the
probe, if required, while the PFT process unit is online. For
example, for removal or maintenance, the bypass or slip stream line
can be shut off and the PFT operation can continue uninterrupted.
The bypass or slip stream line can also be configured so that the
NIR probe is installed at a desired cross-sectional location (e.g.,
around 11 o'clock) and a desired longitudinal location (e.g., at
least 6 pipe diameters downstream from a turbulence point) to
acquire the measurements of interest. Providing NIR probes
associated with bypass or slip stream lines can also facilitate
adjusting sample conditions, controlling bitumen/diesel composition
via diesel addition, and cleaning (e.g., flushing) of the NIR probe
for instance by flushing the sample line instead of the process
line to prevent plugging and/or fouling of the probes.
[0137] The NIR measurements that are obtained are used to construct
NIR correlation models, determine various physicochemical
characteristics of PFT process streams, and ensure quality control
or operational control of the PFT process.
NIR Monitoring of PFT Process Streams and Units
[0138] Various PFT streams and units can be monitored using NIR
methods. In general, NIR spectral measurements are obtained and
used to determine one or more physical or chemical characteristics
of the PFT process stream and/or an upstream PFT unit. The
characteristics can be determined using a NIR calibration models
having a correlation allowing for accurate estimation of the
characteristics, and the measurements can be used for process
control strategies to maintain performance and efficiency.
[0139] Generally, PFT processes produce high quality, partially
de-asphalted bitumen products, with low solids and water contents.
This can eliminate the need for upgrading the bitumen product
before selling to a high-conversion refinery, for example. Solvent
diluted bitumen produced in a PFT process needs to meet quality
specifications before being commercialized. Generally, final
bitumen product should contain less than 0.5 wt % of solids and
water, have a viscosity below 350 cP and a density below 940
kg/m.sup.3. These specifications may be enabled and controlled by
monitoring various parameters and physicochemical characteristics
in the PFT process. For example, the presence of more than 10 wt %
of asphaltenes in diluted bitumen increases the diluted bitumen
viscosity and hinders its flow within the pipe. Low viscosity
allows the diluted bitumen to be pumped even at low temperature.
Other characteristics, such as S/B ratio, are also of importance
for certain PFT process streams.
[0140] As explained above, the PFT process includes various
separation vessels in the FSU, SRU, TSRU and other unit operations
of the process. Separation vessels have certain operating
parameters that can be monitored and controlled to maintain
performance. For example, in settling vessels the upward velocity
of the overflow stream is a parameter that can be monitored to
assess performance and detect upsets in the settling process.
[0141] The physicochemical characteristic of the PFT process stream
can be a physical property, such as density, viscosity, or
asphaltene agglomerate size or size distribution. The
physicochemical characteristic can be a dynamic characteristic,
such as a flow velocity or a settling velocity within a unit of the
PFT process, e.g., an upward velocity of the diluted bitumen
overflow for a settling vessel. The physicochemical characteristic
can be a chemical composition, such as bitumen content, paraffinic
solvent content, asphaltene content, mineral solids content, water
content (soluble-water content, or free-water content). It has been
found that certain water chemistry characteristics (e.g., chloride
content) may also be determined using NIR spectral
measurements.
[0142] Obtaining NIR spectral measurement can include the use of an
NIR probe as described further above. In some implementations, at
least one NIR probe is installed online, positioned in an upper
region of a horizontal pipe section and within a hydrocarbon
stratum; and a light source (e.g., laser beam) is emitted by the
NIR probe into the PFT process stream. The probe may be a
reflectance probe or a transmission probe, and can be selected
depending on the nature of the PFT process stream and the
characteristic to be determined.
[0143] The light emitted by the NIR probe interacts with the PFT
process stream and the resulting radiation is captured by an NIR
detector. The radiation received after interaction with the PFT
process stream is captured and can be analysed by an NIR analyser,
which provides the NIR spectral measurements. Any NIR analyser
fitted with a fiber optic probe can be used to analyse the detected
IR radiation and provide the NIR spectral measurements. For
example, a Matrix-F FT-NIR spectrometer (Bruker.RTM.) with
transmission and reflectance probes may be used to take NIR
spectral measurements.
[0144] In some implementations, the NIR spectral measurements are
continuously obtained during operation of the PFT process, and the
physicochemical characteristics of interest are continuously
determined. Once the NIR spectral measurements are obtained, they
can be used to monitor the PFT process characteristics based on NIR
calibration models. More regarding the calibration models will be
discussed further below.
[0145] The NIR calibration models may be built using chemometric
methods, laboratory analyses of collected or prepared samples, and
corresponding NIR reflectance or transmission spectral
measurements. In some implementations, the NIR calibration models
are multivariable calibration models, and may be prepared using
density QC/QA analysis. In addition, methods that include
statistical tools, linear offset subtraction, straight line
subtraction, vector normalization, min-max normalization, multiple
scatter correction (MSC), first derivative and second derivative
data processing methods, and/or a combination of data processing
methods may be used, to emphasize chemical information derived from
the NIR measurements and improve precision and accuracy of the
determined characteristics.
[0146] In some implementations, a series of NIR calibration models
using chemometric methods and based on reflectance spectra may be
developed to determine the concentration of bitumen, pentane, and
asphaltenes in diluted bitumen. Chemometric methods may also been
used to develop the NIR models for S/B ratio and density in diluted
bitumen based on reflectance spectra. It has also been found that
NIR may also be used to determine the upward velocity of the
diluted bitumen overflow, and to determine the water content and
mineral solids content in diluted bitumen. The multi-functionality
of an NIR probe and associated calibration models can facilitate
monitoring of physical and chemical properties of various PFT
streams as well as relevant parameters of PFT processing units.
[0147] NIR Based Determination of Bitumen, Solvent and Asphaltene
Contents
[0148] In some implementations, NIR techniques are used to monitor
bitumen, solvent and/or asphaltene contents in a PFT process
stream. In PFT operations, the addition of a paraffinic solvent
(e.g., C.sub.5 alkanes such as n-pentane and iso-pentane) to the
bitumen froth induces the precipitation of asphaltene flocs or
aggregates. The composition and behavior of the resulting fluids
can be relatively complex and challenging to handle. In addition,
there can be certain target concentration levels of such
hydrocarbon components in certain PFT process streams. For example,
the target asphaltene content in the diluted bitumen overflow
stream can be less than 10 wt %, in order to provide certain
quality requirements for downstream processing and handling. In
addition, S/B ratio is an important parameter that influences the
quantity of asphaltene precipitation. In the FSU settling vessels,
the settling rate of the asphaltene agglomerates is a function of
the solvent composition, the process temperature and the S/B
ratio.
[0149] Bitumen, solvent and asphaltenes (precipitated and
non-precipitated) can be present in various PFT process streams,
including diluted froth, diluted bitumen overflow, bitumen product,
as well as underflow and tailings streams.
[0150] In some implementations, the NIR model is a multivariable
calibration model. The NIR spectra include overtones and
combination bands of the fundamental molecular absorptions found in
the mid infrared region. NIR spectra include generally overlapping
vibrational bands that may appear non-specific and poorly resolved.
Therefore, qualitative and quantitative NIR spectroscopic methods
advantageously include the application of multivariate calibration
algorithms and statistical methods to model NIR spectral response
to chemical or physical properties of the samples used for
calibration. In multivariate analysis, the entire spectrum is
analyzed and the model distinguishes each component present based
on the series of peaks, slopes, and shapes within the spectrum,
rather than by analysis at a particular wavelength or narrow range
for each component.
[0151] In terms of developing calibration models for such
hydrocarbon components, the NIR calibration model can correlate the
NIR spectral measurements with the concentration of the relevant
hydrocarbon component measured. The NIR calibration model can be
developed by correlating NIR measurements with laboratory analyses.
As illustrated in FIG. 12, the NIR model development can include
certain steps, e.g., organizing NIR spectra; calibrating a baseline
model; removing outliers identified in quality assurance/quality
control (QA/QC) analyses; removing outliers identified by the NIR
model and recalibrating; recording and analyzing outliers; and
further improvement to the model. Various model development,
refinement and validation techniques can be used.
[0152] In some implementations, NIR models can be developed by
compiling laboratory analyses for the measurements of density, S/B
ratio, and each individual concentration (bitumen, solvent,
asphaltenes, etc.) in the PFT process stream (e.g., diluted
bitumen) with NIR spectral measurements, using a chemometric
method.
[0153] It was found that the incorporation of QA/QC laboratory data
facilitated development of a reliable model. QA/QC analysis allows
identifying and removing outlier data that may decrease the
accuracy of the correlation model. The term "outlier data" refers
to any observations that are distant from other observations in a
random sample from a population, and may indicate measurement
variability and/or experimental errors. In some implementations, a
density-driven QA/QC analysis can be used to improve the NIR
correlation models for S/B or asphaltene content.
[0154] It has been found that reliable NIR models can be developed
for measuring density of the diluted bitumen. Because a strong
correlation exists between the density and bitumen, solvent and
asphaltene contents in diluted bitumen, density may be used to
verify the accuracy and reliability of the generated values and it
is thus facilitated to obtain reliable NIR calibration models for
bitumen, solvent and asphaltenes contents, as well as for S/B. For
example, FIGS. 8 and 9 show the strong correlation between density
and solvent, bitumen and asphaltenes contents after removal of
outlier data. In some implementations, the NIR model is further
improved by identifying and removing additional outlier data using
statistical tools.
[0155] Multivariable correlation models facilitate developing
accurate estimations of bitumen, solvent and asphaltenes content as
a function of NIR measurements. Correlation models may be improved
based on different data processing methods. In some
implementations, Multiple Scatter Correction (MSC) method, First
Derivative (1D) method, vector normalization method, and/or a
combination of these methods (as mentioned above) may be used.
[0156] The MSC method is suitable to the NIR spectra generated by
light scattering of the colloidal particles in the medium.
Asphaltenes-water-solids agglomerates exist in diluted froth or
diluted bitumen, and they will affect the apparent NIR absorption.
The 1D method will emphasize the chemical information of diluted
bitumen such as bitumen, solvent and asphaltenes in NIR spectra. In
the meantime, this method could eliminate the temperature impact on
the spectra. However, it could eliminate the NIR absorption
generated by light scattering from the particles in the medium.
[0157] NIR calibration models having strong correlations have been
developed to determine density, solvent content, bitumen content,
S/B ratio, and asphaltenes content in diluted bitumen. An example
of modeling process is described in more detail in the
Experimentation section further below.
[0158] In some implementations, and as shown in FIGS. 28 and 29,
using NIR correlation models can facilitate predicting both the
density and the composition of the diluted bitumen (e.g., solvent,
bitumen and asphaltene contents). The density and the different
component concentrations may be predicted continuously and online,
thereby allowing real time detection of the density and composition
of the diluted bitumen in order to facilitate PFT process
control.
[0159] In terms of PFT process control, there may be a controller
or associated equipment that receives the NIR-derived process data
(e.g., density or hydrocarbon concentration) and adjusts at least
one upstream or downstream process parameter. The control can thus
be feedback or feedforward. In some implementations, the
NIR-derived process data is obtained for diluted bitumen overflow
stream, and the adjustment of the PFT process includes adjusting
operation of the FSU (e.g., S/B ratio, flow rates of the various
inlets and outlets of the first or second stage settling vessels,
etc.). Adjusting operation of the SRU can also be performed in some
cases, for example by adjusting process parameters to deal with the
given composition of the diluted bitumen stream.
[0160] Controlling the PFT process may be performed to maintain or
increase the quality of the diluted bitumen. As the quality of the
diluted bitumen is related to S/B ratio and/or asphaltene content
and upward flux, once these contents and parameters are determined
using NIR measurements and NIR correlation models, operational
conditions may be adjusted to keep the contents within
predetermined quality specifications. For example, if elevated
asphaltene content is detected, the S/B ratio of the diluted froth
and/or of the underflow of the first stage settling vessel, can be
increased in order to increase asphaltene precipitation and removal
from the resulting diluted bitumen. In turn, S/B ratios can be
increased by introducing more pure solvent into the FSU (e.g., into
the bitumen froth or into the first stage underflow) and/or by
increasing the flow rate of the solvent-enriched second stage
settler overflow that is recycled back into the bitumen froth.
Other operating parameters can be adjusted in order to reduce
asphaltene content in the diluted bitumen.
[0161] In some implementations, controlling the PFT process
includes adjusting at least one of the following operating
parameters: the flow rate of the bitumen froth feed supplied to the
FSU, the S/B ratio, the solvent composition, flow rates of the
outlet streams of the FSU (e.g., underflows and overflows), and
process-aid content in the diluted bitumen. Depending on the PFT
process stream being monitored, the process control actions may
vary. For example, when the TSRU tailings are monitored, the
process control actions may include adjusting feed or outlet flow
rates of the two TSRU separation vessels, steam injection rate,
recirculation rate of a portion of the underflow of one or both
separation vessels, etc. For example, if TSRU tailings have a
detected solvent content above target values, recirculation can be
increased to increase residence time in the vessels which can
promote liberation of solvent for recovery as an overhead stream
and thereby reduce solvent content in the TSRU tailings. In an
example of feedforward control, the SRU could be adjusted to
respond to elevated solvent and/or asphaltene contents in order to
reduce potential asphaltene precipitation and fouling in the SRU
vessels. In addition, the PFT system can be outfitted with various
optional recycle lines that periodically enable part or all of a
process stream to be recycled when the composition does not accord
with specifications; thus, such recycle lines can be activated in
response to NIR based composition data. Furthermore, the quantity
of process-aids (e.g., dispersants, demulsifiers, defoamers, and
other surfactants, etc.) can be adjusted based on the NIR data in
order to modify the behavior of certain components in the PFT
separation units and thereby control the composition of the
resulting PFT process stream. For example, monitoring water and
solids in diluted bitumen can allow optimizing the dosage of
demulsifier and asphaltene dispersant, or limiting foam formation
in TSRU by controlling the usage of defoamer. [0162] NIR Based
Determination of PFT Separator Parameters
[0163] While determining component concentrations using NIR
techniques can be useful for process control, there are several
other parameters that are relevant to efficient PFT process
performance. For example, in some implementations, NIR spectral
measurements can be used to determine parameters of PFT units, such
as separators. In the case of gravity settlers, for instance, which
are typically used as the settling vessels in the FSU, it has been
found that settling flow characteristics can be reliably and
accurately correlated with NIR spectral measurements.
[0164] In some implementations, NIR spectral measurements are used
to determine the upward velocity of the diluted bitumen overflow in
the first stage settling vessel of the FSU. In addition, the upward
velocity can be correlated with solids and water content in the
diluted bitumen which can further facilitate process control,
particularly of the FSU.
[0165] As briefly explained above, with addition of the paraffinic
solvent asphaltenes present in the bitumen froth are precipitated
in the form of aggregates, and the water and fine mineral solids
are also bound to the asphaltene agglomerates. Thus, the
asphaltene-water-solids aggregates are formed and settle downward
in the settling vessels of the FSU for removal as underflow
streams. As a result, a diluted bitumen stream with low solids and
water contents is produced. In some scenarios, PFT processes can
produce diluted bitumen containing less than 0.1 wt % of solids or
water. While this low water and solids content is advantageous for
the quality and value of the diluted bitumen stream, it presents
some challenges in terms of monitoring such low concentrations in
the diluted bitumen stream. In addition, solids content and water
content in diluted bitumen are relevant parameters as they are
related to the amount of asphaltenes rejected (i.e., if more
asphaltenes are rejected, less solids and water are typically left
in diluted bitumen).
[0166] FIGS. 10 to 11 illustrate certain relations between solids
and water content and asphaltene content in diluted bitumen (also
referred to as "dilbit"). FIG. 10 illustrates the relation between
asphaltenes content and solids content and water content in diluted
bitumen from laboratory results. FIG. 11 illustrates a correlation
between Karl Fisher Water (KFW) and filterable solids settling
tests. However, because both water content and solids content are
very low in diluted bitumen, measurements and resulting
correlations can have reduced accuracy and reliability.
[0167] It has been found that there is a strong correlation between
NIR measurements and the upward velocity (also referred to herein
as the "flux") of the diluted bitumen overflow. In addition, since
the upward velocity and the asphaltenes settling velocity, are
related to the water content and the solids content, this aspect of
the NIR techniques may be used to provide information on water and
solids contents in diluted bitumen. Thus, the quality of a diluted
bitumen overflow stream can be monitored by obtaining NIR spectral
data and determining upward velocity based on the NIR data. The
quality can also be monitored by further determining a
compositional feature of the diluted bitumen overflow stream based
on the determined upward velocity, particularly water and mineral
solids contents. In turn, this stream quality information can be
used for PFT process control. In some implementations, NIR
calibration models based on NIR spectra can be developed based on
Stokes' Law and the estimated flux in the settling vessel, and
these models can then be used to estimate water and solids contents
in the diluted bitumen.
[0168] As briefly described above, in PFT operations, gravity
settlers are used in the FSU to separate water, mineral solids and
precipitated asphaltenes from diluted bitumen by gravity-assisted
density difference. Often, two-stage or three-stage settler
configurations are used where a downstream settler receives the
underflow from the upstream settler and downstream settler
overflows are recycled upstream. The first stage settler produces
an overflow stream that is the diluted bitumen, while the last
downstream settler produces an underflow stream that is the solvent
diluted tailings supplied to the TSRU. FIG. 1c illustrates an
example of a two-stage counter-current configuration. In the FSU
settlers, the precipitated asphaltenes entrap micron-sized water
droplets and fine mineral solids into asphaltenes agglomerates.
[0169] Zone settling behavior can be observed in the FSU settlers.
At steady-state conditions, the interface between the overflow and
the feed to the settler remains at a constant level. This means
that the overflow velocity is less than the settling velocity of
the asphaltenes agglomerates. FIG. 1c shows an FSU operation and
the concept of the upward velocity of the overflow diluted bitumen
in the first stage settler referred to as "FSU 1". In operation,
upward velocity rates correspond to settling rates. With an
increase in the feed flow rate into the FSU, the velocity of the
overflow product also increases. At higher velocity rates, more
water droplets and mineral solids may be carried into the overflow,
which results in the reduction of overflow and product quality.
[0170] Therefore, the upward velocity of the overflow is a relevant
parameter for FSU operational control. Conventionally, the upward
velocity is calculated based on the ratio of the overflow
volumetric flow rate to the overflow cross-sectional area available
for the separation in the given gravity settler. During operation,
however, the cross-sectional area of the FSU vessels could be
gradually reduced due to fouling in the gravity settler walls, for
example. The real upward velocity could deviate from the calculated
upward velocity, and delayed reaction in adjusting the upward
velocity may sacrifice the diluted bitumen quality.
[0171] As will be described in further detail in the
Experimentation section below, it has been found that NIR
reflectance spectra strongly correspond with the upward velocity of
the overflow diluted bitumen, which facilitates determining the
upward velocity of the overflow diluted bitumen using NIR
correlation models.
[0172] In terms of developing the NIR calibration model for upward
velocity, an NIR probe was installed in the FSU in the horizontal
position within the lighter phase zone (i.e., upper diluted bitumen
zone). With settling of the asphaltenes agglomerates, the
supernatant phase (or overflow) becomes cleaner and cleaner as is
flow upward in the settling vessel, with solids and water being
entrapped with asphatenes to form agglomerates that flow downward
toward the underflow. As shown in FIG. 58, the absorbance, slope
and shape of NIR spectra are different depending on the settling
advancement.
[0173] In some implementations, an NIR probe can be installed in
the FSU to determine the level of the interface between the diluted
bitumen and the aqueous phase with asphaltene agglomerates in the
FSU based on the composition of diluted bitumen and diluted froth,
as shown in FIG. 1a (NIR probe 3). Online monitoring of the level
of the interface can prevent to over-feeding the first stage FSU
which could push the interface too high to upset the FSU operation
which would eventually deteriorate the quality of diluted bitumen.
A similar method can be applied for the second stage FSU settler
even though its overflow has a much higher solvent content.
[0174] In some implementations, NIR calibration models are
developed based on the NIR spectral measurements' change with the
settling behavior. NIR spectral measurements and laboratory
composition analyses of collected samples can be carried out for a
settling process, and then the composition measured using
laboratory analysis and their corresponding NIR spectral
measurements can be compiled and processed using chemometric
methods.
[0175] In some implementations, a principal component analysis
(PCA) is then carried out to generate PCA spectral plots based on
NIR spectra and models, such as illustrated in FIG. 59. The PCA
spectral plot includes a plurality of nodes, each node
corresponding to a stage of the settling advancement and its
associated water and solids contents in the diluted bitumen
overflow. It has been found that the nodes define a pattern where
location of the node depends on the water and solids contents. For
example, FIG. 59 shows the PCA spectral plot generated according to
the NIR spectra of FIG. 58. The conversion of the NIR measurements
of FIG. 58 in the PCA plot of FIG. 59 is based on a chemometric
method. According to water and solids content in the supernatant
phase, the score loading and location of the nodes in the PCA plot
shows a clear pattern which correspond to the zone movement in the
FSU settler. Referring to FIG. 59, the curved line with an arrow
shows the variation of water and solids contents in the supernatant
phase with the zone settling. According to this analysis, it is
possible to find the right operational window and give the
threshold zone when the product quality tends to get worse. For
example, when the location is at the right side of the vertical
line in FIG. 59, water and solids contents in diluted bitumen are
low, and the diluted bitumen is good quality. It is also possible
to continuously monitor and identify trends to take early remedial
action, e.g., if the trend on the graph of FIG. 59 is tending
toward the left (i.e., reducing in quality) then corrective action
can be taken to stay within the "clear product" node.
[0176] As shown in FIG. 34, which presents the upward velocity
estimated by an NIR model as a function of true upward velocity, it
was found that NIR models can help determine flux rates of the FSU
overflow with appropriate accuracy for process control. The
mechanism for this measurement may be attributed to the light
scattering of the physical size of asphaltenes aggregations or
asphaltenes/water/solids agglomerates in the medium. This finding
also extends the application of NIR techniques in the context of
PFT operations. Monitoring the flux rate facilitates detection of
upset conditions in the FSU, so that corrective action can be taken
before the diluted bitumen quality is significantly
compromised.
[0177] In some implementations, compositional information of the
diluted bitumen, such as water content and solids content, can be
determined based on the previously determined settler
characteristics, such as upward velocity. It has been found that
the upward velocity of the FSU settler overflow has a generally
linear relation to water and solids contents. Once the upward
velocity is estimated, one can derive the water and/or solids
contents of the diluted bitumen overflow based on the estimated
upward velocity.
[0178] By way of further explanation, it is noted that typical
bitumen froth is approximately 60 wt % of bitumen, 30 wt % of
water, and 10 wt % of solids although such concentrations can vary
depending on various factors. Most water in the froth is "free
water" and relatively easy to precipitate out. Less than 10% of the
water is in emulsified water droplets. The mineral solids are
dominated by clays, carbonates and heavy minerals, having particle
sizes less than 10 microns. In PFT, when the froth is diluted with
a paraffinic solvent, a portion of the asphaltenes is precipitated
out from the hydrocarbon phase as agglomerates along with water
droplets and fine solids. The size of these agglomerates directly
affects water and solids removal from the diluted bitumen and the
quality of the diluted bitumen.
[0179] In usual operational conditions, the Reynolds (Re) number is
in the order of 0.9. When Re is below 1.0, the system is well
within the range of applicability of the so-called "creeping flow"
regime, in which inertial effects can be neglected and Stokes' Law
is suitable for describing the flow. According to Stokes' Law, the
critical size (d.sub.c) of the agglomerates released from diluted
bitumen can be calculated based on the density (.rho..sub.f) and
viscosity (.mu..sub.f) of diluted bitumen, and the density
(.rho..sub.c) of agglomerates. For example, the calculated critical
size of agglomerates increases with increasing flux, as shown in
Table 9 of Experimentation 3 further below. Stokes' Law is as
follows:
u c = g .function. ( .rho. c - .rho. f ) .times. d c 2 18 .times.
.mu. f ##EQU00001##
[0180] Because water content and solids content in diluted bitumen
linearly corresponds to the flux in the FSU settler, instant water
and solids contents in diluted bitumen may be obtained in
accordance with the flux determined at that time. In some
implementations, NIR measurements are taken continuously using
online NIR probes and the upward velocity is continuously
determined. Therefore, it is possible to monitor the water and
solids contents, which may be quite low (e.g., below 0.5 wt % or
below 0.1 wt %), in real time and take immediate corrective actions
in response to upset conditions in the PFT process.
[0181] In terms of process control, various control strategies can
be implemented based on the upward velocity information and/or the
water and solids content information obtained through NIR
techniques. Many of the process control examples mentioned further
above could be implemented. In addition, when upward velocity
information indicates a trend toward lower quality overflow, the
corrective action can include reducing flow rate of the diluted
froth feed into the settler vessel, although other parameters can
be adjusted. In some scenarios, the FSU operating parameters can be
adjusted with a view to maximizing overflow rates while keeping the
overflow quality within pre-determined specifications. In the case
where the upward velocity is above a target value, for which water
and solids contents in the diluted bitumen may be too high (e.g.,
above 0.5 wt %), the diluted froth feed rate can be decreased, the
S/B ratio can be increased and/or the amount of process-aid can be
adjusted. [0182] NIR Based Determination of Residual Components
[0183] NIR based techniques can be used to determine the
concentration of various other residual components in PFT process
streams. As described above, residual water and mineral solids
present in diluted bitumen can be determined. Other residual
components can also be detected, such as the quantity of paraffinic
solvent (e.g., pentane) that is present in a PFT start-up fluid or
a hydrocarbon fluid for PFT turndown, maintenance or cleaning
(e.g., diesel). Thus, NIR spectral measurements of the start-up
fluid can be obtained; and then the concentration of a residual
component can be determined based on the NIR spectral measurements.
The quantity of residual solvent in the bitumen product stream can
also be monitored by NIR based techniques.
[0184] The bulk fluid in which the residual component is present
can be a hydrocarbon-containing fluid, such as diesel, toluene,
naphtha, etc., and may be used in context of starting up or winding
down the PFT operation. For example, diesel may be used during
start-up of the PFT process to bring the system up to a target
temperature. After preheating, the diesel needs to be removed from
the system while the normal operating streams are introduced. It is
preferable to keep the content of paraffinic solvent, bitumen, and
any other hydrocarbon mixture at relatively low levels in the
recovered diesel. It is also desired to minimize the amount of
diesel that may remain in the PFT system.
[0185] The NIR probe used for taking the NIR measurements may be a
transmission probe or a reflectance probe, and may be located on a
dedicated outlet line for removing the diesel or on one of the
downstream lines of the PFT system, for example. In some
implementations, a reflectance probe is used to take the NIR
measurements to facilitate stable spectra to be obtained. When
determining pentane content in diesel, transmission probes may also
be used, allowing acquisition of more reliable and accurate models
in the case of residual content. The NIR probes for obtaining these
measurements can be installed in cross-sectional and longitudinal
locations, as described above, and/or can also be installed in a
bypass or slip stream line, as described above.
[0186] In SRU operation, correctly detecting pentane, diesel or
diluted bitumen concentrations in the relevant stream is important
for the operation to determine the appropriate handling of the
bitumen product stream, e.g., whether the bitumen product is ready
to discharge to a farm tank or should rather be recycled back into
the PFT process for further purification particularly when the PFT
plant is in start-up, upset, and/or shutdown modes. It has been
found that using NIR reflectance spectra can help determine
accurate pentane-in-diesel and diesel-in-dilbit contents.
[0187] In some implementations, pentane-in-diesel content,
bitumen-in-diesel content, and/or diesel-in-dilbit content are
determined based on NIR techniques. Depending on various factors,
the ratio of pentane-in-diesel content, bitumen-in-diesel content,
and/or diesel-in-dilbit content can be from 0-100%. In some
implementations, a reflectance probe can be used to measure
components that are present above 1%. When measuring lower levels
(e.g., less than 0.1%) of one or more components, a transmission
probe is preferable. In some implementations, target levels for the
residual components are as follows: bitumen content in diesel is
less than 0.1 wt %, pentane content in diesel is less than 0.1 wt
%, and diesel content in bitumen is less than 0.1 wt % or less than
1 wt %.
[0188] In some implementations, NIR calibration models are
developed depending on the residual component and the target level
or threshold to be detected. For example, in the case of pentane in
diesel, different NIR calibration models may be used when the
pentane content is either below or above 0.1 wt %. In the case of
diluted bitumen or bitumen in diesel, different NIR calibration
models may be used when the bitumen content is below 0.1 wt % or
between 0.1 wt % and 1 wt %, and/or above 1 wt %.
[0189] Detecting less than 1000 ppm pentane either in diesel or in
bitumen product is relevant for both safety purposes and meeting
regulatory requirements. The paraffinic extraction process ideally
operates with very little solvent loss from the system. It was
previously believed that NIR could not determine less than 1 wt %
pentane in bitumen, for example using a univariate data processing
method, and conventional monitoring methods thus consisted of using
a headspace with Gas Chromatography (GC) measurements, which
significantly increases the constraints of engineering design and
implementation as well as operational cost. However, it was found
that such low pentane levels can be detected and monitored using
NIR based techniques with chemometric analysis.
[0190] In some implementations, the residual content to be
monitored is residual pentane content, and the bulk component is
diesel, bitumen or a bitumen-containing mixture that is diluted
with another hydrocarbon. Such bitumen-containing mixtures may be a
10/90 bitumen/toluene mixture, a 20/80 bitumen/diesel mixture, or a
mixture of bitumen and various other hydrocarbons that can include
other paraffins. In some implementations, the content of pentane to
be determined is below 1000 ppm.
[0191] In some implementations, the probe used to obtain the NIR
measurements is a transmission NIR probe. It has been found that
transmission spectra provide more reliable and accurate results
when pentane is less than 0.1 wt %. The content of pentane can be
directly measured using transmission probes.
[0192] In some implementations, chemometric methods can be used to
develop NIR calibration models that allow estimating the residual
content in the hydrocarbon-containing stream. For example, a series
of samples including different residual component contents in a
hydrocarbon product may be prepared. In some implementations, the
residual content is measured using accurate analytical methods
(such as gas chromatography, for example); and then NIR spectral
measurements of each of the samples are taken using a transmission
probe. The NIR measurements are compiled with the corresponding
laboratory measured residual contents. Both laboratory data and NIR
spectral data are process using chemometric methods to develop an
NIR calibration model. An example of modelling process is described
in more detail in Experimentation 3 further below.
[0193] During PFT operation, the online NIR measurements can be
used with developed NIR calibration model to determine the residual
pentane content in diesel, bitumen or bitumen-containing mixture.
Because the NIR measurements are online measurements, the residual
content may be determined continuously and in real time, and the
quality of the hydrocarbon-containing stream is monitored in a
continuous manner.
[0194] In terms of process control, the residual component
concentrations can be used to adjust the PFT process, including the
start-up mode or other operating modes. For example, when pentane
levels are above a desired target level in bitumen product, the
bitumen product can be recycled for further solvent recovery in the
SRU. If pentane content is elevated in diesel, the diesel stream
can be recycled back to recover additional solvent. In addition, if
it is detected that pentane content is increasing and yet is in the
acceptable target range, the PFT process can be adjusted to
stabilize solvent recovery and ensure that the PFT process stream
remains within the operating window in terms of pentane
content.
[0195] In some implementations, other residual components in PFT
process streams may be monitored. For example, residual water
content in SRU underflow and water content in recovered solvent
streams (e.g., TSRU overhead, SRU overhead and VRU underflow) may
be determined using NIR measurements and NIR calibration models.
[0196] NIR based determination and control for PFT process-aids
[0197] Because of their viscous and adhesive nature, the asphaltene
flocs tend to agglomerate and can increase the risk of plugging or
fouling in PFT equipment, and can also hamper solvent recovery by
entrapping solvent within the flocs. Therefore, process-aids, such
as dispersants, demulsifiers, defoamers or other surfactants or
suitable chemical additives can be used in PFT processes. The
surfactants can be selected and used based on various factures,
such as operating temperatures, S/B ratios, system configuration,
and so on.
[0198] It has been found that the NIR spectra of diluted bitumen
show different patterns with the addition of process-aid, as shown
in FIG. 60. Therefore, according to the pattern change of NIR
spectra, the presence and/or performance of the process-aid can be
monitored. For example, FIG. 61 shows water content in diluted
bitumen as a function of the dosage of process-aid measured by NIR.
The profile of water content in diluted bitumen can thus track the
best dosage of chemical addition, and prevent a process-aid
overdose which could reduce product quality for example.
[0199] Thus, in the PFT process, the process-aid dosage can be
monitored using NIR techniques. One can thus obtain NIR spectral
measurements as described herein, and determine a physicochemical
characteristic of an overflow stream, which may be related to the
process-aid dosage. The physicochemical characteristic may be a
compositional property of the PFT process stream (e.g., water
content, solids content) or a size of the asphaltenes agglomerates.
In some implementations, one can determine water and solids content
or asphaltene agglomerates size based on the upward velocity
previously determined, and then such characteristics can be used to
determine the dosage of process-aid.
[0200] In some implementations, the process-aid is an asphaltene
dispersant and its dosage in an FSU settling vessel is monitored
using NIR techniques. NIR spectral measurements are obtained from
the diluted bitumen overflow. The asphaltene dispersant dosage may
be monitored based on the water and/or solids content of the
diluted bitumen, or based on the size of the asphaletene
agglomerate precipitating in the FSU. Both water and solids
contents as well as asphaltenes agglomerate size may be determined
using the previously determined upward velocity of the overflow
diluted bitumen, as described herein.
[0201] Furthermore, asphaltene aggregate size can be correlated to
S/B ratio. The S/B ratio affects the quantity of asphaltenes
precipitating and also the corresponding structure of the
aggregate. It is to be noted that temperature and the type of
solvent will also play a role (e.g., a lighter solvents will
produce larger and denser asphaltene clusters than heavier
solvents). According to Stokes' Law, which describes the drag
behavior of a particle, the settling velocity of the asphaltene
agglomerates is a function of the critical size of the asphaltenes
agglomerates, the diluted bitumen viscosity, and the difference of
density between the diluted bitumen and the asphaltene
agglomerates. In a PFT process, the settling velocity generally
corresponds to the upward velocity of the overflow diluted bitumen.
The increased flux of diluted bitumen (at fixed S/B and for a
chosen solvent) will potentially drag greater quantities of solids
and water with it.
[0202] In some implementations, water and solids contents and
asphaltenes agglomerates size are determined by using Stokes' Law
along with a previously determined upward velocity of the overflow
diluted bitumen. This allows monitoring the asphaltene dispersant
dosage of the PFT process. In some implementations, the sizes of
asphaltenes agglomerates are inferred rather than directly
measuring the size, which could be done for example with
laser-based techniques. The asphaltenes agglomerates size may be
less than 100 .mu.m.
[0203] When the process-aid dosage is monitored according to the
methods described herein, corresponding actions may be taken in
response to the determined performance of the process-aid. For
example, one can control the PFT process to optimize the
process-aid dosage and control quality of PFT process streams. The
PFT process may be controlled to increase or decrease the dosage of
the process-aid, or to increase the activity by modifying other
process parameters such as the FSU feed rate or S/B ratio. It is
noted that diluted bitumen product quality does not related to
higher asphaltene precipitation rates. Thus, in certain scenarios,
controlling the PFT process can include adjusting at least one of
the feed rate of the diluted froth into the FSU, the S/B ratio, the
solvent composition, and process-aid dosage (e.g., dosage in the
diluted bitumen froth), so as to decrease the asphaltene
agglomerate settling rate and therefore the upward velocity of the
overflow diluted bitumen. [0204] NIR Based Determination for
Asphaltenes or Asphaltenes Agglomerates
[0205] Using NIR based techniques, the size of the asphaltenes
agglomerates can be monitored, and actions may be taken in response
to the measurements. Agglomerate size is a factor that can
influence settling, and can be modified to enhance settling
performance. In terms of process control, process-aids can be added
to control the size of the agglomerates. In the case where the
asphaltene agglomerate size is below a threshold, the feed rate can
be decreased, the S/B can be increased and/or the amount of
process-aid can be adjusted. [0206] NIR Based Determination of
Solubilized Water Versus Non-solubilized Water
[0207] In PFT processes, bitumen froth can typically contain
approximately 30 wt % water. Water chemistry of the "froth water"
is relevant for a number of reasons, including its impact on
process operation and on equipment. For example, higher chloride
levels in the water phase can lead to higher corrosion risks and
associated disadvantages. In some scenarios, the froth water
chemistry can be generally similar to the recycle water (RCW)
chemistry, RW being the water used in primary extraction to remove
bitumen from oil sands ore. A certain amount of RCW can be used for
flushing the pipelines and vessels in PFT (which can also be
referred to as secondary extraction) and for making up flow for the
system during abnormal operational conditions.
[0208] Water content in diluted bitumen can be divided
soluble-water content and non-soluble water (including free water
and emulsified water) content. The soluble-water present in
hydrocarbon phases would be intimately associated with the
hydrocarbon phase. As soluble-water is miscible with hydrocarbons,
it forms a homogeneous phase and the soluble-water would not
settle. Soluble-water is generally considered as "pure" water
carried in the hydrocarbon phase, and may simply follow the
hydrocarbon phase's flow through the unit operation, e.g., upward
in the settling vessel. Non-soluble water, including "free" and/or
"emulsified" water, is process water (e.g., RCW) that may be
carried over if the flux in the settling vessel becomes too high or
if process-aids are overdosed. Non-soluble water also includes
salts that can corrode equipment. The free- and/or emulsified-water
is generally present in the hydrocarbon phase as droplets that will
tend to sink in the hydrocarbon phase due to density differences.
It is therefore of interest to control free-water and/or emulsified
water content which would be the bulk carrier of problematic
salts.
[0209] Soluble-water content may be influenced by the temperature
of the hydrocarbon phase and hydrocarbon composition, since
temperature affects solubility. Free-water and emulsified-water,
which are more damaging in terms of the quality of the PFT process
streams (e.g., the diluted bitumen and the bitumen product), can be
limited in PFT process streams by adjusting certain operational
conditions, e.g., changing flux rates, S/B ratio, and/or using a
different solvent or process-aids such as demulsifiers and/or
asphaltene dispersant.
[0210] In this context, NIR can be used to measure whether any
impurities (e.g., inorganic salts such as sodium chloride or
calcium naphthenate) in water will affect water solubility in
pentane. It has been found that these impurities may affect the
solubility of water in pentane (see soluble water content in
pentane as a function of the chloride content and the temperature
in FIG. 74).
[0211] Quantitatively determining the soluble-water content in
pentane, while measuring the total water in diluted bitumen, can
allow obtaining the free-water and emulsified-water content by
subtracting soluble water content from the total water content in
diluted bitumen. In this manner, a more accurate measure of
non-soluble water can be obtained, which can then be used in
process control strategies. It has been found that NIR probes can
detect soluble-water in pentane, and that it is possible to
determine the source of water in the PFT process stream. It has
also been found that that soluble water can be detected
independently of free water and emulsified water.
[0212] In some implementations, the soluble-water content is
determined based on NIR techniques. The NIR spectral measurements
can be obtained and interpreted using NIR calibration models to
determine soluble water content. In some implementations, the NIR
calibration model is developed using chemometric methods that allow
determination of a correlation between the NIR spectral
measurements and the soluble-water. The free- and emulsified-water
content is then determined by subtracting the soluble water content
from the total water content that can also be measured using NIR
methods, as described herein.
[0213] The diluted bitumen overflow includes pentane, and the
soluble-water content in pentane may be estimated by NIR
techniques. As shown in FIG. 62, where predicted values of
soluble-water content are compared to theoretical values from water
solubility in pentane studies found in the literature, an NIR
calibration model predicting soluble-water content may be developed
to estimate the soluble-water content in pentane.
[0214] FIG. 63 shows the evolution of the predicted soluble-water
content in pentane with temperature. The soluble-water content
matches the literature values, thereby showing that soluble-water
content may effectively be estimated using NIR based
techniques.
[0215] In terms of process control, once the total water content is
determined, and according to the determination of soluble water in
pentane or in diluted bitumen, the free- and emulsified-water
content (non-soluble water content) can be determined by
subtracting the soluble-water content from the total water content.
The PFT process can be controlled in accordance with this
determined non-soluble water content (rather than the total water
content) in order to target the reduction of free-water and
emulsified-water in the diluted bitumen and/or bitumen product. In
some implementations, the PFT process is controlled by adjusting
operational conditions, such as at least one of diluted bitumen
froth feed rate, S/B ratio, process-aid dosage, solvent
composition, and operational temperature, based on the determined
non-soluble water content. The operating conditions can be adjusted
in order to minimize non-soluble water content in the diluted
bitumen and/or bitumen product or keep the non-soluble water
content below a target threshold. Enhanced precision of process
control can thus be achieved in terms of control based on the water
content of the diluted bitumen or bitumen product.
EXPERIMENTATION, MODELLING & RESULTS
[0216] Various aspects of NIR spectrometry have been studied in the
context of PFT operations. Experimentation, chemometric modelling
information, and other results are described below. Note that
different spectral calibration models are used to detect high
concentrations vs. low concentrations of the same component.
[0217] In each of the following experimentation summaries, a Bruker
Matrix FT-NIR analyzer with transmission and reflectance probes was
used for NIR measurements and analyses. Also, Bruker OPUS
Spectroscopy software was used for processing NIR spectra.
[0218] In order to evaluate the feasibility and reliability of
using online NIR analyzers for operational control and
product/stream quality control, the following steps were conducted
and the results are discussed and summarized in the experimentation
summaries below: [0219] 1. QA/QC of Laboratory Data for NIR
Modeling-Verification of the density data integrity, and use of the
strong relationship between density, and hydrocarbon content
(bitumen, solvent and asphaltenes), to identify potential errors in
the lab data; [0220] 2. Building NIR Models--Building chemometric
models for each component (bitumen content, solvent content,
asphaltenes content, solids content, water content), and for S/B
and density using the OPUS/QUANT software in calibrating the NIR
spectra via their respective laboratory data; [0221] 3.
Quantitative Analysis of Unknown Samples--Using developed NIR
models to predict S/B, density, and the composition of diluted
bitumen. [0222] 4. Comparing with RI Measurement--The NIR generated
S/B were compared to the RI generated S/B to identify a possible
correlation between the outputs of both monitoring instruments.
Experimentation 1: NIR Calibration Model for Measuring
Solvent/Bitumen/Asphaltene Content in Diluted Bitumen
[0222] [0223] QA/QC of Lab Data for NIR Modeling
[0224] Quality assurance and quality control (QA/QC) was realized
to confirm and ensure accuracy of the calibration model. A series
of diluted bitumen samples was collected for mass balance
calculation. The density and the composition of these samples were
analyzed by a density meter, and by Dean-Stark analysis.
Theoretically, the density of diluted bitumen is determined by the
contents of solvent, bitumen and asphaltenes etc.; therefore a good
correlation between the density and these components is expected.
Therefore, the integrity of the lab data may be verified using a
density correlation.
[0225] In order to collect representative samples, the density
measurement of FSU diluted bitumen (referred as Dilbit 1 in FIG. 3)
was used to determine the operation condition and timing for mass
balance sample collection. Therefore two sets of density
measurements were obtained. FIG. 3 shows that there is a strong
correlation between densities of FSU diluted bitumen (referred as
Dilbit 2) for mass balance calculation and the density of Dilbit
1.
[0226] FIGS. 4 and 5 illustrate the correlations between density
and bitumen content, solvent content, and asphaltenes content of
diluted bitumen of mass balance samples. They show that, in a first
step, weak correlations were obtained between the density of
diluted bitumen versus its bitumen content, solvent content, and
asphaltenes content. Because density data were measured twice, they
were more reliable. Four data points of bitumen content, and
solvent content were treated as outliers and would not be used for
building NIR models and RI correlation. FIGS. 6 and 7 show that
much better correlations were obtained after four data points of
bitumen content, and solvent content were removed.
[0227] Although four data points of solvent content were removed in
FIG. 5, there were two other data points scattered far away from
the correlation curve in FIG. 7. They were outliers, so these
solvent data points were removed. FIG. 8 shows an even stronger
correlation after two data points of solvent content were removed.
After removing all outliers, final correlations between density and
bitumen content, and asphaltenes content were obtained as shown in
FIG. 9. [0228] Sampling Method
[0229] During the trials, there were two methods used for sample
collection. Most samples were collected in the glass jar through a
cooling coil, and some samples were collected in the sample bomb.
Comparing the compositional analysis of samples collected in two
kinds of containers at the same time, higher asphaltenes content
were obtained in samples collected by the sample bomb. Table 1
summarizes the discrepancy of the composition of these samples.
TABLE-US-00001 TABLE 1 Comparison of the Composition of Diluted
bitumen Collected in the Glass Jar and the Sample Bomb Sample
Method Asphaltenes, Bitumen, Solvent, Discrepancy.sup.2, % ID
collected % % % Asphaltenes Bitumen Solvent 6.1.2 CC.sup.1 9.1 36.0
64.0 -23.1 -0.7 0.4 SB.sup.1 11.5 36.3 63.7 6.4.2 CC 8.9 35.6 64.4
-2.9 0.5 -0.3 SB 9.2 35.5 64.5 7.2.1 CC 8.5 35.1 64.9 -28.9 2.3
-1.2 SB 11.3 34.3 65.7 7.3.1 CC 9.5 37.2 62.8 -11.9 -0.4 0.2 SB
10.7 37.3 62.7 7.4.1 CC 8.0 34.1 65.9 -8.5 -1.1 0.6 SB 8.7 34.5
65.5 7.5.1 CC 8.7 35.6 64.4 -8.2 0.1 0.0 SB 9.4 35.6 64.4 7.6.1 CC
8.7 35.7 64.3 -10.9 -0.7 0.4 SB 9.7 36.0 64.0 7.7.1 CC 8.9 36.1
63.9 -5.6 0.4 -0.2 SB 9.4 35.9 64.1 .sup.1CC--Cooling coiled
column; SB--Sample bomb; .sup.2% of discrepancy = (Data.sub.CC -
Data.sub.SB)/((Data.sub.CC + Data.sub.SB)/2)*100
[0230] Since most samples were collected by the cooling coil to
glass jar, asphaltenes content obtained for the samples collected
by the sample bomb were used for determining how much asphaltenes
should be added in the samples collected in the cooling coil
method. The adjusted asphaltenes was used for building NIR
asphaltenes model. Other measurements from the samples collected by
the sample bomb were not used for their NIR models.
[0231] Glass jar is the normal container for sample collection
during trials. Two sample collection methods were used for
comparison. Diluted bitumen was firstly cooled down through the
cooling coil and then collected into the glass jar. In the second
method, diluted bitumen was collected through the sample bomb
directly. In order to investigate any difference between the two
sample methods, ten samples were collected in both ways. FIG. 75
illustrates the detailed procedures of two sample collection
methods. Considering different cooling processes in these two
methods, some variability of the composition measurement in diluted
bitumen were expected and lab results were used to validate the
expectation shown in FIG. 75. Green font indicates the expectation
matched laboratory results and red font indicates the expectation
did not match laboratory results.
[0232] By comparing the two methods, it has been found that the
sample collected by the sample bomb would be more representative of
real samples and contain higher asphaltenes content because some
asphaltenes might precipitate out and adhere on the wall of the
cooling coil due to temperature dropped.
[0233] According to the results, when the temperature of diluted
bitumen reduces from 90.degree. C. to 50.degree. C., about 2%
asphaltenes will precipitate out from diluted bitumen (FIG. 76).
FIG. 77 displays bitumen content, solvent content, and asphaltenes
content in diluted bitumen and the density of diluted bitumen of
two samples collected at the same time. The standard deviation of
the test method was shown in the data points for reference.
[0234] Results in FIG. 77 show that except for asphaltenes content
in bitumen all other measurements for two sample methods were very
close and the difference of most measurements fell in the range of
the standard deviation of the test method. Although the difference
of bitumen measurement for samples collected by two methods was in
the range of the standard deviation, slightly consistently higher
bitumen content measured in the sample collected in the sample bomb
was observed in FIG. 78.
[0235] FIG. 79 shows the correlation between the density of diluted
bitumen and S/B. It appears that both methods give strong
correlations. Considering two samples collected at the same time,
although two methods show similar trend, slightly lower S/B for the
sample collected by the sample bomb method when the samples were
taken in the same condition. The difference of S/B of two samples
is less than 0.05.
[0236] FIG. 80 shows that the relationship between S/B of diluted
bitumen and asphaltenes content in bitumen measured in the samples
collected by two methods. It clearly shows that at same S/B ratio,
asphaltenes contents in the samples collected by the sample bomb
method were 0.9-1.3% higher than those collected by the cooling
coil method.
[0237] Because most samples were collected using the cooling coil
method, asphaltenes contents in these samples were underestimated.
So 0.9-1.3% of asphaltenes will be added for these measurements.
According to the formula shown in FIG. 7A, the amount of the
asphaltenes was adjusted depending on S/B of diluted bitumen.
[0238] From the above discussion, it can be concluded that 0.9-1.3%
of asphaltenes precipitated out during sampling by the cooling coil
method. Since most samples were collected by the cooling coil to
glass jar, the asphaltenes contents in these samples were
underestimated, the asphaltenes content were adjusted, and then
used for building NIR asphaltenes model. Other measurements from
the samples collected by the sample bomb were not used for their
NIR models.
[0239] Finally, it is to be noted that the difference of
asphaltenes content in diluted bitumen collected by two methods did
not cause significantly the difference of S/B calculation (less
than 0.05).
TABLE-US-00002 TABLE A1-1 Comparison of Lab measurements of diluted
bitumens collected by two methods No. of Method Density,
Asphaltene, Bitumen, Solvent, MB collected kg/m3 % % % S/B 6.2.1 CC
0.7208 8.3 35.7 64.3 1.80 SB 0.7219 11.0 35.4 64.6 1.83 6.4.2 CC
0.7206 8.9 35.6 64.4 1.81 SB 0.7214 9.2 35.5 64.5 1.82 7.3.1 CC
0.726 9.5 37.2 62.8 1.69 SB 0.7265 10.7 37.3 62.7 1.68 7.4.1 CC
0.7164 8.0 34.1 65.9 1.93 SB 0.7171 8.7 34.5 65.5 1.90 7.5.1 CC
0.7213 8.7 35.6 64.4 1.81 SB 0.7208 9.4 35.6 64.4 1.81 7.6.1 CC
0.7211 8.7 35.7 64.3 1.80 SB 0.7215 9.7 36.0 64.0 1.78 7.7.1 CC
0.7217 8.9 36.1 63.9 1.77 SB 0.7225 9.4 35.9 64.1 1.79 8.2.1 CC
0.7199 8.5 35.4 64.6 1.82 SB 0.7216 10.4 35.7 64.3 1.80
[0240] In PFT, usually less than 0.1 wt. % of filterable solids and
water are left in diluted bitumen, which brings a significant
challenge to determine their contents. Generally speaking, solids
content and water content in diluted bitumen are directly
attributed to the amount of asphaltenes rejected. In other words,
more asphaltenes are rejected, less solids and water left in
diluted bitumen. FIG. 10 (a) displays the relationship between
asphaltenes content and solids content in diluted bitumen from the
lab results. According to previous studies, one outlier was
identified.
[0241] FIG. 10 (b) shows the relationship between asphaltenes
content versus water content in diluted bitumen from lab results.
Although a weak correlation was observed, one data point was
scattered far away from majority data, so this data was marked as
an outlier.
[0242] A method was developed and used to assess water and solids
collected in settling tests, as shown in FIG. 82. It was found that
there is a good correlation between KFW and filterable solids in
supernatant phase.
TABLE-US-00003 TABLE 2 summarizes mass balance data whether or not
used for building the calibration of the NIR measurement. FSU1 OF
FSU1 OF FSU 1 OF FSU1 OF Karl FSU1 OF Asph by probe Bitumen Solvent
solids Fisher No. MB density (%) (wt. %) (wt. %) (wt. %) Water, ppm
S/B Note 1 6.1.1 0.7233 9.05 36.03 63.97 0.087 245 1.78 2 6.1.2
0.7235 9.13 35.99 64.01 0.113 278 1.78 3 6.1.2 BS 0.7263 Outliers
determined by the correlation between density vs. solvent, bitumen,
asphaltenes contents 4 6.2.1 0.7208 8.26 35.66 64.34 0.017 278 1.80
5 6.2.2 BS 0.7219 Outliner 35.37 64.63 1.005 n.a. 1.83 6 6.2.3
0.7211 10.05 35.77 64.23 Outliner 2095 1.80 7 6.2.4 0.7211 9.05
35.57 64.43 0.085 185 1.81 8 6.3.1 0.721 8.89 35.58 64.42 0.04 228
1.81 9 6.3.2 0.7208 9.23 35.55 64.45 0.091 185 1.81 10 6.3.3 0.7216
9.38 35.76 64.24 0.124 182 1.80 11 6.3.4 0.7215 9.17 35.61 64.39
0.049 242 1.81 12 6.3.5 0.7214 9.24 35.63 64.37 0.042 211 1.81 13
6.3.6 0.7216 9.53 Outliers determined 0.05 246 n.a. 14 6.3.7 0.7218
9.45 by the correlation 0.052 229 15 6.3.8 0.7217 9.35 between
density vs. 0.047 316 16 6.3.9 0.7215 9.15 bitumen, solvent. 0.047
286 17 6.4.1 0.7202 8.99 35.36 64.64 0.125 320 1.83 18 6.4.2 0.7206
8.94 35.64 64.36 0.044 426 1.81 19 6.4.2 BS 0.7214 9.20 35.46 64.54
0.055 n.a. 1.82 20 6.4.3 0.7204 8.83 See above note. 0.012 426 n.a.
21 7.2.1 0.7201 8.45 35.10 64.90 0.012 245 1.85 22 7.2.1 BS 0.7227
Outliers determined by the correlation between density vs. solvent,
bitumen, asphaltenes contents 23 7.2.2 0.7203 8.42 35.28 64.72
0.015 198 1.83 24 7.2.3 0.7206 8.37 35.33 64.67 0.036 256 1.83 25
7.2.4 0.7193 8.79 35.28 64.72 0.093 n.a. 1.83 26 7.2.5 0.7199 8.73
34.95 65.05 0.035 n.a. 1.86 27 7.3.1 0.726 9.50 37.18 62.82 0.029
216 1.69 28 7.3.1 BS 0.7265 10.70 37.32 62.68 0.085 n.a. 1.68 29
7.3.2 0.7254 9.69 37.01 62.99 0.084 255 1.70 30 7.3.3 0.7259 9.92
37.03 62.97 0.059 427 1.70 31 7.4.1 0.7164 7.99 34.09 65.91 0.018
218 1.93 32 7.4.1 BS 0.7171 8.70 34.48 65.52 0.139 n.a. 1.90 33
7.4.2 0.7166 8.11 34.40 65.60 0.057 252 1.91 34 7.4.3 0.7167 7.85
34.35 65.65 0.017 Outlier 1.91 35 7.5.1 0.7213 8.66 35.59 64.41
0.022 347 1.81 36 7.5.1 BS 0.7208 9.40 35.57 64.43 0.058 n.a. 1.81
37 7.5.2 0.7213 8.70 35.74 64.26 0.027 185 1.80 38 7.5.3 0.7206
8.84 35.56 64.44 0.017 Outlier 1.81 39 7.6.1 0.7211 8.70 35.70
64.30 0.015 344 1.80 40 7.6.1 BS 0.7215 9.70 35.96 64.04 0.107 n.a.
1.78 41 7.6.2 0.7214 9.21 35.62 64.38 0.118 379 1.81 42 7.7.1
0.7217 8.89 36.05 63.95 0.022 379 1.77 43 7.7.1 BS 0.7225 9.40
35.89 64.11 0.053 n.a. 1.79 44 7.7.2 0.7217 8.73 35.96 64.04 0.019
n.a. 1.78 45 8.3.2 0.7253 9.84 37.17 62.83 0.026 353 1.69 46 8.3.3
0.7275 10.43 37.81 62.19 0.046 370 1.64 47 8.4.1 0.7264 10.57 37.42
62.58 0.02 271 1.67 48 8.4.2 0.7267 10.94 37.35 62.65 0.16 318 1.68
49 8.4.3 0.7266 10.57 37.48 62.52 0.021 302 1.67 50 8.5.1 0.7266
10.31 37.57 62.43 0.021 320 1.66 51 8.6.1 0.7271 10.62 37.60 62.40
0.02 367 1.66 52 8.2.1 0.7199 8.48 35.43 64.57 0.026 217 1.82 No
NIR 53 8.2.1 BS 0.7216 10.40 35.73 64.27 0.104 n.a. 1.80 spectra 54
8.2.2 0.7213 9.75 35.77 64.23 0.161 386 1.80 55 8.2.3 0.7219 9.64
36.05 63.95 0.037 371 1.77 56 8.3.1 0.7256 9.98 37.14 62.86 0.024
317 1.69 57 7.1.1 Operated in the counter mode. This group of data
will be used as test 58 7.1.1BS data to validate the calibration
curves generated using above data. 59-65 8.1.1-8.1.5
[0243] Developing NIR Models
[0244] Chemometrics method was used to build the following models
in the OPUS/QUANT Spectroscopy Software which was provided by
Bruker.
[0245] The modeling process shown in FIG. 12 was completed for all
required measurements. The first four steps only show the S/B model
as an example. However the theoretical process was the same for
other measurements. [0246] a) Organization of NIR Spectra According
to Pilot Mass Balance and Lab Data
[0247] A series of NIR spectra were selected and organized
according to the time which the lab samples were collected for mass
balance calculation. In order to account for possible deviations in
sample collecting times, three spectra covering a five minute
window over the sample time would be used. At a high level, the
three spectra change negligibly in this window. However, they
changed noticeably on a micro scale (FIG. 13). This allows for an
accurate average over the sample window to be used for each data
point in the model. An example of the spectra organization used for
several points in the models for Week A is shown below in Table 3,
with the points used in FIG. 13 highlighted. [0248] b) Development
of Preliminary NIR Models
[0249] After compiling the relevant spectra, all of the spectra
with available lab data were loaded into the OPUS/QUANT software in
order to understand whether NIR software itself can identify the
outliers which were removed based on QA/QC analysis. A series of
the models (referred as baseline models) were obtained. One can see
these models did not trend with high correlation, which indicated
that NIR software can pick up poor data points. FIG. 14 shows the
initial model for the S/B with the green line representing the 1/1
line between the predicted and true values, and the blue line
representing the correlation line.
TABLE-US-00004 TABLE 3 An example of respective NIR spectra
organized for week A Lab Data Asphaltene OPUS OPUS Sample by probe
Bitumen Solvent Solids Water Flux File Numbers File No MB# Time S:B
(%) (wt %) (wt %) (wt %) (KFW) Density (mm/min) Reflectance Time 1
6.1.1 12:15 1.78 9.05 36.03 63.97 0.087 245 0.7233 338 297 12:14
12:15 1.78 9.05 36.03 63.97 0.087 245 0.7233 338 301 12:16 12:15
1.78 9.05 36.03 63.97 0.087 245 0.7233 338 309 12:19 2 6.1.2 13:35
1.78 9.13 35.99 64.01 0.113 278 0.7235 354 519 13:34 13:35 1.78
9.13 35.99 64.01 0.113 278 0.7235 354 525 13:36 13:35 1.78 9.13
35.99 64.01 0.113 278 0.7235 354 533 13:39 3 6.1.2(SB) 13:35 1.76
11.51 36.25 63.75 0.549 0.7263 342 523 13:35 13:35 1.76 11.51 36.25
63.75 0.549 0.7263 342 527 13:37 13:35 1.76 11.51 36.25 63.75 0.549
0.7263 342 537 13:40 17 6.4.1 10:45 1.83 8.99 35.36 64.64 0.125 320
0.7202 530 4081 10:44 10:45 1.83 8.99 35.36 64.64 0.125 320 0.7202
530 4087 10:46 10:45 1.83 8.99 35.36 64.64 0.125 320 0.7202 530
4095 10:49 18 6.4.2 11:15 1.81 8.94 35.64 64.36 0.044 426 0.7206
513 4165 11:14 11:15 1.81 8.94 35.64 64.36 0.044 426 0.7206 513
4171 11:16 11:15 1.81 8.94 35.64 64.36 0.044 426 0.7206 513 4179
11:19 19 6.4.2(SB) 11:15 1.82 9.2 35.46 64.54 0.055 0.7214 515 4169
11:15 11:15 1.82 9.2 35.46 64.54 0.055 0.7214 515 4173 11:17 11:15
1.82 9.2 35.46 64.54 0.055 0.7214 515 4181 11:20
[0250] c) Improvement of NIR Models Using QA/QC Processed Data
[0251] The integrity of the lab data was verified using a density
correlation. Six outlier points were identified as being
inconsistent with the data set and were dismissed as lab errors.
Upon the removal of these six outliers, the models improved. The
QA/QC improved model for S/B is shown in FIG. 15, and Table 4
summarizes the points removed with respect to above Table 3.
TABLE-US-00005 TABLE 4 Summary of Data points removed during the
QA/QC analysis MB Data Removed (QA/QC) Reason 3 inconsistent with
Density Data 13 inconsistent with Density Data 14 inconsistent with
Density Data 15 inconsistent with Density Data 16 inconsistent with
Density Data 21 inconsistent with Density Data
[0252] d) Recalibration of Models by Removing Outliers Identified
by NIR software
[0253] After removing the outliers identified in the lab data
QA/QC, the NIR software was identifying other outlier data points.
Verified with operational conditions and sampling methods, these
outliers occur either at operational upset or the lab results from
both sample methods to correlate one NIR spectra. A summary of the
excluded points for S/B is shown below in Table 5, with reference
to above Table 3.
TABLE-US-00006 TABLE 5 Summary of outliers identified by the NIR
Point Removed (NIR Identified) Operational Condition 6 Sample Bomb
- No relation to Cool Coil Model 19 Sample Bomb - No relation to
Cool Coil Model 20 Residual asphaltenes dispersant present in
system. 22 Residual asphaltenes dispersant present in system. 23
Residual asphaltenes dispersant present in system. 24 Residual
asphaltenes dispersant present in system. 25 Residual asphaltenes
dispersant present in system. 27 Sample Bomb - No relation to Cool
Coil Model 30 Plugged contact section for first stage (Low
confidence measurement) 31 Sample Bomb - No relation to Cool Coil
Model 33 Uncharacteristic shift in spectra (See FIG. 17 below).
Potential sample time deviation. 35 Sample Bomb - No relation to
Cool Coil Model 39 Sample Bomb - No relation to Cool Coil Model 42
Sample Bomb - No relation to Cool Coil Model 44 Large differential
pressure (high deviation from target) 45 Large differential
pressure (high deviation from target)
[0254] After removal of all outliers identified by QA/QC and NIR
software, the final models for S/B, density, bitumen content,
solvent content, and asphaltenes content are shown in FIGS. 17 a,
b, c, d, and e.
[0255] Early in the modeling process, it was observed that the
models showed a strong correlation for density, bitumen content,
and solvent content (and thus S/B), and a moderate correlation of
asphaltene content. However, the models for water content and
solids content were relatively weak. Since water and solids were
present in the product stream in small quantities, it is
challenging to obtain the accurate laboratory measurements. This
made the reduced overall reliability for using this lab data to
build NIR models. These models are shown below in FIG. 18, but were
not used for progressing further in the work process. [0256] e)
Improve Model with Different Data Preprocessing Method
[0257] The OPUS/Quant software contains ten data preprocessing
methods, but the two of focus for this project are the Multiple
Scatter Correction (MSC) and the First Derivative (1D) based on the
recommendation of Suncor NIR expert. According to the principle of
data processing provided by the OPUS software, MSC method is
suitable to the NIR spectra generated by light scattering of the
colloidal particles in the medium. Asphaltenes/water/solids
agglomerates exist in diluted bitumen, and they will affect the
apparent NIR absorption. First Derivative method will emphasize the
chemical information of diluted bitumen such as bitumen, solvent
and asphaltenes in NIR spectra. In the meantime, this method could
eliminate the temperature impact on the spectra. However, it could
eliminate the NIR absorption generated by light scattering from the
particles in the medium.
[0258] Considering chemical characteristic of diluted bitumen,
after obtaining these NIR models processed by the MSC method, the
First Derivative method was used to compare whether or not the
models can be further improved.
[0259] As stated above, to ensure the reproducibility of the
calibration samples, multiple spectra were used for each sample in
the calibration. As these samples are not identical, a data
preprocessing procedure can be used to bring them into line with
each other. Data preprocessing can eliminate any variations in
offset or different linear baselines by normalizing the
spectra.
[0260] The MSC procedure is used to correct signals from noise and
background effects which cause baseline shifting and tilting. It
performs a linear transformation of each spectrum for it to best
match the mean spectrum of the whole set, and often used for
spectra measured in diffuse reflection, this preprocessing at its
optimized data range yielded a very accurate model which is shown
below in FIG. 19 for S/B.
[0261] The First Derivative method was used to provide a
comparative model shown below in FIG. 21 for S/B. This method
involves calculating the first derivative of the spectrum, and
emphasizes steep edges of a peak which are attributed to the
chemical characteristic of bitumen, asphaltenes and solvent in
diluted bitumen. However spectral noise is also enhanced.
[0262] In FIG. 20, a relatively accurate model is shown overall.
However, the calibration points on the higher end of the line are
identified as outliers by the NIR software. FIG. 21 shows the
calibration range exclusive of the outliers and shows an accurate
correlation over the mid-lower ranges. These two data preprocessing
methods (MSC and 1D) are both reasonably accurate. However, MSC
appears slightly more reliable for this data set at higher values.
These results indicated that both chemical characteristics of
diluted bitumen and physical sizes of asphaltenes/water/solids
agglomerates in diluted bitumen play important roles in their NIR
absorption. However, the physical size of asphaltenes/water/solids
agglomerates in diluted bitumen seems more dominate this
reflectance NIR absorption.
[0263] With respect to the frequency region, the PLS regression is
a full spectrum method, meaning that the model generally improves
as the amount of data points increases. However, as seen in FIG. 22
below, there are areas of the spectra (grey areas) that show either
a lot of spectral noise, or no information. By excluding these
areas, and optimizing effective frequencies (white areas), there is
an increase in accuracy for the chemometric model. These ranges
reflect water absorption (5200 cm.sup.-1 and 7200 cm.sup.-1),
bitumen absorption (5500-6000 cm.sup.-1), and particle size
information (the shift of NIR spectra toward low absorption).
[0264] The OPUS/QUANT software includes a frequency optimization
tool that automatically checks common frequency regions in
combination with several data preprocessing methods, and generates
a list of frequency ranges with their respective rank and RMSECV
(root mean square error of cross validation). From this, the
appropriate range to validate the model can be chosen. This
populated optimization tool is shown in FIG. 23.
[0265] For each frequency range, for each data processing method,
there is a rank with an associated RMSECV. By plotting RMSECV with
respect to the rank as seen in FIG. 24, the optimal rank for the
model can be determined.
[0266] The root mean square error of cross validation is a measure
of the error of the model and is used as criterion to judge the
quality of the method. The rank is the number of factors used to
represent the model. Too few factors results in an under fit model
where many features are not explained. On the other hand, over
fitting the model only adds noise and degrades the model. Choosing
the optimal rank is tied to the quality of the overall model.
Residual Prediction Deviation (RPD) is another parameter to judge
the reliability of the prediction.
[0267] Residual Prediction Deviation (RPD) is the important
parameter to judge the reliability of the prediction. Table 7 lists
the value of RDP to evaluate the models. A summary of R.sup.2, and
RPD, and data used in the final models for bitumen content, solvent
content (S/B), asphaltenes content, and density of diluted bitumen
is listed in Table 6 for both MSC and 1D models.
TABLE-US-00007 TABLE 6 Guideline of NIR modelsused for prediction
RPD Classification Application <1.0 very poor not recommended
1.0-2.4 poor not recommended 2.5-2.9 fair rough screening 3.0-3.9
reasonabe screening 4.0-5.9 good QC 6.0-7.9 very good OA 8.0-10.0
excellent any application >10.0 superior as good as
reference
TABLE-US-00008 TABLE 7 Summary of final calibration model details
Data Data Total Used % Used Used % used R.sup.2- R.sup.2- Lab for
for for 1D for RPD Measurement MSC 1D Data MSC MSC Model 1D MSC 1D
Density 92.0 90.4 37 28 76 27 73 3.53 3.23 S:B 90.0 88.6 37 28 76
27 73 3.16 2.97 Solvent 90.4 89.0 37 28 76 27 73 3.22 3.02 Bitumen
90.4 89.0 37 28 76 27 73 3.22 3.02 Asphaltenes 76.2 68.1 41 38 93
38 93 2.2 1.77 Water 50.0 39.7 38 33 87 33 87 1.41 n.a. Solids
-17.7 2.8 41 38 93 38 93 0.92 n.a.
Experimentation 2: Quantitative Analyses of Unknown Samples; S/B,
Density, and Composition of Diluted Bitumen in a Three-Week
Pilot
[0268] Quantitatively Analysis of Spectra Collected in Three-Week
Pilot
[0269] After finalizing two sets of models (MSC and 1D), they were
used to predict S/B, density, and the composition of diluted
bitumen over the timeline of each week in the pilot. Two spectra
were selected every 30 minutes during the operational weeks. From
this, the OPUS/Quant software analyzed the spectra with respect to
the loaded model (MSC and 1D), and provided predictions for each
measurement. FIGS. 25 and 26 respectively display the profiles of
S/B and asphaltenes content for the MSC model-Week C generated by
the software, as an example of the software output. The x-axis
represents the time point with points taken every 30 mins. The
y-axis represents the weight % of the asphaltenes.
[0270] In running a quantitative analysis, the NIR model produces a
prediction based on the spectra inputted. The prediction points are
displayed in green square, green star, red square and red star as
shown in FIG. 25. If the date point is shown as a green square, it
means this prediction is above 95% confidence level in the
perspective of statistical analysis. The confidence level reduces
in the order of green square, green star, and red square, which can
be broken down into outside ranges, and outliers. When a value is
predicted to be outside of that certainty range it is in the NIR
software. The analysis will still provide prediction for this
value, with lower accuracy, such as shown in green star and red
square. If the point is flagged by a red star, it means this
prediction will be no more trustful.
[0271] Overall, the predictions trend well within the accuracy
margin of the model used, with the stronger models (density, S/B)
providing more accurate results then the weaker models
(asphaltenes). Many red stars (corresponding to outlier data) are
displayed in FIGS. 25 and 26. After verifying with the operational
team, it was confirmed that these outliers were attributed to major
operational upsets in the pilot, and summarized below in Table 8.
It means that these stars potentially can be used to identify if
any upset occurs and which time frame of the operation.
TABLE-US-00009 TABLE 8 Summary of Operational Conditions and the
Resulting NIR Outliers Outlier Condition Test 40229-40563 Flushing
out system with water solvent, error in NIR asphaltenes readings;
S:B model predicts adjusted levels in the system. Test 64461-65377
Pilot not in operation; blank data Test 65457-66269 pilot operating
in 2 stage mode with chemical addition; or tar is not incorporated
in the calibration model Test 71581-71655 Pilot not in operation;
Blank data
[0272] When verifying the accuracy of the components (with respect
to density) and operational upsets, removing these outlier points
significantly improves result integrity and the overall
correlation, especially in Week A and C where the first several
hours of NIR recordings for each week occurred when the pilot was
not in operation.
[0273] FIGS. 28 and 29 show the NIR generated data using for each
week (MSC) for S/B and density and bitumen content, solvent
content, and asphaltenes content respectively are shown minus the
rejected data points. The dashed lines represent the change in
weeks. It is to be noted that all NIR models were developed based
on the data collected when FSU was operated in a single stage mode.
However these models were well applied to generate reasonable
prediction for the composition of diluted bitumen when the FSU was
operated in a counter mode. This indicates the generally
application of NIR measurement which naturally captures the
information related to the composition of diluted bitumen
regardless how to operate FSU. [0274] Verification of the
Correlation Predicted NIR models
[0275] As mentioned herein, there is a strong correlation between
the density and bitumen content, solvent content, and asphaltenes
content in diluted bitumen, and this correlation exists naturally
regardless of analytical methods or data processing methods. It is
therefore possible to use this correlation to determine the
accuracy of the independently built models. The accuracy of the NIR
calibration model was validated by checking this correlation of the
density predicted vs. individual component predicted from
individual model. The comparisons between NIR prediction values and
lab data are seen below in FIGS. 30 to 33 for S/B and asphaltenes
content for the MSC and 1D models respectively. [0276] Density vs.
S/B or asphaltenes content generated values by NIR models for the
full pilot timeline [0277] Density vs. S/B or asphaltenes content
of lab data at the lab sampling times
[0278] For the MSC model, the modeled density calibration was very
strong for modeled S/B, and matched the lab measurement very well.
The 1D model showed similar trends at marginally less accuracy.
This shows that with adequate accuracy, an S/B trend over the
course of the pilot can be monitored by NIR.
[0279] The other major component is asphaltenes. With a less
accurate (R2) model, there was less confidence in predicting these
values accurately, which shows in the density correlation. The 1D
model shows similar accuracy: there is still a positive trend
showing increased asphaltenes content as the density increases.
Overall, given the weaker model, the asphaltenes results were
concluded as adequate.
Experimentation 3: Water and Solids Contents in FSU Overflow
[0280] Building NIR calibration model for water and solids contents
in diluted bitumen using NIR Spectra Collected in Week A
[0281] As described above, relatively weak water and solids models
were obtained based on lab results of water and solids analyses. It
was found that the water content reported in the FSU overflow was
consistently below the solubility limit for water in pentane at the
pilot operational condition. The lower water content was found to
be attributed to the rapid precipitation of water in diluted
bitumen during sample cooling from plant operation condition to lab
analysis condition. However, NIR was installed online; and its
measurement should capture real water content in diluted
bitumen.
[0282] In Week A operation, S/B of the overflow was fixed at 1.77,
and only changed parameter was the flux of FSU. FIG. 36 displays
the NIR spectra of diluted bitumen collected in Week A. The pattern
of these spectra were similar, however, the absorbance of NIR
spectra declined corresponding with the increase of the flux. In
the meantime, water peaks at 5200, and 7200 cm.sup.-1 gradually
increased. According to the absorption peaks of water and the shift
of NIR spectra, higher water content and larger size of
asphaltenes/water/solids agglomerates in diluted bitumen were
determined.
[0283] Typical froth is approximately 60% of bitumen, 30% of water,
and 10% of solids. Most water in the froth is free water, and
easily to precipitate out. Less than 10% of water is in emulsified
water droplets. In 10% of solids, majority of solids are dominated
by clays, carbonates and heavy minerals, which particle size is
less than 10 micron.
[0284] In PFT, when the froth is diluted with pentane at S/B of
1.6, and a portion of the asphaltenes is precipitated out from
hydrocarbon phase as agglomerates along with water droplets and
fines. However, the size of these agglomerates will directly affect
their removal from diluted bitumen and the quality of diluted
bitumen. In the pilot operational condition, the Reynolds number of
the order of 0.9, this value of Re is well within the range of
applicability of the so call "creeping flow" regime, in which
inertial effects can be neglected and the Stokes Law is suitable
for describing the flow. According to Stocks Law, the critical size
(dc) of the agglomerates released from diluted bitumen can be
calculated based on the density (.rho.f) and viscosity (.mu.f) of
diluted bitumen, and the density (.rho.c) of agglomerates. The
calculated critical size of agglomerates increased with increasing
flux shown in Table 8.
[0285] Comparing NIR spectrum collected in the lowest flux with
that in the highest flux shown in FIG. 37, it seems approximately
three times of water differences between these two samples. In
water solubility study, it was determined that the minimum water
content in diluted bitumen should be around 0.11% at 90.degree. C.
If one assumed the lowest water content was 0.11%, the highest
water content should be at 0.33%.
[0286] Most solids analyses fell in the range of 0.04-0.07%, and
did not show any trend with the variation of operational condition.
Many studies show that fines removal was correlated to water
removal in froth treatment process. Water-to-solids ratio is
obtained based on the lab results shown in FIG. 11. Herein, it was
assumed that the lowest solids content corresponded to the lowest
water content. All lab results and calculated water and solids
values are listed in Table 9.
[0287] The MSC data process method was used to process NIR spectra
collected in Week A. As shown in FIGS. 38a and 38b, when the
calculated water content and solids content were used to build NIR
models, it was found very strong NIR water model and solids model
obtained (high R.sup.2 and high RPD). Because the OPUS software was
built on complex mathematic and statistical analysis, the strong
models reflect reasonable correlation between theoretical water and
solids values versus their NIR spectra.
[0288] Although NIR water model and solids model were developed
based on Week A spectra, these models were used to predict water
and solids contents in diluted bitumen in the whole pilot period,
and the predicted results are displayed in FIGS. 39 and 40. The
results show that both water model and solids model can well
predict water content and solids content in diluted bitumen for the
whole period of the pilot. [0289] Investigation of Water and Solids
Contents in Diluted bitumen with the Variety of Operational
Conditions in Detail
[0290] FIGS. 41 and 42 show water content and solids content in
diluted bitumen in Week A. Since S/B was kept consistently at 1.77,
water content and solids content gradually increased with the flux,
which trend was reasonable as what was deduced above.
[0291] In Week B, operational parameters were various from the
application of asphaltenes dispersant, S/B, and the flux. FIGS. 43
and 44 show water content and solids content in diluted bitumen in
Week B. By comparing operational condition and water content and
solids content in diluted bitumen, it was found that the trend of
the variation of water and solids reasonably reflected the change
of the operational condition, such as higher S/B produced better
quality of diluted bitumen (lower water content and solids
contents).
[0292] Week C's prediction further confirmed that operational
changes could be captured by NIR spectra which were align well with
the water content and solids content in diluted bitumen. Even the
change of the dosage of asphaltenes dispersant was detected by NIR
spectra because its addition affects the size of
asphaltenes/water/fines agglomerates. Overdosing of asphaltenes
dispersant could emulsify water and result in higher water content
and solids contents in diluted bitumen, as shown in FIGS. 45 and 46
in the range of the data points from 30-95.
[0293] FIG. 47 maps the water content and solids content in diluted
bitumen in three week of pilot period. The results show that NIR
can be used for measuring water content and solids content in
diluted bitumen to control the product quality. In the meantime, in
accordance to the variation water content and solids content in
diluted bitumen, the operational conditions could be monitored
simultaneously.
[0294] The critical size of asphaltenes/water/solids agglomerates
for precipitating out hydrocarbon phase also can be modeled based
NIR spectra. Very strong NIR model was built for measuring the
critical size of asphaltenes/water/solids agglomerates, as shown in
FIG. 48. FIG. 49 displays the predicted critical size of
asphaltenes/water/solids agglomerates in the whole pilot. The
critical size is in the range of 63 .mu.m-78 .mu.m.
Experimentation 4: Residual Pentane In Diesel, Diluted Bitumen Or
Bitumen
[0295] A series of pentane-in-diesel solutions were prepared. The
concentration of pentane was less than 1000 ppm. Their
concentration was determined by gas chromatography which is widely
used with high accuracy. The results measured by GC were used as
references for modeling and validation. Their NIR spectra were
recorded as shown in FIG. 68. Although the variation between
spectra is not easily distinguishable by human eye, the difference
is significant enough for NIR with chemometrics to identify.
[0296] These spectra were processed by applying chemometrics to
calibrate the pentane concentration in diesel using references. A
very strong calibration was established. The standard deviation
(SD) of this model is 35.4 ppm shown in FIG. 69. This accuracy is
good enough to meet the detection limit requirement of this stream.
FIG. 70 shows excellent agreement between predicted values by the
NIR model and the actual analytical results measured by GC.
[0297] Similar measurements were conducted for pentane in 10%
bitumen toluene solutions. The concentration of pentane in
solutions was determined by GC analysis. The results measured by GC
were used as the reference for modeling and validation. The NIR
spectra were recorded for less than 1000 ppm pentane in 10% bitumen
toluene solutions shown in FIG. 71.
[0298] These spectra were processed by applying chemometrics to
calibrate the pentane concentration in 10% bitumen toluene
solutions using references. An even stronger calibration was
established. The standard deviation of this model is 10.9 ppm shown
in FIG. 72. This accuracy can meet the detect limit requirement of
this stream. FIG. 73 shows that excellent agreement between
predicted values by the NIR model and the actual analytical results
measured by GC.
[0299] Tables 10 and 11 give a summary of NIR technique application
in PFT process (Table 10), and more particularly on residual
solvent analysis (Table 11).
Experimentation 5: NIR Based Techniques for Free, Emulsified,
Soluble Water
[0300] Experiments were performed to detect the composition of the
supernatant in a settling test. FIG. 83 shows the test setup. FIGS.
84 and 85 show the NIR spectra of the supernatant with settling
time. It was clearly observed the spectra change with time, for
example: (i) water peak in 5000-5600 nm from high to disappeared;
(ii) the orientation of the tail of NIR spectra about 4500 nm
changed from downward to upward; (iii) NIR spectra shifts from top
to bottom when water reduces with time; and (iv) when water content
<400 ppm in dilbit (reach soluble water level in dilbit), all
NIR spectra group at the bottom. FIG. 86 shows water content in the
supernatant with time based on laboratory results. According to
laboratory results and the shape and location of NIR spectra, one
can interpret that the type of water in the supernatant changes
from free-water and emulsified-water toward only soluble water left
in the dilbit.
TABLE-US-00010 TABLE 10 Summary NIR Technology Application in PFT
Process NIR Probe Tested Application Reflectance Transmission
Findings FSU Overflow Yes, stable Yes, spectra Reflectance probe
was implemented in spectra with noise the plant. NIR can be used
for the recorded background measurement of S/B, composition of
bitumen, solvent, asphaltenes, density and flux of FSU. NIR can
extend to detect water, solids and potentially water chemistry
analysis. NIR can detect zone settling behavior. FSU Vessel Yes Yes
NIR can detect zone settling behavior in the FSU. NIR can be used
for monitoring the interface between diluted bitumen and aqueous
phase with asphaltene agglomerates and for the measurement of S/B,
composition of bitumen, solvent, asphaltenes, water and solids and
density. Water solubility in Yes No NIR can detect soluble water in
pentane pentane. Due to the limitation, only the reflectance probe
was tested, but based on the principal of NIR technology,
transmission NIR will give even better detection. SRU Pentane Yes
Yes Stable spectra were collected; Feed in Diesel Dependent on the
concentration of Diluted Yes Was not pentane or diluted bitumen in
diesel, bitumen in tested; it can different NIR models should be
used to Diesel detect the get right analysis, for reflectance
probe: composition of .ltoreq.1% of pentane or diluted bitumen,
these streams. SD < .+-. 0.08%; 0-100% of pentane or diluted
bitumen, SD < .+-. 2.3%. Bitumen Yes Stable spectra were
collected for < 15% in Diesel of bitumen in diesel; Dependent on
the concentration of bitumen in diesel, different NIR models should
be used to get right analysis: .ltoreq.1% of bitumen, SD < .+-.
0.02%; <15% % of bitumen, SD < .+-. 1%. No stable spectra
recorded when bitumen is > 15% in diesel (suspected bitumen ppt.
in diesel)
TABLE-US-00011 TABLE 11 NIR Application on Residual Solvent
Analysis NIR Probe Tested Sample Reflectance Transmission Findings
0-1000 ppm Yes Yes Both probes recorded stable spectra; pentane in
diesel Transmission spectra provided more reliable and accuracy
analysis when pentane is <0.1%; Transmission probe can directly
measure <1000 ppm pentane in diesel, SD is <70 ppm; Detailed
results were included in this ppt. 0-1000 ppm Not tested Yes
Transmission probe was selected for pentane in 10% this
measurement; bitumen toluene Transmission probe can directly
solution measure; <1000 ppm pentane in diluted bitumen, SD is
~15 ppm; Detailed results were included in this ppt. 0-1000 ppm Yes
Transmission probe can directly measure pentane in 20% <1000 ppm
pentane in diluted bitumen, SD bitumen diesel is ~11 ppm. solution
0-1000 ppm Yes Transmission probe can directly measure pentane in
bitumen <1000 ppm pentane in diluted bitumen, SD is ~190 ppm.
High SD is attributed to the challenge to make the homogenized
sample.
Experimentation 5: RI Measurement versus NIR Measurement [0301]
Verify RI Data vs. Lab Data
[0302] Comparison of values of S/B generated by NIR measurement and
refractive index (RI) measurement was done by first verifying the
reliability of RI measurement data using the lab data. It is
important to note that the RI also identifies outliers as bad data
points and excludes them. The major sections of data excluded by
the NIR as shown in Table 7, match the major section of data
excluded by the RI. The relationship between the Lab and RI data is
established in FIGS. 50, and 51 for density, and S/B respectively.
It can clearly be seen that the data form into 3 clusters, for Week
A, B and C. The Week A cluster is isolated and does not have any
clear relation to the other weeks potentially due to the readings
being insensitive during that test period.
[0303] For the next examination, Week A data were removed, as it
was degrading the overall value of the correlation. This is shown
in FIGS. 52, and 53 for density, and S/B respectively. The
correlation significantly improved for all components, and trended
in the expected direction. It was concluded that there was a
discrepancy in the Week A data, and the Week B and C correlations
would be used to compare with the NIR values. The below figures
show that there is a correlation between RI and density/S/B (2).
[0304] Comparison of NIR Prediction and RI Prediction
[0305] Due to the reasonable calibration between laboratory data
and RI data for Week B and C, the scale of data was increased to
include the entire pilot range, except for Week A. This is shown in
FIGS. 54, and 55 for density, and S/B respectively. The results
showed that there was no clear correlation between NIR prediction
and RI measurement over the pilot range for Week B and C. It can
easily be identified which values are the obvious outliers
(circled). However, the RI does not reject this data as bad in its
initial internal screening. The ranges show no conclusive trend and
a high magnitude of fluctuation. This is due to the RI being
irresponsive at lower flow rates (2). This further shows that there
is a large degree of inconsistency with the RI measurement and a
controlled study on the relationship between RI and density should
be completed in order to account for these inconsistencies.
[0306] The values for NIR, RI, and laboratory data are compared
along the same time domain in FIGS. 56 and 57 below for Weeks B and
C and show that the NIR prediction matched laboratory data
reasonably throughout the whole pilot. Conversely, there was a
large amount of scattered RI measurement points observed in the
range. It should be noted that the values circled represent the
period where the pilot was run in two stages with chemical
injection. NIR still provided reasonable prediction although both
density and S/B models were developed based on lab data collected
at a single stage operational condition.
* * * * *