U.S. patent application number 13/787168 was filed with the patent office on 2013-09-26 for control asset comparative performance analysis system and methodology.
The applicant listed for this patent is Russell F. BROWN, John P. HAVENER, William HORNER, Richard B. JONES, Gregory D. MARTIN. Invention is credited to Russell F. BROWN, John P. HAVENER, William HORNER, Richard B. JONES, Gregory D. MARTIN.
Application Number | 20130253685 13/787168 |
Document ID | / |
Family ID | 40408801 |
Filed Date | 2013-09-26 |
United States Patent
Application |
20130253685 |
Kind Code |
A1 |
HAVENER; John P. ; et
al. |
September 26, 2013 |
CONTROL ASSET COMPARATIVE PERFORMANCE ANALYSIS SYSTEM AND
METHODOLOGY
Abstract
A system and method is provided for determining the variability
induced on a process output. The method includes the analysis of
input variable values to determine the total variability. A series
of processes may be analyzed and ranked so that a process owner may
gain an understanding of how a target process performs relative to
the processes of other process owners. The method includes the
generation of graphical process comparisons and advice regarding
asset performance. The method also includes the estimation of cost
impacts due to changes in induced variability.
Inventors: |
HAVENER; John P.; (Dallas,
TX) ; MARTIN; Gregory D.; (Dallas, TX) ;
BROWN; Russell F.; (Dallas, TX) ; HORNER;
William; (Dallas, TX) ; JONES; Richard B.;
(Dallas, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
HAVENER; John P.
MARTIN; Gregory D.
BROWN; Russell F.
HORNER; William
JONES; Richard B. |
Dallas
Dallas
Dallas
Dallas
Dallas |
TX
TX
TX
TX
TX |
US
US
US
US
US |
|
|
Family ID: |
40408801 |
Appl. No.: |
13/787168 |
Filed: |
March 6, 2013 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
13198988 |
Aug 5, 2011 |
|
|
|
13787168 |
|
|
|
|
Current U.S.
Class: |
700/111 |
Current CPC
Class: |
G06Q 10/0633 20130101;
Y02P 90/82 20151101; G06Q 10/06 20130101; Y02P 90/84 20151101; G06Q
10/06393 20130101 |
Class at
Publication: |
700/111 |
International
Class: |
G06Q 10/06 20120101
G06Q010/06 |
Claims
1. A computer-implemented method for generating advice to improve
the economic value of an industry target process unit having a
control application comprising the steps of: selecting a set of
same industry process datasets comprising of process unit input and
output variable values of units from the same industry as the
target unit; selecting a set of target process datasets comprising
input and output variable values for the target unit; determining
the overall induced variability gaps for the set of same industry
datasets and the set of target process datasets; determining the
overall output variability gaps for the set of same industry
datasets and the set of target process datasets; determining the
overall variability reduction ratio gaps for the set of same
industry datasets and the set of target process datasets; rank
ordering the same industry process units based on at least one
overall variability category, wherein the categories include
overall induced variability gaps, overall output variability gaps,
and overall variability reduction ratio gaps; and generating advice
to improve the economic value of the target process based on
comparing at least one target unit overall variability category to
the same industry overall variability category.
2. A computer-implemented method for generating advice to improve
the economic value of an industry target process unit having one or
more control applications, utilizing a variability graph comprising
the steps of: selecting sets of same industry process units;
separating the process units into at least one category based on at
least one overall variability, wherein the categories comprise:
quartiles based on overall induced variability, and quartiles based
on overall output variability; constructing a graph of the process
units with at least one category displayed, wherein the graph
comprises: lines dividing the processes into quartiles by overall
induced variability gaps, lines dividing the processes into
quartiles by overall output variability gaps, and radial lines
extending from the origin dividing the processes into quartiles by
overall variability reduction ratio gaps; displaying overall
induced variability and overall output variability of the target
process on the variability graph; and generating advice based on
comparing the target unit's position on the graph.
3. The computer-implemented method of claim 2, wherein: if the
target process's overall output variability needs improvement but
the target unit's overall variability reduction ratio gap is
acceptable, the generated advice comprises reducing the target
unit's overall induced variability; and if the target process's
overall output variability needs improvement but the target unit's
overall induced variability gap is acceptable, the generated advice
comprises improving the target unit's overall variability reduction
ratio gap.
4. The computer-implemented method of claim 3, wherein the target
unit is a crude unit, wherein reducing the target unit's overall
induced variability gap is accomplished by improving the
variability of the feed rate, feed temperature, and feed API to the
target unit.
5. The computer-implemented method of claim 3, wherein reducing the
target unit's overall variability reduction ratio gap is
accomplished by improving the target unit's control
applications.
6. A system for generating advice to improve the economic value of
an industry target process unit having a control application
comprising: a server, comprising: a processor, and a storage
subsystem; a database stored by the storage subsystem comprising:
same industry process datasets comprising process unit input and
output variable values of unites from the same industry as the
target unit; and operational performance datasets for the target
unit comprising input and output variable values; a computer
program stored by the storage subsystem, when executed causing the
processor to: select a set of same industry process datasets;
select a set of target process datasets; determine overall induced
variability gaps for the set of same industry datasets and the set
of target process datasets; determine the overall output
variability gaps for the set of same industry datasets and the set
of target process datasets; determine the overall variability
reduction ratio gaps for the set of same industry datasets and the
set of target process datasets; rank order the same industry
process units based on at least one overall variability category,
wherein the categories include overall induced variability gaps,
overall output variability gaps, and overall variability reduction
ratio gaps; and generate advice to improve the economic value of
the target process based on comparing at least one target unit
overall variability category to the same industry overall
variability category.
7. A system for generating advice to improve the economic value of
an industry target process unit having one or more control
applications, utilizing a variability graph comprising: a server,
comprising: a processor, and a storage subsystem; a database stored
by the storage subsystem comprising: same industry process
datasets; and operational performance datasets for the target unit;
a computer program stored by the storage subsystem, when executed
causing the processor to: select a plurality of same industry
process unit datasets; organize the process units into at least one
category based on at least one overall variability, wherein the
categories comprise: quartiles based on overall induced
variability, and quartiles based on overall output variability;
construct a graph of the process units with at least one category
displayed, wherein the graph comprises: lines dividing the
processes into quartiles by overall induced variability gaps, lines
dividing the processes into quartiles by overall output variability
gaps, and radial lines extending from the origin dividing the
processes into quartiles by overall variability reduction ratio
gaps; display overall induced variability and overall output
variability of the target process on the variability graph; and
generate advice based on comparing the target unit's position on
the graph.
8. The system of claim 7, wherein: if the target process's overall
output variability needs improvement but the target unit's overall
variability reduction ratio gap is acceptable, the generated advice
comprises reducing the target unit's overall induced variability;
and if the target process's overall output variability needs
improvement but the target unit's overall induced variability gap
is acceptable, the generated advice comprises improving the target
unit's overall variability reduction ratio gap.
9. The system of claim 8, wherein the target unit is a crude unit,
wherein reducing the target unit's overall induced variability gap
is accomplished by improving the variability of the feed rate, feed
temperature, and feed API to the target unit.
10. The system of claim 8, wherein reducing the target unit's
overall variability reduction ratio gap is accomplished by
improving the target unit's control applications.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application Ser. No. 60/969,102, filed Aug. 30, 2007, which is
incorporated by reference in its entirety.
[0002] This application is a division of U.S. Non-Provisional
application Ser. No. 13/195,988, filed Aug. 2, 2011, which is
incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION
[0003] 1. Field of the Invention
[0004] The invention relates to a system and method for measurement
and comparative performance analysis of assets for production
facilities.
[0005] 2. Background Summary
[0006] Manufacturers make large investments in assets, e.g.
personnel, instruments and equipment, field wiring, operator
interfaces, automation systems, computers and software
applications, to maximize profits and to improve safe operations,
the benefits of which include better control of production rates,
higher quality products manufactured by their production facilities
with lower production risks and improved safety.
BRIEF SUMMARY OF THE INVENTION
[0007] A system and method for measurement and comparative
performance analysis for production environments is provided. In a
manufacturing plant, control assets can have varying degrees of
success in control performance depending on several factors
including but not limited to: the mechanical integrity of the
process equipment being controlled, the selection of the control
assets employed, the mechanical integrity of the assets, the
accuracy and reliability of the data provided by the instruments,
the design and control strategies used, the capabilities of the
software used to express the control strategies, the skills of the
people responsible for maintaining the assets, the tuning of the
adjustable parameters in the software, and the tuning of the
adjustable hardware setting of the final control element
instruments. The manufacturing facility's production capacity,
quality and yield are affected by the varying performance of the
control assets.
[0008] Previously, companies have experienced a long felt and unmet
need to evaluate the effectiveness of assets and to compare the
performance of the assets to those employed by their competition in
order to identify their competitive position, to evaluate
opportunities to maximize their current investments, and to
evaluate the opportunity to improve their competitive position by
making new asset investments.
[0009] Overall product quality, production rates, efficiencies, and
yields produced are not solely dependent on the control assets
performance. In the context of a manufacturing facility, the
quality of the raw materials used to produce products will also
impact final product measurements. In addition, the consistency and
smoothness of the operation of the facility has a direct impact on
the amount of variation that is imposed on the manufacturing
process. These process variations can result in lower production,
lower product quality, and lower efficiency unless eliminated by
the control assets.
[0010] Control assets are not capable of eliminating an infinite
amount of process variation imposed by the variability of the
process, but can reduce the negative impacts to the greatest extent
possible. Previously, there was no systematic and universally
comparable method (1) to assess control asset performance by way of
measuring the effectiveness of the reduction of variation achieved
by the control assets, (2) to separate the financial gains that
could be achieved by improving the process variation, or (3) to
determine the effectiveness to which the variation can be rejected
by the control assets.
[0011] The separation of the process variation impacts from the
control assets capability to reduce the impacts has important
implications on the costs to improve performance. Process variation
can often be reduced by low or no cost changes in operating
practices and procedures, which serve to reduce the process
variation if the impact of these variations can be measured and
evaluated.
[0012] Low or no capital cost improvements in control performance
can also be achieved by tuning existing control assets. Controls
are often "de-tuned" to move less aggressively, in order to satisfy
personnel's desire for slow and understandable changes. This
de-tuning serves to improve acceptance of the closed loop operation
of the control application mechanism, system, or device. In a
manufacturing plant, operators are often empowered to put the
controller in "open loop," or otherwise defeat the action of the
controller, if they are uncomfortable with the aggressiveness or
efficacy of the controller's actions. De-tuning typically results
in lower performance and higher process variability. The degree to
which operators accept aggressive tuning is individualistic. Thus,
controls often have the capability to reduce variability if more
aggressively tuned. In accordance with the present invention,
improved tuning of the existing control assets can be achieved if
the impact can be measured and effectively communicated.
[0013] Alternately, new, or upgrades to, control assets could be
employed to increase performance, resulting in increased capital
costs. Without separation of the process variation impact from the
control performance impact, expensive investments might be made in
control assets which might not result in the improvements targeted.
For example, a new control application costing over one million US
dollars might be installed to reduce variation when simple actions
to reduce process variation and tune existing controls may have
been just as effective at little or no capital cost.
[0014] Expensive new control application mechanisms, systems, and
devices can also fail due to unrealistic expectations of the amount
of variation reduction, resulting in disappointment and potential
failure. If realistic expectations can be set initially, then a
reasonable combination of operational changes and control
application mechanisms, systems, or devices can be designed with
realistic expectations for improvements. In accordance with the
present invention, by comparing the degree of variation reduction
targeted by the proposed new controls to the degree of variation
reduction achieved by the leaders in the industry, a realistic
expectation of improvement can be set. This can only be
accomplished if the variation reduction due to controls can be
separated from the degree of variation imposed by the inputs to the
process.
[0015] Similarly, new control application mechanisms, systems, or
devices have been installed because management felt that advanced
controls must surely be required for the type of process being
controlled. Management, in the absence of the objective
measurements of the variability levels and reduction that is
achievable through the use of the method and system according to
the present invention, often thinks in terms of an "automation
gap." The shorthand for this automation could be described as
follows: "The competition has control assets employed that we do
not, therefore we need them, too." When the expensive control
investment is installed, management is disappointed to find that
little improvement is achieved. Within a short time the control
asset is abandoned, and the project is considered a failure. If an
objective measurement of the process variability were available
initially, management would have learned that the present product
variation compares well with the competition despite having only
simple controls. In a manufacturing plant, use of the present
invention would have revealed that this is because the raw
materials, operating practices, and process variation is small,
resulting in little variation to be rejected, and therefore no need
for expensive advanced controls.
[0016] The converse can also occur, where management has had little
success with control applications, and as a result they fail to
make critical control asset investments. The competition can gain a
significant advantage in this case.
[0017] The separate identification, comparison, and assessment of
economic opportunity allows for reduction of variation in
performance. The following description is given in the context of
the oil refining industry. However, the method and system are
universally applicable with easy extension of the metrics and
methodology into any production environment, including but not
limited to: power generation and transmission; pharmaceutical
manufacturing; food and beverage manufacturing; the pulp and paper
industry; petrochemical manufacturing; organic and inorganic
chemical manufacturing; the polymers and plastics industry; the
operation of industrial, power and marine boilers; automotive
manufacturing; internal combustion engine control; medical
equipment manufacturing; metals and mining industry; packaging;
mail and package processing; construction; project development; and
transportation; as well as, a host of other industry and business
applications.
[0018] According to one (or an) embodiment, a system and method is
disclosed for comparative operational and process control
performance analysis of industrial process units using unique
algorithms, graphical presentation methods and economic gap
calculations all based on reduction of process variability. While
the process and manufacturing facility in several embodiments
pertain to the hydrocarbon and chemical process industries, the
present invention applies to control assets generally and include
but are not limited to sales, marketing, transportation, project
development, and construction applications as well.
[0019] Embodiments of a method relate to the various refining
process unit types, including, but not limited to, crude
distillation, vacuum distillation, catalytic reforming, catalytic
cracking, hydrocracking, hydrotreating, and delayed coking Direct
extensions of the method in refining alone include: visbreaking,
thermal cracking, hydrogen generation, hydrogen purification, MTBE
production, Alkylation, Isomerization, desulfurization, sulfur
recovery, tail gas recovery, sulfuric acid generation, asphalt and
bitumen production, coke calcinators, desalination, CO.sub.2
liquification, cumene, cyclohexane, hydrodealkylation, toluene,
xylene, paraxylene, ethybenzene, deisopenanizer, deisohexanizer,
dehaptanizer, alkyate/reformate splitter, solvent deasphalting,
aromatic solvent extraction, extractive distillation, calicination,
and propane/propylene splitting among other refining processes.
[0020] One embodiment is a computer-implemented method for
determining the amount of induced variability of variables in a
process comprising the steps of: collecting a plurality of datasets
of input variable values and output variable values; calculating
standard deviations for each of the datasets of input variable
values and output variable values; and determining induced
variability of each of the datasets of output variable values.
[0021] Another embodiment is a computer-implemented method of
automating the presentation of advice on process control asset
performance comprising the steps of: collecting a plurality of
datasets of input variable values and output variable values;
calculating standard deviations for each of the datasets of input
variable values and output variable values; calculating induced
variability of each of the datasets of output variable values;
calculating output variability of each of the datasets of output
variable values; calculating a reduction in variability for at
least two processes; and generating advice based on the calculated
induced variability, calculated output variability, and reduction
in variability of a target process.
[0022] Another embodiment is a computer-implemented method of
automating the presentation of advice on control asset performance
comprising the steps of: selecting a set of input variables;
selecting a set of output variables, wherein the variability of the
selected output variable values is affected by the variability of
the selected input variable values; collecting a plurality of
datasets of input variable values and output variable values for
the input variables and the output variables; processing the input
variable values and the output variable values to remove outliers;
wherein the processing comprises: removing data errors; calculating
standard deviations for each of the processed datasets of input
variable values and output variable values; estimating combined
variability of each of the processed datasets of input variable
values; calculating induced variability of each of the processed
datasets of output variable values; calculating output variability
of each of the processed datasets of output variable values;
calculating variability ratio for each of the processed datasets of
output variable values; calculating the overall induced variability
for at least four processes; calculating the overall output
variability for at least four processes; calculating the overall
reduction in variability for at least four processes; rank ordering
the processes by overall induced variability and overall output
variability; separating the processes into at least one category
based on at least one overall variability, wherein the categories
comprise: quartiles based on overall induced variability, and
quartiles based on overall output variability; constructing a graph
of the processes units with at least one category displayed,
wherein the graph comprises: lines dividing the processes into
quartiles by overall induced variability, lines dividing the
processes into quartiles by overall output variability, and radial
lines extending from the origin dividing the processes into
quartiles by overall reduction in variability; displaying the
overall induced variability and overall output variability of a
target process on the graph; and generating advice based on the
category of the target process.
[0023] Yet another embodiment is a system comprising: a server,
comprising: a processor, and a storage subsystem; a database stored
by the storage subsystem comprising: input and output data; a
computer program stored by the storage subsystem, when executed
causing the processor to: collect a plurality of datasets of input
variable values and output variable values; calculate standard
deviations for each of the datasets of input variable values and
output variable values; and determine induced variability of each
of the datasets of output variable values.
[0024] Another embodiment is a system comprising: a server,
comprising: a processor, and a storage subsystem; a database stored
by the storage subsystem comprising: input and output data; a
computer program stored by the storage subsystem, when executed
causing the processor to: collect a plurality of datasets of input
variable values and output variable values; calculate standard
deviations for each of the datasets of input variable values and
output variable values; calculate induced variability of each of
the datasets of output variable values; calculate output
variability of each of the datasets of output variable values;
calculate a reduction in variability for at least two processes;
and generate advice based on the calculated induced variability,
calculated output variability, and reduction in variability of a
target process.
[0025] Another embodiment is a system comprising: a server,
comprising: a processor, and a storage subsystem; a database stored
by the storage subsystem comprising: input and output data; a
computer program stored by the storage subsystem, when executed
causing the processor to: select a set of input variables; select a
set of output variables, wherein the variability of the selected
output variable values is affected by the variability of the
selected input variable values; collect a plurality of datasets of
input variable values and output variable values for the input
variables and the output variables; process the input variable
values and the output variable values to remove outliers, wherein
the processing comprises: removing data errors; calculate standard
deviations for each of the processed datasets of input variable
values and output variable values; estimate the combined
variability of each of the processed datasets of input variable
values using the calculated standard deviations; calculate the
induced variability of each of the processed datasets of output
variable values using the calculated standard deviations; calculate
the output variability of each of the processed datasets of output
variable values using the calculated standard deviations; calculate
the variability ratio for each of the processed datasets of output
variable values using the induced and output variabilities;
calculate the overall induced variability for at least four
processes using the induced variability of the processed datasets;
calculate the overall output variability for at least four
processes using the output variability of the processed datasets;
calculate the overall reduction in variability for at least four
processes using the induced and output variabilities; rank order
the processes by overall induced variability and overall output
variability; separate the processes into at least one category
based on at least one overall variability, wherein the categories
comprise: quartiles based on overall induced variability, and
quartiles based on overall output variability; constructing a graph
of the processes with at least one category displayed, wherein the
graph comprises: lines dividing the processes into quartiles by
overall induced variability, lines dividing the processes into
quartiles by overall output variability, and radial lines extending
from the origin dividing the processes into quartiles by overall
reduction in variability; display the overall induced variability
and overall output variability of a target process on the graph;
and generate advice based on the category of the target
process.
[0026] Another embodiment is a computer-implemented method for
estimating energy savings for a process comprising the steps of:
collecting a plurality of datasets of input variable values;
calculating the standard deviations for each of the processed
datasets of the input variable values; collecting a set of standard
deviation benchmarks corresponding to at least one input variable;
calculating a difference between the standard deviation of at least
one input and at least one corresponding standard deviation
benchmark; and estimating the savings related to the
difference.
[0027] Another embodiment is a system comprising: a server,
comprising: a processor, and a storage subsystem; a database stored
by the storage subsystem comprising: input and output data; a
computer program stored by the storage subsystem, when executed
causing the processor to: collect a plurality of datasets of input
variable values; calculate the standard deviations for each of the
processed datasets of the input variable values; collect a set of
standard deviation benchmarks corresponding to at least one input
variable; calculate the difference between the standard deviation
of at least one input and at least one corresponding standard
deviation benchmark; and estimate the savings related to the
difference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0028] These and further features will be apparent with reference
to the following description and drawings, wherein:
[0029] FIG. 1 is a flow chart illustrating one embodiment of a
method to calculate production unit variability metrics.
[0030] FIG. 2 is a diagram of one embodiment of an induced
variability gain magnitude matrix construction for a crude unit.
Similar induced variability gain magnitude matrix constructions
have been reduced to practice for all major refinery process units.
Similar constructions are contemplated for all major process units
in all continuous and discontinuous process operations.
[0031] FIG. 3 is a flow chart of an embodiment of a method to
calculate the gain magnitude values in any induced variability gain
matrix.
[0032] FIG. 4 is a diagram illustrating an embodiment of a method
to obtain initial estimates of variability gains by examination of
the boiling point curves of various refinery crude feeds.
[0033] FIG. 5 is a diagram showing an embodiment of a method of
analysis of process improvements by use of a variability
metrics.
[0034] FIG. 6 is a diagram illustrating exemplary economic yield
benefits that can be estimated through the use of the novel metrics
of the disclosed embodiments.
[0035] FIG. 7 is a diagram illustrating exemplary economic energy
benefits that can be estimated through the use of the novel metrics
of the disclosed embodiments.
[0036] FIG. 8 is a diagram illustrating an embodiment for a crude
unit of the unique Variability Graph which utilizes the novel
metrics of the disclosed embodiments to easily visualize and
diagnose the overall performance of crude units. Similar
Variability Graphs have been reduced to practice for all major
refinery process units. Similar constructions are contemplated for
all major process units in all continuous and discontinuous process
operations.
[0037] FIG. 9 is a diagram illustrating an embodiment of a system,
which includes the hardware and software engines that implement the
embodiment.
[0038] FIG. 10 is a diagram illustrating a vector representing
total variability on the Variability Graph.
BRIEF DESCRIPTION OF THE TABLES
[0039] These and further features will be apparent with reference
to the following description and tables, wherein:
[0040] TABLE 100 shows exemplary industry process input data
parameters collected to support creation of the novel metrics of
the disclosed embodiments.
[0041] TABLE 200 shows exemplary industry process output data
parameters collected to support creation of the novel metrics of
the disclosed embodiments.
[0042] TABLE 300 shows exemplary industry process data observation
datasets of inputs and outputs output data collected to support
creation of the novel metrics of the disclosed embodiments.
[0043] TABLE 400 shows exemplary induced variability gain magnitude
matrix inputs and outputs for various refinery process unit
types.
[0044] TABLE 500 shows exemplary automated advice that can be
delivered based on the unified overall metric Vo-Vi-Vrr.
DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION
[0045] Unit is broadly defined as a distinct entity within a larger
group, such as operating entities within a facility or business
setting. Examples of units include electric power generators,
chemical reactor vessels, pharmaceutical production lines, and
package delivery systems.
[0046] One embodiment of the method, shown in FIG. 1, involves the
use of a database that contains unit level process operating data
for generating a comparison. The process parameters for which data
is collected are identified in Table 100 and Table 200.
[0047] In step 100, historical process data are extracted for the
target unit as defined in Table 100 and Table 200. The data are
gathered for a multiplicity of data set observations of real-time
uncompressed operational data from the target process (three or
more data sets are preferred, but only one is needed). In a
preferred embodiment, a minimum of three data sets are collected,
each covering time frames defined in Table 300. The time frames for
data collection can vary from those shown in Table 300. Data
quantity can be as low as one single complete set of inputs and
outputs.
[0048] A multiplicity of data sets is collected during "Normal
State" operations, defined as a period of time in which the unit is
operating normally without large process disturbances. One
embodiment uses three data sets when the data are manually
collected. For applications in which the data are collected
automatically, any number of observations can be collected up to
and including continuous data collection.
[0049] For crude and vacuum refining units, a second multiplicity
of data sets is collected during crude switch operations, defined
as that period of time in which the crude oil charge is being
changed from one crude source to another, accompanied by a change
in density and composition measured in API (a standard measurement
of crude density) or specific gravity. These data are handled in a
separate metric for crude switch performance. Note that crude
switch observations can be collected for other highly affected
units in a refinery, such as gas processing plants, desalters, etc.
Crude and vacuum refining units have been reduced to practice.
[0050] For delayed coking units, a second multiplicity of data sets
is collected during drum switch operations, defined as that period
of time in which the coking drum, which feeds the main fractionator
is being switched from one drum to another. These data are handled
in a separate metric for drum switch performance.
[0051] In step 200, the data are examined and preprocessed to
assure the input information is valid. This step includes analysis
of the values to assure the values are reasonable, the values are
of the right order of magnitude, and the raw process data do not
contain instrumentation or data recording abnormalities such as
"spikes." Spikes are events in which the data for one observation
show an inordinately large or small reading and immediately return
to a reasonable range. If the abnormality indicates a change that
is physically impossible for an actual operating unit to have
actually experienced, then the spike data reading is eliminated
from the dataset. If the data values in general are not reasonable
values, then the operating unit which supplied the data is
contacted to assure that the correct process parameters were used.
Preprocessing can be done by automated checks, or can be done
manually. In either case, an individual with industry experience is
generally used to assure the reasonableness of the data either by
personal review of the data, or use of automated logic created by
the individual with industry experience.
[0052] Not all inputs are measured by the industry. Some inputs
might be derived or inferred from data that is normally recorded.
These readings are called inferred inputs. In step 300, inferred
input values are calculated. Some of these inferred values are
industry standard calculations such as liquid hourly space velocity
(LHSV) (calculated from reactor dimensions and process flow rates)
and catalyst loadings (the density of the catalyst loaded into the
reaction vessel as collected from the unit log data from the
operations personnel). Other parameters such as API could be
measured online but typically are not measured. Another embodiment
is used to infer API, which is described below.
[0053] In step 400, pseudo set points of the input and output data
observations are established and added to the data set. Typically
the industry does not maintain a long term record of set points
used. Operating units typically record the actual process values,
but not the set points. For industries that maintain a history of
the set points, set points are preferred for use. However, if the
set points are not recorded then they are estimated. There are
several methods that can be used to estimate set points. A few of
those are given below: [0054] 1. Use actual recorded set points if
they exist. [0055] 2. Use controller statistics that are recorded
by the control application or external software applications.
[0056] 3. Use the average value of the data during the observation
period as the estimate of the set point. This typically introduces
only small errors in that set points should not be changed minute
to minute. The observations are collected during "normal state" in
which few major process changes are being introduced. [0057] 4. Use
the running average of the data as the set point. This poses some
problems for the dynamics. [0058] 5. Use a running average to
detect set point changes, and then divide the observation into time
segments with different set points. For each segment use the
average of all data in that segment as the set point estimate.
[0059] 6. Visually scan the data and assign set points manually.
This list of methods is illustrative and exemplary only. To assure
equal treatment among all participants, since few have the set
point information, method 3 above is preferred. However, when set
point information is more common in a field or process, method 1
above would be preferred.
[0060] In step 500, the standard deviations of the input and output
data deviation from the pseudo set points for each variable in each
observation data set are calculated.
[0061] In step 600, an estimate of the combined variability across
the multiplicity of observations is calculated. This is done by
combining the standard deviations from the multiplicity of
observations into one estimate of input and output standard
deviations to yield .sigma.X.sub.(k) and Vo(i). This combination
may be accomplished by several methods. The methods below are
illustrative and exemplary only.
1.
.sigma.X.sub.k=Sqrt(((.sigma.X.sub.k1).sup.2+(.sigma.X.sub.k2).sup.2+
. . . +(.sigma.X.sub.kn).sup.2))/n) (I-1)
[0062] Where .sigma.X.sub.k=the standard deviation of input
X.sub.k.
[0063] .sigma.X.sub.kn=the standard deviation of input X.sub.k for
observation period n. [0064] n=the total number of observation
periods 1 . . . n [0065] Note that when comparing similar unit
operations, equation I-1 is preferred. For comparing operations
that are dissimilar and might have values of X that are an order of
magnitude different between various operating units in the
population, then the coefficient of variation of the input
parameter .sigma.X.sub.k: [.sigma.X.sub.k/Average Xk] should be
used.
[0065] 2.
Vo(i)=Sqrt((.sigma.Y.sub.k1).sup.2+(.sigma.Y.sub.k2).sup.2+ . . .
+(.sigma.Y.sub.kn).sup.2)/n) (I-2)
[0066] Where Vo(i)=the "Output variability".dbd.Standard deviation
of output variable Yi. Which equals .sigma.Y.sub.i.
[0067] .sigma.Y.sub.in=the standard deviation of output Y.sub.i for
observation period n. [0068] Note that when comparing similar unit
operations, equations I-3 is preferred. For comparing operations
that are dissimilar and might have values of Y that are an order of
magnitude different, then the coefficient of variation of the
output parameter Y.sub.i: [.sigma.Y.sub.i/Average Y.sub.i] should
be used.
[0069] In step 700, the Induced Variability Vi(i) of each Output
Variable i is calculated. This is done using a novel Gain Matrix
which estimates the variability of product measurements from the
standard deviation of the input variables .sigma.X.sub.k. An
example gain matrix for a crude unit is given in FIG. 2. A unique
gain matrix can be developed for each unit type. Only the crude
unit gains are given as an example. An example of the gains used in
the gain matrix for the example crude unit in FIG. 2 is given in
Table 400.
The methods to develop gains according one embodiment are described
herein. The use of a gain magnitude matrix, which estimates product
variations from inducing parameter variations, is a new and novel
approach. It is also convenient that the gains used are very
similar to the gain values common in linear control applications,
where the magnitude is taken of each gain for the purpose of
estimating output variability, which is always non-negative. It is
important to note that, unlike gain matrix applications in practice
today for control applications (superposition of linear systems
which adds the gain-multiplied contributions), the individual
contributions from the gain magnitude calculations are not summed
directly. Instead, according an embodiment, the individual
contributions are squared and summed appropriately taking into
account any correlation that may exist between inputs. The square
root of the sum is then taken. This approach may be referred to as
"the weighted variance approach." Vi.sub.i is defined as the
induced variability standard deviation of product output "i" of
interest. It is an estimate of the amount of variability that is
being caused by the variability of selected inputs to the process
unit and:
Vi i = [ k = 1 n G o i - x k 2 * .sigma. X k 2 + 2 l = 1 n j < 1
n G o i - x l G o i - x j * .sigma. X l , X j ] 1 / 2 ( I - 5 )
##EQU00001##
[0070] where: [0071] G.sub.o.sub.i.sub.-x.sub.k=gain magnitude of
the unit product output i interest to the standard deviation of
input inducing parameter X.sub.k. [0072]
.sigma..sup.2.sub.x.sub.k=variance of input X.sub.k [0073]
.sigma..sub.X.sub.1.sub.,X.sub.j=covariance between inputs X.sub.1
and X.sub.j, [0074] If the inputs, X.sub.1 and X.sub.j, are
independent and subsequently uncorrelated observation, then:
[0074] Vi i = [ k = 1 n G o i - x k 2 * .sigma. X k 2 ] 1 / 2 ( I -
5 A ) ##EQU00002##
As an illustration, Vi could be an estimate of the amount of
variability that is induced upon an output product property of
interest by the variability of the key process inputs.
[0075] In step 800, the dimensionless Variability Ratio Vr(O) and
Variability Reduction Ratio Vrr(O) of each output variable of
interest is calculated.
Vr.sub.I=(Vo.sub.i/Vi.sub.i) (I-6)
Vrr.sub.i=1-Vr.sub.i (I-7)
[0076] Where Vr.sub.i=Variability Ratio of output product property
of interest i. [0077] Vrr.sub.i=Variability Reduction Ratio of
output product property of interest i. Note that Vr and Vrr are
dimensionless numbers as all units cancel out in the division.
Dimensionless numbers have special qualities for benchmarking as
dimensionless measurements of units of any capacity or size can be
directly compared. These two novel dimensionless parameters have
specific meanings Vr is the fraction of the induced variability
that remains in the product. Vrr is the fraction of the induced
variability that has been removed by the unit controls. The
preferred method is to use Vrr as higher values relate to better
control asset performance. However, all calculations can be
performed using Vr alone, since Vr introduces no artificial
constant and therefore retains its dimensionless nature throughout
the analysis. The constant can interfere with some uses of the
measure, however, Despite this limitation, Vrr is the preferred
metric for communication to management, since it does not require
the audience to think in reverse terms. The estimation of Vi and
Vrr allows the separate analysis and management of control action
from process induced variability on a stream-by-stream,
property-by-property basis regardless of the size of the units
being compared.
[0078] In step 900, the overall unit output variability performance
metric is calculated. Although the product stream by stream and
attribute by attribute metrics are very useful for diagnosis of
methods to improve unit operations, management has need of an
overall performance metric to help understand and compare the
overall unit performance to competition. This is accomplished with
the overall Vo metric and Vi metrics.
Vo=(Vo.sub.1*f.sub.1+Vo.sub.2*f.sub.2+ . . . +Vo.sub.1*f.sub.1)
(I-8)
Vi=(Vi.sub.1*f.sub.1+Vi.sub.2*f.sub.2+ . . . +Vi.sub.i*f.sub.i)
(I-9)
[0079] Where [0080] Vo=overall unit output product variability
achievement. [0081] Vi=overall unit calculated induced variability
[0082] Vo.sub.i=average standard deviation of the measured output
variability observations on stream i [0083] Vi.sub.i=average
standard deviation of the calculated output variability imposed by
process variation observations on stream i [0084] f.sub.i=fraction
of the product stream to the agglomerated total production of
interest. This can be mass fraction or volume fractions of the
total production of interest. The preferred embodiment is the
volume fraction as volumes are directly measured but mass requires
conversion of the measured values using an approximated density
that introduces errors. Vo is the main metric for comparing units
overall performance. Vi is the main metric to compare the amount of
variability induced by process operations. Another embodiment
incorporates the importance factors by product variable based on
economics or other criteria. This is a simple extension of the
weights used. Another embodiment uses the square root of the sum of
the squares approach combined with the weighted average as given in
the equations below:
[0084] Vo=(Vo.sub.1.sup.2*f.sub.1+Vo.sub.2.sup.2*f.sub.2+ . . .
+Vo.sub.1.sup.2*f.sub.1).sup.0.5 (I-8A)
Vi=(Vi.sub.1.sup.2*f.sub.1+Vi.sub.2.sup.2*f.sub.2+ . . .
+Vi.sub.i.sup.2*f.sub.i).sup.0.5 (I-9A)
Of course equation I-8A and I-9A honor the fact that the Vo and Vi
are standard deviations.
[0085] In step 1000, the overall unit variability ratio Vr and
variability reduction ratio Vrr are calculated. Although the
product stream by stream and attribute by attribute metrics are
very useful for diagnosis of methods to improve unit operations,
management has need of an overall control performance metric to
help understand and compare the overall unit control performance of
the process unit to competition. This is accomplished with the
overall Vr and Vrr metrics.
Vr=(Vr.sub.1*f.sub.1+Vr.sub.2*f.sub.2+ . . . +Vr.sub.i*f.sub.i)
(I-10)
Vrr=1-Vr (I-11) [0086] Where Vr=overall variability ratio, an
estimate of the fraction of induced variability that remains in the
product. [0087] Vrr=overall variability reduction ratio, an
estimate of the fraction of the induced variability that has been
removed from the product by the unit controls. [0088]
f.sub.i=fraction of the product stream i to the agglomerated total
production of interest. Vrr is the preferred embodiment of the main
metric for comparing units overall control performance. As stated
previously, Vr can alternately be used for the same purpose, but
must be understood to be the inverse of the efficacy of the
controls. Alternate embodiments include the incorporation of
importance factors by product parameter based on economics or other
criteria. An alternate embodiment of equation I-10 is to use the
square root of the sum of the squares approach given in the
equations below:
[0088] Vr=(Vr.sub.1.sup.2*Mf.sub.1+Vr.sub.2.sup.2*Mf.sub.2+ . . .
+Vr.sub.i.sup.2*Mf.sub.i).sup.0.5 (I-10A)
[0089] In FIG. 3, the process for development of the key induced
variability gains is described. The process involves gathering
multiple sources of information to establish the order of magnitude
of the gains, developing a trial gain set, and then tuning the
trial gain by testing the calculated Vi against industry data. A
final gain set is then established, which is used for all study
participants.
[0090] In step 2100, participation from a significant portion of
the target industry is sought to gather the operational data that
will be required to obtain the gains. Step 2200, which is
impractical in continuous, large production processes but may be
effective in discrete manufacturing, is the step of requesting that
industry obtain a training signal set of data for development of
the Vi gains directly. In step 2100, industry is asked to put all
present controllers in open loop and take no operator actions to
reject disturbances for a period of time to collect the data needed
to directly determine the actual gains between input disturbances
and output production. Various levels of deliberately introduced
input disturbance might also be required. The data collected from
such experiments creates a measured true collected Vi signal to
train a model against. This creates a solid training signal. Step
2200 would be very expensive for industry since it could produce
low quality production and might be unsafe to operate in the
requested manner. For these reasons, step 2200 is not the preferred
method for continuous, large production processes and may be
skipped in those circumstances.
[0091] When step 2200 is impractical, it must be realized that no
actual training signal exists to allow the Vi gains to be directly
calculated. Therefore, the Vi gains must be estimated or inferred.
This is done by gathering multiple sources of information from
which to construct an estimate of the order of magnitude of the
gains, and then testing the gains by calculating induced
variability and checking the reasonableness of the results.
[0092] In step 2300, a more reasonable approach is taken.
Participation from a significant portion of the target industry is
sought to gather normal operating data with the unit controllers in
action. For the refining industry units, these data are defined in
Table 300. The parameters to be captured are given in Table 100 and
Table 200. The gains to be developed between the Inputs in Table
100 and the Outputs in Table 200 for one embodiment are disclosed.
Participants are asked to gather the data and submit it for
assembling an industry wide testing data set. When a reasonable
result with one set of gains that produces reasonable results for
all participants in the industry training set is obtained, it will
be the gain set employed.
[0093] In step 2300, participating refineries' crude slates are
examined and a representative sampling of, for example, three to
five crudes are selected for development of initial gain
magnitudes. The initial gain magnitudes are calculated from
examination of the boiling point curves of the representative
crudes as shown in FIG. 4.
[0094] In step 2400, the literature is searched for reported gains
from the inputs to the outputs used in actual installed industry
controllers. These gains are most often obtained from step tests.
Since the induced variability gains should be very similar to the
controller process control gains, the magnitudes of these process
gains can be used as one estimate of the gain magnitudes for the
induced variability gains in this analysis.
[0095] In step 2500, personal expert experience of operators and
operations personnel is consulted to develop estimates of gain
magnitudes. In such interviews the expert may be asked questions
such as the following: "If you were to increase the crude feed rate
by 5,000 bpd and you did not increase the Naphtha draw rate, how
much do you think the Naphtha draw temperature would rise?" These
anecdotal responses are tabulated to determine the approximate
magnitude of the gain. The above question is an example only of the
process of interviewing the expert.
[0096] In step 2600, all of the various sources of gain magnitudes
from steps 2100 through 2500, including those from other sources
are examined to develop an initial starting trial set of gains for
testing against the representative industry process data.
[0097] In step 2700, the initial trial gains are tuned by
successive testing and modification against the entire data set of
collected representative industry process data created in step
2200. In this process, outlier results for the estimate of Vi, Vr,
and Vrr are examined to determine which input is most responsible
for the error. These are adjusted within the reasonable bounds of
the gains established in step 2600.
[0098] Once step 2700 has been repeated until the developer is
satisfied that the best possible gains has been established, then,
in step 2800, a single set of gains is established as the analysis
gain set, and this set is applied to all participating process
units. This is the preferred method to provide reasonable and
comparable results to all industry participants. An alternate
embodiment is to calculate a unique gain set for each and every
participating process unit, or unique gains for any selected subset
of process units.
Development of Inferred Values
[0099] Inferred values provide input values that are key concepts
that are not typically measured directly by instruments in the
industry, but can be calculated from measurement that are recorded.
These can be well established first principle concepts, laws of
physics, well established engineering design and analysis
parameters, or novel or new concepts or calculations that prove
useful in estimation of variability of the output products.
[0100] By way of example of inferred values, examine Table 100,
which identifies several inferred values. Table 100 serves as an
example only of the use of inferred values that will be applied in
other unit operations or other industries in addition to
refining.
[0101] For reformers and hydrotreaters, the well established
principle of reactor liquid hourly space velocity (LHSV) is an
inferred input. The calculation of LHSV is well established in the
industry and need not be explained here. It is calculated from
reactor dimensions and catalyst loading and reactor feed rates
which are measured and recorded.
[0102] For hydrocrackers and hydrotreaters, the Weighted Average
Bed Temperature (WABT) is an inferred input, and the calculation of
WABT is well established in the industry. Often WABT is recorded
directly from calculations done in the distributed control system
or reactor temperature controllers; however, if the WABT is not
directly available, then the WABT can be calculated from the
individual reactor bed temperatures that are recorded.
[0103] For Reformers, the Weighted Average Inlet Temperature (WAIT)
of the reactors is an inferred input, and the calculation is well
established in the industry. Often WAIT is recorded directly from
calculations done in the distributed control system or reactor
temperature controllers; however, if the WAIT is not directly
available, then the WAIT can be calculated from the individual
reactor inlet temperatures which are recorded.
[0104] For Crude and Vacuum Units, the API or density of the unit
feed can be measured on-line but seldom is measured on-line in
industry practice. The API is a rough measurement of the
composition of the unit feed, and therefore is an important input
affecting the product variation and therefore should be inferred if
not directly measured.
[0105] The basic concept for the invention of the API standard
deviation inferred value is to use the flow and temperature
readings of the column itself as data from a large on-line
analyzer. Each column side draw has a known product class, a
typical draw tray temperature under atmospheric column pressure,
and a known API range. As the volume fractions of these draws
change, and the tray temperatures change, there is an implied
change in the crude feed composition to the unit that was required
to produce these changes in distillation products.
[0106] There are three complications that make it impractical to
develop the standard deviation of API directly from the above
standard industry knowledge: 1) the overhead and base flows are not
considered, only the side draw flows are given, thus the mass
balance to the crude feed is incomplete; 2) we are predicting the
standard deviation of variation in API, not the API and covariance
can occur; and 3) the action of the side draw product controllers
is to manipulate the volume percent of the draws to maintain target
properties, and thus the controllers themselves contribute to the
variation.
[0107] These complications require then a empirical correlation
rather than a straight forward calculation based on first
principles knowledge. These correlations were developed by a
combination of first principles knowledge and regression against
industry data on scores of atmospheric and vacuum units. The
results have proved to be robust.
[0108] First we will describe the crude unit crude feed API
standard deviation inferred value, then describe the vacuum unit
atmospheric tower bottoms feed API standard deviation inferred
value. The crude unit feed API variation is inferred from the
standard deviations of the draw tray temperatures and flows of the
column side streams as given in the equations below.
.sigma.V.sub.API(1)=f.sub.(1)(3.312E-06.sigma.X.sup.2.sub.temp(1)+0.0664-
4.sigma.X.sub.temp(1))+(.sigma.X.sub.temp(1)*.sigma.X.sub.flow(1))
(II-1)
.sigma.V.sub.API=(.sigma.V.sub.API(1)+.sigma.V.sub.API(2),+ . . .
+.sigma.V.sub.API(1)) (II-2) [0109] Where .sigma.V.sub.API=The
inferred standard deviation of crude feed API. [0110]
.sigma.V.sub.API(1)=The inferred contribution to the standard
deviation of the crude feed due to the standard deviation of the
API of side stream (1) product. [0111] .sigma.X.sub.temp(1)=The
standard deviation of the draw tray temperature of side stream (1)
product. [0112] .sigma.X.sub.flow(1)=The standard deviation of the
draw flow of side stream (1) product. [0113] f.sub.(1)=The fraction
of side stream product (1) of the sum of all side stream products.
Note that the sum does not include overhead gas or atmospheric
tower bottoms flow.
[0114] The standard deviation of the API of the atmospheric tower
bottoms feed to a vacuum unit is inferred from the standard
deviations of the draw tray temperatures and flows of the vacuum
column side streams as given in the equation below.
.sigma.V.sub.API(1)=f.sub.(1)(0.00002.sigma.X.sup.2.sub.temp(1)+0.0427.s-
igma.X.sub.temp(1))+(.sigma.X.sub.temp(1)*.sigma.X.sub.flow(1)
(II-3)
V.sub.API=.SIGMA..sigma.V.sub.API(1) (II-4) [0115] Where
.sigma.V.sub.API=The inferred contribution to the standard
deviation of the crude feed due to the standard deviation of the
API of side stream (1) product. [0116] .sigma.V.sub.API(1)=The
inferred standard deviation of API of side stream (1) product.
[0117] .sigma.X.sub.temp(1)=The standard deviation of the draw tray
temperature of side stream (1) product. [0118]
.sigma.X.sub.flow(1)=The standard deviation of the draw flow of
side stream (1) product. [0119] f.sub.(1)=The fraction of side
stream product (1) of the sum of all side stream products. Note
that the sum does not include overhead gas or vacuum tower bottoms
flow.
[0120] Although the preceding examples are for specific inferred
inputs for specific units in refining, they are illustrative and
exemplary, and additional inferred calculations may be used for
input values.
Calculation of Performance Metrics and Gaps
[0121] The calculated standard deviations of all input, output, and
variables from step 500 in FIG. 1 are gathered from all industry
participating units. In addition, the overall performance
parameters, Vo, Vi, Vr, and Vrr are gathered, along with the
individual stream performance parameters Vo.sub.i, Vi.sub.i, and
Vrr.sub.i. All of these are arranged in ascending order and divided
into quartiles with Quartile 1 having the lowest variation and
therefore the best performance. The average of all values in
Quartile 1 is calculated as the base line for comparison. A
performance gap is calculated for each input and output variable as
the difference between the individual standard deviation and the
Quartile 1 average. The use of the Quartile 1 average is the
preferred embodiment, however the difference to the combined
Quartile 1 and 2 average (top half average) and the difference to
the study average (average of all values) can also be calculated
and reported. While quartiles are used in this embodiment, and are
common in some industries, the overall and individual performance
parameters may be separated into any number of divisions.
[0122] Individual Input Variability Metrics
Q1.sigma.X.sub.k=Average .sigma.X.sub.k of lowest 25% of collected
.sigma.X.sub.k (III-1)
Q2.sigma.X.sub.k=Average .sigma.X.sub.k of 2.sup.nd lowest 25% of
the collected .sigma.X.sub.k (III-2)
Q3.sigma.X.sub.k=Average .sigma.X.sub.k of 2.sup.nd highest 25% of
the collected .sigma.X.sub.k (III-3)
Q4.sigma.X.sub.k=Average .sigma.X.sub.k of the highest 25% of the
collected .sigma.X.sub.k (III-4)
Top3.sigma.X.sub.k=Average .sigma.X.sub.k of the lowest three
collected .sigma.X.sub.k (III-5)
TopHalf.sigma.X.sub.k=Average .sigma.X.sub.k of lowest 50% of the
collected .sigma.X.sub.k (III-6)
Average.sigma.X.sub.k=Average .sigma.X.sub.k of all collected
.sigma.X.sub.k (III-7)
[0123] Individual Input Variability Gaps
Gap.sigma.X.sub.k=.sigma.X.sub.k-Selected Variability Metric from
(III-1 to III-7). (III-8) [0124] The preferred embodiment of
Gap.sigma.X.sub.k is to use the Q1.sigma.X.sub.k, for overall gap,
and to use the others to create intermediate gap closure goals.
[0125] Individual Output Variability Metrics
Q1Vo.sub.i=Average Vo.sub.i of lowest 25% of collected Vo.sub.i
(III-9)
Q2Vo.sub.i=Average Vo.sub.i of 2.sup.nd lowest 25% of the collected
Vo.sub.i (III-10)
Q3Vo.sub.i=Average Vo.sub.i of 2.sup.nd highest 25% of the
collected Vo.sub.i (III-11)
Q4Vo.sub.i=Average Vo.sub.i of the highest 25% of the collected
Vo.sub.i (III-12)
Top3Vo.sub.i=Average Vo.sub.i of the lowest three collected
Vo.sub.i (III-13)
TopHalfVo.sub.i=Average Vo.sub.i of lowest 50% of the collected
Vo.sub.i (III-14)
AverageVo.sub.i=Average Vo.sub.i of all collected Vo.sub.i
(III-15)
[0126] Individual Output Variability Gaps
GapVo.sub.i=Vo.sub.i-Selected Individual Metric from (III-9 to
III-15). (III-16) [0127] The preferred embodiment of GapVo.sub.i is
to use the Q1Vo.sub.i, and to use the others to create intermediate
gap closure goals.
[0128] Individual Output Metrics--Variability Ratio Vr.sub.i.
Q1Vr.sub.i=Average Vr.sub.i of lowest 25% of collected Vr.sub.i
(III-17)
Q2Vr.sub.i=Average Vr.sub.i of 2.sup.nd lowest 25% of the collected
Vr.sub.i (III-18)
Q3Vr.sub.i=Average Vr.sub.i of 2.sup.nd highest 25% of the
collected Vr.sub.i (III-19)
Q4Vr.sub.i=Average Vr.sub.i of the highest 25% of the collected
Vr.sub.i (III-20)
Top3Vr.sub.i=Average Vr.sub.i of the lowest three collected
Vr.sub.i (III-21)
TopHalfVr.sub.i=Average Vr.sub.i of lowest 50% of the collected
Vr.sub.i (III-22)
AverageVr.sub.i=Average Vr.sub.i of all collected Vr.sub.i
(III-23)
[0129] Individual Variability Ratio Gaps
GapVr.sub.i=Vr.sub.i-Selected Individual Metric from (III-17 to
III-23). (III-24) [0130] The preferred embodiment of GapVr.sub.i is
to use the Q1Vr.sub.i, and to use the others to create intermediate
gap closure goals.
[0131] Individual Output Metrics--Variability Reduction Ratio
Vrr.sub.i.
Q1Vrr.sub.i=Average Vrr.sub.i of lowest 25% of collected Vrr.sub.i
(III-25)
Q2Vrr.sub.i=Average Vrr.sub.i of 2.sup.nd lowest 25% of the
collected Vrr.sub.i (III-26)
Q3Vrr.sub.i=Average Vrr.sub.i of 2.sup.nd highest 25% of the
collected Vrr.sub.i (III-27)
Q4Vrr.sub.i=Average Vrr.sub.i of the highest 25% of the collected
Vrr.sub.i (III-28)
Top3Vrr.sub.i=Average Vrr.sub.i of the lowest three collected
Vrr.sub.i (III-29)
TopHalfVrr.sub.i=Average Vrr.sub.i of lowest 50% of the collected
Vrr.sub.i (III-30)
AverageVrr.sub.i=Average Vrr.sub.i of all collected Vrr.sub.i
(III-31)
[0132] Individual Variability Reduction Ratio Gaps
GapVrr.sub.i=Vrr.sub.i-Selected Individual Metric from (III-25 to
III-31). (III-32) [0133] The preferred embodiment of GapVrr.sub.i
is to use the Q1Vrr.sub.i, and to use the others to create
intermediate gap closure goals.
[0134] Overall Unit Performance Metrics--Induced Variability
Q1Vi=Average Vi of lowest 25% of collected Vi (III-33)
Q2Vi=Average Vi of 2.sup.nd lowest 25% of the collected Vi
(III-34)
Q3Vi=Average Vi of 2.sup.nd highest 25% of the collected Vi
(III-35)
Q4Vi=Average Vi of the highest 25% of the collected Vi (III-36)
Top3Vi=Average Vi of the lowest three collected Vi (III-37)
TopHalf Vi=Average Vi of lowest 50% of the collected Vi
(III-38)
Average Vi=Average Vi of all collected Vi (III-39)
[0135] Overall Induced Variability Gaps
GapVi=Vi-Selected Individual Metric from (III-33 to III-39).
(III-40) [0136] The preferred embodiment of GapVi is to use the
Q1Vi, and to use the others to create intermediate gap closure
goals.
[0137] Overall Unit Performance Metrics--Output Variability
Q1Vo=Average Vo of lowest 25% of collected Vo (III-41)
Q2Vo=Average Vo of 2.sup.nd lowest 25% of the collected Vo
(III-42)
Q3Vo=Average Vo of 2.sup.nd highest 25% of the collected Vo
(III-43)
Q4Vo=Average Vo of the highest 25% of the collected Vo (III-44)
Top3Vo=Average Vo of the lowest three collected Vo (III-45)
TopHalfVo=Average Vo of lowest 50% of the collected Vo (III-46)
AverageVo=Average Vo of all collected Vo (III-47)
[0138] Overall Output Variability Gaps
Gap Vo=Vo-Selected Individual Metric from (III-41 to III-47).
(III-48) [0139] The preferred embodiment of GapVo is to use the
Q1Vo, and to use the others to create intermediate gap closure
goals.
[0140] Overall Unit Performance Metrics--Variability Ratio
Q1Vr=Average Vr of lowest 25% of collected Vr (III-49)
Q2Vr=Average Vr of 2.sup.nd lowest 25% of the collected Vr
(III-50)
Q3Vr=Average Vr of 2.sup.nd highest 25% of the collected Vr
(III-51)
Q4Vr=Average Vr of the highest 25% of the collected Vr (III-52)
Top3Vr=Average Vr of the lowest three collected Vr (III-53)
TopHalfVr=Average Vr of lowest 50% of the collected Vr (III-54)
Average Vr=Average Vr of all collected Vr (III-55)
[0141] Overall Variability Ratio Gaps
GapVr=Vr-Selected IndiVrdual Metric from (III-49 to III-55).
(III-56) [0142] The preferred embodiment of GapVr is to use the
Q1Vr, and to use the others to create intermediate gap closure
goals.
[0143] Overall Unit Performance Metrics--Variability Reduction
Ratio Vrr
Q1Vrr=Average Vrr of lowest 25% of collected Vrr (III-57)
Q2Vrr=Average Vrr of 2.sup.nd lowest 25% of the collected Vrr
(III-58)
Q3Vrr=Average Vrr of 2.sup.nd highest 25% of the collected Vrr
(III-58)
Q4Vrr=Average Vrr of the highest 25% of the collected Vrr
(III-59)
Top3Vrr=Average Vrr of the lowest three collected Vrr (III-60)
TopHalfVrr=Average Vrr of lowest 50% of the collected Vrr
(III-61)
Average Vrr=Average Vrr of all collected Vrr (III-62)
[0144] Overall Variability Reduction Ratio Gaps
GapVrr=Vrr-Selected Individual Metric from (III-57 to III-62).
(III-63) [0145] The preferred embodiment of GapVrr is to use the
Q1Vrr, and to use the others to create intermediate gap closure
goals.
[0146] In addition to the standard deviations Quartiles 1 given in
the above paragraph, some process parameter average values can be
similarly divided into quartiles and reported back to participants.
This is not the preferred practice as the average values represent
the set point setting and are considered proprietary by study
participants. One exception to this is the column pressure of
atmospheric crude units and vacuum units. These parameters averages
can be reported back as higher pressure causing the distillation to
be more difficult and less energy efficient. In reporting back the
pressures, it is important to divide the industry data into process
types. In particular for vacuum unit there are two main types (wet
and dry vacuum units). The pressures can only be compared with like
types of vacuum units.
[0147] Column Pressure (P) Metrics
Q1P=Average P of lowest 25% of collected P (III-64)
Q2P=Average P of 2.sup.nd lowest 25% of the collected P
(III-65)
Q3P=Average P of 2.sup.nd highest 25% of the collected P
(III-66)
Q4P=Average P of the highest 25% of the collected P (III-67)
Top3P=Average P of the lowest three collected P (III-68)
TopHalfP=Average P of lowest 50% of the collected P (III-69)
Average P=Average P of all collected P (III-70)
[0148] Overall Variability Reduction Ratio Gaps
GapP.sub.=P-Selected Individual Metric from (III-64 to III-70).
(III-71) [0149] The preferred embodiment of GapP is to use the Q1P,
and to use the others to create intermediate gap closure goals.
[0150] It is anticipated that individual unit types will contain
certain variables that the industry will find valuable to compare
as averages, which will not be considered proprietary process
information. Column pressure is just an example, and other average
values maybe selected for other processes. In some industries, the
set points might not be considered proprietary, and industry
participants might be willing to share this information for
comparative purposes. In such cases, the critical set points might
be collected and shared by this same technique.
Calculation of Economic Values of Closing Performance Gaps
[0151] Some of the key areas where a gap economic value from
equations III-8, 16, 24, 40, 48, 56, 63, and 71 can be estimated
are:
Yield improvements Energy improvements Capacity improvements.
[0152] These improvements can be achieved in at least three ways
that can be calculated from the novel metrics of this
invention:
The improvement achieved by matching the control performance
benchmark Vrr and/or Vrr.sub.i while making no changes in the
induced variability Vi or Vi.sub.i. The improvement achieved by
matching the induced variability benchmark Vi or Vi.sub.i while
making no changes in the control assets performance Vrr or
Vrr.sub.i The improvement achieved by matching both the induced
variability benchmark Vi or Vi.sub.i and the control assets
performance Vrr or Vrr.sub.i simultaneously.
[0153] FIG. 5 illustrates how variability reduction can affect
production yield and throughput. Time series A in FIG. 5 represents
the present product variation, which varies between the
demonstrated upper and lower data limits (bar 1), as dictated by
the standard deviation of the data, Vo, which is calculated from
the collected process data.
[0154] Time series B represents the time series benchmark generated
from calculation of the benchmark achievable variation Vob, and the
selected benchmark Variability Reduction Ratio benchmark Vrrb as
given in the equation below:
Vob=Max[Vi*Min(Vrrb,Vrr), MinVo] (IV-1) [0155] Where [0156]
Vob=benchmark achievable standard deviation of the product
measurement. [0157] Vi=the induced variability of the unit being
analyzed [0158] Vrrb=the selected Vrr benchmark from equations
III-57 through III-62 above [0159] Vrr=the variability reduction
ratio for the unit being analyzed. Use of the actual value assures
that if by chance the Vrr of the process is better than the
benchmark, that the current performance will be used in the
calculation. [0160] MinVo=Minimum demonstrated Vo in the
population, use of the minimum in the collected industrial data
assures that the numbers calculated are limited to performance that
has been demonstrated to be achievable in actual industrial
application. Note that the average to the three best Vo's can be
used instead of the single best Vo to prevent revealing any
participants actual value. Different values may be substituted into
Equation IV-1 depending on the objective of the improvement
analysis:
Case 1: Analysis of the Controls Improvement
[0161] Vi=the Vi of the unit
[0162] Vrr=a selected benchmark value from equations III-57 through
III-62 above.
Case 2: Analysis of Induced Variability Improvement
[0163] Vi=a selected benchmark value from equations III-33 through
III-39 above.
[0164] Vrr=the Vrr of the unit.
Case 3: Analysis of Simultaneous Induced Variability and Controls
Improvement
[0165] Vi=a selected benchmark value from equations III-33 through
III-39 above.
[0166] Vrr=a selected benchmark value from equations III-57 through
III-62 above.
One embodiment uses the overall unit Q1Vi, and Q1Vrr to simplify
analysis, but note than a large combination of analysis are
possible by substituting any combination of individual metrics and
overall metrics from equations III-1 through III-63 above or values
in the analysis equations. This preferred embodiment calculates the
potential variation achievable if the unit process control assets
performance can match that of the 1.sup.st quartile average, and
limits the potential variation to be no smaller than the smallest
demonstrated variations reported by the industry data
collection.
[0167] In an alternate embodiment, Vob is not calculated from Vrr
and Vi as given in Equation IV-1, but instead Vob equal=is set to
the average of the 1.sup.st quartile Vo (Q1Vo from Equation III-41
above). However, this method may not be preferred, since it ignores
the input variability that the unit faces. It might not be
demonstrated by the industry that Q1Vo could be achieved stating
with the level of induced variability the unit faces.
[0168] Referring back to FIG. 5, now that we have explained how
time series B is established, we can now easily see the gap in
performance. The Gap is Vo-Vob. If the unit being analyzed can
match the benchmark Vob, then the time series of the process would
be the same as that in time series B.
[0169] By application of the method described above we have
established that time series B has been demonstrated achievable in
industry. Once time series B has been achieved, the opportunity
exists to move the process set point to the existing process
constraints to take advantage of the lower variation and achieve an
economic benefit. This is done by adjusting the set point to push
time series B against the most economical constraint upper or lower
bound depending on the better economics. Three types of constraints
are illustrated in FIG. 5:
The demonstrated data upper and lower limit--(bar 1). Or the
product upper or lower specification constraint--(bar 2). A known
process constraint upper or lower constraint that is a hard
physical constraint or a calculated constraint. Calculated
constraints can include limits inferred from other outputs or
inputs including combination constraints. This constraint could be
directly measured or calculated by any conceivable method--(bar
3).
[0170] All of these constraint types can be used, however one
preferred embodiment is the use of the "Same Limit Rule," which
means that the upper and lower bounds demonstrated in the collected
data for the unit are used. This is the same as the Demonstrated
Data Limit in FIG. 5 (Bar 1).
[0171] The "Same Limit Rule" is preferred because its use will
ensure that the economic value will be conservatively estimated,
and the process is known to be able to achieve these limits because
the historical data collected itself proves that to be so. This
limit is illustrative and exemplary only, since any measured or
calculated limit established by any method may be used.
[0172] In the refining industry, for example, the upper and lower
product specifications are not likely to be achievable because the
overall plant optimization LP model would have set the set points
that the process runs under and the act of adjusting to the wider
specification limits would defeat the overall plant optimization.
Adjusting to the known process constraints is perfectly valid but
requires the work to establish the actual known limits, which is
not a trivial task. One method would be to communicate to the LP
model the new capability demonstrated by time series B, and a new
soft limit would be calculated by the LP. This would result in new
bar 3 limits.
[0173] Referring back to FIG. 5, the process can now be improved by
moving the set point such that the reduced variability is up
against the process constraint selected for analysis, that being
bar 1, bar 2, or bar 3.
[0174] If the time series represents an output quality measure such
as 90% point for a product of a crude distillation unit then this
shift has a known economic value at the plant and also infers a
change in the volume of the product produced. If the distribution
is moved upwards, then the temperature is increasing and the amount
of volume of increased production can be calculated from the
boiling point curve for that crude feed as given in FIG. 4.
[0175] If the time series represents a production rate, then the
production rate can be increased by moving upwards to the selected
constraint. In both cases, the economic value of the increased
production can be calculated.
Economic Value=increased volume*price of product (IV-2)
[0176] In the case of a distillation unit, unless the overall
throughput is increased, then the improvement represents a yield
improvement to a more valuable product. Referring to FIG. 6,
reduced variability results in an increase in production of a more
valuable product over a less valuable product.
Economic Value=Increase in draw 1*(price draw 1-price draw 2)
(IV-3)
[0177] An energy savings can be calculated directly from a
reduction in the temperature variations of the individual column
distillation product streams. FIG. 7 illustrates the basic
concepts. All upward swings in the variation of the temperature of
the column products are assumed to require the addition of heat
into the unit. In the illustrated case, the heat source is a fired
furnace with efficiency .epsilon..
Energy Savings
Value=P.SIGMA..sub.1([0.5m.sub.1Cp.sub.1(6.sigma.T.sub.11-6.sigma.T.sub.2-
1)]/.epsilon.) (IV-4)
[0178] Where [0179] P=price of energy in economic unit per unit of
mass [0180] 1=streamidentifier . . . 1 can be just the side streams
from a distillation unit, or can encompass all exit streams from
the unit, or any subset being analyzed. [0181]
.SIGMA..sub.1=summation over all selected streams 1 to i [0182]
.epsilon.=unit heat source efficiency factor. In refining this is
the efficiency of the unit fired heater. However the general form
of equation IV-4 allows .epsilon. to represent the efficiency of
any unit heat source. [0183] m.sub.1=mass flow or stream 1. [0184]
Cp.sub.1=heat capacity of stream 1 [0185] .sigma.T.sub.11=standard
deviation of temperature of column product stream 1 as measured in
the observation data. [0186] .sigma.T.sub.21=standard deviation of
temperature benchmark selected from the individual output variation
benchmark equations III-9 through III-15 above. Equation IV-4 is
the preferred embodiment. However, depending on the refinery
control philosophy (3.sigma. or 2.sigma. control limits) the
constant 6 (corresponding to 3.sigma.) in equation IV-4 can be
replaced with a constant value of 4 (corresponding to
2.sigma.).
[0187] The aforementioned methods of calculating quality, yield and
energy improvement are illustrative and exemplary, since quality,
yield, and energy improvement may be determined using other
measurements and calculations.
Graphical Construct for Visualizing and Diagnosing Overall Unit
Performance.
[0188] FIG. 8 shows a novel graphical construct according to one
embodiment to display the overall performance of a unit by using
the overall metrics Vo, Vi, and Vrr calculated by equations III-40,
III-48, and III-63 respectively. This graphical construct will now
be referred to as a "Variability Graph."
[0189] The Variability Graph is constructed for one unit type at a
time. All units of the same type under analysis can be plotted on
the same graph to indicate their relative performance. The example
unit type selected for one embodiment is a crude distillation unit,
however, similar graphic constructs can be developed for all unit
types.
[0190] The X-axis of this graph is the induced variability metric,
Vi, which is calculated by equation III-40. For crude units, Vi is
given as the standard deviation of the side draw temperatures of
the crude unit side streams in degrees F. The side stream draw
temperature is a measurement of the composition of the stream, and
the variation of the temperature is a measurement of the quality of
the material. The induced variability represents the amount of side
stream temperature variation that the input variation would cause
the side stream products to have if not removed by the unit
controls.
[0191] The Y-axis of this graph is the output variability metric
Vo, which is calculated by equation III-48. For crude units, Vo is
given as the actual standard deviation of the side draw
temperatures in degrees F. as calculated from the raw observation
data. Thus Vo is the key actual column control performance.
[0192] Each unit in the study can be plotted using the units Vo and
Vi data points. Point 1, 2, and 3 in FIG. 8 represent the overall
performance of three crude units. Since the most desirable
condition is zero induced variability and zero product variability,
the most desirable spot on the graph is at the origin.
[0193] The vertical dashed lines on FIG. 8 divide the X-axis axis
(Vi induced variability) into four regions representing the four
quartiles of Vi performance. Quartile 1 is the lowest variability
and the most desirable quartile to be in. The horizontal lines on
the FIG. 8 divide the Y-axis (Vo output variability) into four
regions representing the four quartiles of Vo performance. Quartile
1 is the lowest variability and the most desirable quartile.
[0194] The radial diagonal lines that extend outward from the
origin divide the graph space into four regions representing the
four quartiles of variability reduction performance as measured by
Vrr which is calculated by equation III-63. Quartile 1 is the
lowest variability and the most desirable quartile.
[0195] To understand why the radial lines represent the Vrr,
consider point 4 on FIG. 8, the angle a and the right triangle
formed by the three points of the origin, point 4 and intercept of
the x-axis of a line dropped straight down from point 4. The
tangent of .alpha. is 3/10, which is Vo/Vi. Looking at equation I-6
and I-7, it can be seen that Vr=Vo/Vi and Vrr=1-Vo/Vi. Thus, the
radial lines directly represent Vr and Vrr, and also represent
lines of constant controller performance over any value of induced
variability recorded in the industrial data collected.
[0196] With the information conveyed by the Variability Graph, one
skilled in the art can ascertain knowledge about a unit's
performance by simple examination of the region of the graph where
the point representing the unit's performance falls.
[0197] For example, consider Point 1 in FIG. 8. This unit is
operating very well. The unit's overall performance is measured by
Vo and Vo is in the 1.sup.st quartile. The induced variability is
measured by Vi, and Vi is also in the 1.sup.st quartile. The
performance of the controls is measured by the Vr and the Vrr is
also in the first quartile. Point 1 is one of the very best
performing units in the entire study. In fact it is in the top
0.25*0.25*0.25= 1/64 of the study population.
[0198] Now we will look at Point 2 in FIG. 8. Point two is a poor
overall performing unit because the main measurement of success is
Vo, and the Vo of the unit is in the 4.sup.th quartile. Looking at
the Vi we can see that the induced variability of the unit is
extremely high in comparison to the unit population and is in the
high end of the 4.sup.th quartile.
[0199] Previously, management might erroneously conclude that this
unit represented by point 2 in FIG. 8 is in need of better process
controls. An investment in expensive new control applications for
the unit might be pursued. However, examination of the Vrr shows
that this unit already has exceptional control performance. The Vrr
is quartile 1. In fact, if extending a radial line from point 2 to
the origin as is shown in FIG. 8, it passes through point 1 which
represents one of the very best performing units in the industrial
data. Therefore, it can be concluded that this is not a unit
controls problem. This is a problem caused by excessively high
induced variability in the feeds to the unit. Even the very best
controller in the study could not achieve outstanding Vo results
with this high an induced variability. The diagnosis then is to
search out the causes of the high induced variability. This can be
done by looking at the quartile ratings of the three inducing
parameters (feed rate, feed temperature and feed API) from
examination of the results of equation III-8.
[0200] As we improve the induced variability of the unit presented
by point 2, we will be improving Vi with constant controller
performance. Thus the unit performance should improve and travel
down a line of constant Vrr approximated by Line b. As can be seen
on the graph at point 5, if the induced variability can just be
reduced to 3.sup.rd quartile which is still higher than the study
average that the overall unit performance as measured by Vo will be
1.sup.st quartile.
[0201] Now we will examine point 3 in FIG. 8. The unit represented
by point 3 is also an overall poor performer as measured by Vo
which is 4.sup.th quartile. However, examination of the induced
variability shows that the unit has no excuses since the induced
variability is low and in the 1.sup.st quartile. The problem with
this unit is the poor performance of the unit controls as witnessed
by the poor 4.sup.th quartile Vrr. This unit is in need of tuning
the existing controls, and potentially new control
applications.
[0202] It should be noted that it has been demonstrated in
industrial applications that points in the region of the graph
occupied by point 3 can also have mechanical problems that prevent
the unit from performing well that are independent of the controls
themselves. The unit should also be checked for mechanical
integrity of the column internals. If the unit is mechanically
sound, then the existing controls might be poorly tuned. Units in
this region of the graph often have controls that are causing more
harm than good. Simply placing the offending controls in open loop
might reduce output variability dramatically.
[0203] Assuming that the unit is mechanically sound, as we work to
improve controller performance, the unit performance will improve
and travel down a line of constant induced variability approximated
by Line c. As can be seen, the unit will achieve 1.sup.st quartile
overall performance if the controls performance measured by Vrr can
just achieve 3.sup.rd quartile as shown by Point 6 in FIG. 8.
[0204] As previously stated, Variability Graphs have been created
through this invention for all refining unit types. Some units have
multiple graphs. For example, Fluid Catalytic Cracking (FCC) units
typically have 5 graphs. The FCC unit can be placed on one graph
showing the final products from the main fractionator. However,
there is more information to be displayed for a FCC unit. The
reaction section of the unit must be analyzed separately for flue
gas oxygen or carbon monoxide control depending on the unit
combustion mode (complete or incomplete combustion). In addition,
the unit wet gas compressor or air blower controls must be analyzed
separately depending on which limits unit throughput. This results
in 5 Variability Graphs in the FCC analysis. This further
illustrates the general use of variability graphs to analyze
subparts of the process.
[0205] Additional Variability Graphs can be constructed on a stream
by stream basis or for specialized portions of the unit operation.
The use of the variability graphs for explaining stream-by-stream
performance is illustrative and exemplary, since the graphs may be
used to analyze any control system.
On-Line Real Time Analysis with the Metrics
[0206] It should be recognized that all calculations within this
patent application can be automated and placed in real time
monitoring and control applications to deliver process alarms,
invoke expert systems or logic trees, provide feedback to control
loops, and directly deliver set points.
Automated Delivery of Advice by Quartile
[0207] The division of the key metrics Vo, Vi, and Vrr developed
above allow the automated delivery of advice on the performance of
the unit. A combined "Performance Key" metric, Vo-Vi-Vrr, is
developed by the concatenation of the three measures separated by
dashes. For example, if Vo is quartile 3, and Vi is quartile 1 and
Vrr is quartile 4, then the Performance Key metric Vo-Vi-Vrr would
be 3-1-4. Since each measure has 4 quartiles, there are 4*4*4=64
potential values of Vo-Vi-Vrr. For each unit type a table can be
built that delivers advice based on combined metric. Note that any
combination of the metrics Vo and Vi can be used, as values of just
Vo and Vi contain within them the value of Vrr. The addition of Vr
or Vrr allows the space to be further divided into 64 regions for
diagnosis.
[0208] A computer program matches the combined metric to one of the
64 options defined by the Performance Key and delivers advice
appropriate for the unit performance. An example of this advice for
a vacuum unit is given in Table 500. Table 500 is illustrative and
exemplary, and a number of similar tables can be used for different
types of units. The advice in table 500 is exemplary only, and
additional or alternate advice statements can be automatically
constructed. For example, the main input variables variability can
be automatically compared to their quartiles to relate which of the
inputs is most responsible for high induced variability.
[0209] As an example of the use of the automated advice from table
500, a Performance Key of 3-1-4 using the automated advice from
Table 500 would deliver the following:
TABLE-US-00001 Advice 1 The overall Performance Key = 3-1-4. Advice
2 Overall unit performance is below study average. Advice 3 Poor
variability reduction with controls. Advice 4 Excellent low input
variability. Advice 5 Tune existing controls and consider control
application improvements.
[0210] It should be noted that additional and more detailed
automated interpretation and advice could be delivered by more
detailed automated analysis of any of the metrics of this
invention. All are contemplated and within the scope of this
invention.
Vr Vector Representation
[0211] An alternate method of analysis of a unit's performance
based on the Vo and Vi is the Vr Vector Representation.
|Vr|=(Vo.sup.2+Vi2).sup.0.5 (V-1)
.alpha.=Tan.sup.-1Vr (V-2)
[0212] Where |Vr|=The magnitude of the Vr vector=the hypotenuse of
the right triangle formed with Vo and Vi and the sides.
.alpha.=the Vr angle
[0213] The Vr vector represents the total variability experienced
by the unit in analysis. The larger the value |Vr| the more "shook
up" the unit is. It is desirable to have lower values of |Vr|. The
angle .alpha. represents the amount of variability that has been
reduced by the units controls. The smaller the value of .alpha.,
the more variability has been reduced. |Vr| and .alpha. can be
placed into quartiles and placed into a graph similar to FIG. 8 as
shown in FIG. 10. Advice similar to that in Table 500 can also be
developed and delivered using |Vr| and .alpha..
[0214] The Vr vector presents the entire performance picture in one
vector. It is mathematically useful to interpret Vr in polar
coordinates, for the purpose of creating generalized quartiles that
replace the three quartile sets previously described with one set
of quartiles.
[0215] The Vr vector interpretation provides a basis for analyzing
the information contained in two vectors, such as would occur when
comparing the variability performance of two similar units or the
same unit at two different times (as in an on-line application).
Vector algebra can be used in these cases, namely, vector addition,
subtraction, and dot and cross products.
[0216] As shown in FIG. 9, one embodiment of a system used to
perform the method includes a computing system. The hardware
consists of a processor 910 that contains adequate system memory
920 to perform the required numerical computations. The processor
910 executes a computer program residing in system memory 920 to
perform the method. Video and storage controllers 930 are required
to enable the operation of display 940. The system includes various
data storage devices for data input including floppy disk units
950, internal/external disk drives 960, internal CD/DVDs 970, tape
units 980, and other types of electronic storage media 990. The
aforementioned data storage devices are illustrative and exemplary
only. These storage media are used to enter and store the process
data frequency and loss data to the system, store the calculations,
and store the system-produced analysis reports and graphs. The
calculations can apply statistical software packages or can be
performed from the data entered in spreadsheet formats using
Microsoft Excel, for example. The analysis calculations are
performed using either customized software programs designed for
company-specific system implementations or by using commercially
available software that is compatible with Excel or other database
and spreadsheet programs. The system can also interface with
proprietary or public external storage media 1030 to link with
other databases to provide additional data to be applied to the
performance measurement benchmarking system and method
calculations. The output devices can be a telecommunication device
1000 to transmit the calculation worksheets and other system
produced graphs and reports via an intranet or the Internet to
management or other personnel, printers 1010, electronic storage
media similar to those mentioned as input devices and proprietary
storage databases 1030. These output devices are illustrative and
exemplary only. If the analysis is to be performed on-line for
real-time process monitoring and control, then the above system can
also have additional sources of input and output.
[0217] The manufacturing control system 2000, which can include
programmable logic controllers, distributed control systems, or
field bus devices, would provide live data to the processors 910.
It is also possible for the manufacturing control system 2000,
which contains central processing systems, to take on all or part
of the tasks of the processor 910. The results of the methods and
calculations can be received from the processors 910 for use in
real time control and alarming inside the manufacturing control
system 2000.
[0218] Additional data for the method may come from the process
data historian 2010, which keeps records of process variable and
parameter values with time stamps and can also share any portion of
the calculations performed by the processors 910. The results of
the calculations from the processors 910 can also be stored in the
process data historian 2010.
[0219] Input data can also be received by the processors 910 from
external process control systems 2020 that reside on computers
external to the manufacturing control system 2000. The results of
the methods and calculations can be received from the processors
910 for use in real time control and alarming inside the external
process control systems 2020.
[0220] The manufacturing information system 2030 can receive data
and results from the processors 910 either directly or secondarily
from the manufacturing control system 2000 the process data
historian 2010 or the external process control systems 2020. This
data can be used to create key performance indicators such as Vi,
Vo, and Vrr for plots and written reports. Information from the
manufacturing information system 2030 can be passed on to the
company information systems 2040 the company intranet or world wide
web 2050 for use in any conceivable purpose.
[0221] The foregoing disclosure and description of the preferred
embodiments of the invention are illustrative and explanatory
thereof, and various changes in the details of the illustrated
system and method may be made without departing from the scope of
the invention. In particular, the system can operate as a stand
alone analysis method without the process data historian 2010,
external process control systems 2020, manufacturing information
system 2030, company information systems 2040, and company intranet
or world wide web 2050. Additionally, an embodiment of the system
can be on-line live by incorporating the processor 910 functions
into the manufacturing control system 2000, the process data
historian 2010, the external process control systems 2020, or the
manufacturing information system 2030.
TABLE-US-00002 TABLE 100 Input Data Collected by Unit Type Unit
Type Input Parameters Crude Units Crude Flow Crude Temp Crude
API.sub.(Inferred) Furnace Outlet Temp Column Pressure Vacuum Units
ATB Flow ATB Temp ATB API.sub.(Inferred) Furnace Outlet Temp Column
Pressure FCC Units Fresh Feed Rate Preheat Temp Riser Outlet Temp
O.sub.2 or CO Vol % Air or WG Flow Hydrocrackers Fresh Feed Rate
WABT Recycle Flow Reformers Fresh Feed Rate WAIT
LHSV.sub.(Inferred) Hydrotreaters Fresh Feed WABT
LHSV.sub.(Inferred) Cokers Fresh Feed Furnace Outlet Temp Recycle
Flow CFR
TABLE-US-00003 TABLE 200 Output Data Collected by Unit Type Unit
Type Output Parameters Crude Units Draw Temp of each Draw Flow Rate
of each Bottoms Flow Rate Bottoms Flow Temp side draw product side
draw product Vacuum Units Draw Temp of each Draw Flow Rate of each
Bottoms Flow Rate Bottoms Flow Temp side draw product side draw
product FCC Units Draw Temp of each Draw Flow Rate of each Bottoms
Flow Rate Bottoms Flow Temp side draw product side draw product
Hydrocrackers Draw Temp of each Draw Flow Rate of each Bottoms Flow
Rate Bottoms Flow Temp side draw product side draw product
Reformers RONC Octane Product Flow Rate Product Flow Temp Analysis
Hydrotreaters Draw Temp of each Draw Flow Rate of each Bottoms Flow
Rate Bottoms Flow Temp side draw product side draw product Cokers
Draw Temp of each Draw Flow Rate of each Bottoms Flow Rate Bottoms
Flow Temp side draw product side draw product
TABLE-US-00004 TABLE 300 Output Data Collected by Unit Type
Crude-Switch Unit Type Normal-State Data or Drum-Switch Data Crude
Units 24-hr datasets - 3 ea 24-hr datasets - 3 ea Vacuum Units
24-hr datasets - 3 ea 24-hr datasets - 3 ea Reformers 12-hr
datasets - 3 ea FCC Units 12-hr datasets - 3 ea Hydrocrackers 12-hr
datasets - 3 ea Hydrotreators 12-hr datasets - 3 ea Cokers 12-hr
datasets - 3 ea 12-hr datasets - 3 ea
TABLE-US-00005 TABLE 400 Example Vi Gain Matrix for a Crude Unit
Crude Feed Rate Furnace Inlet Temp API, Side-Draw Stream .degree.
F./vol % .degree. F./.degree. F. .degree. F./API LSR 16.9 1.0 13.8
Med Naphtha 12.9 1.0 12.4 Hvy Naphtha 12.2 1.0 11.1 Lt Kerosene
11.3 1.0 9.7 Kerosene 11.2 1.0 8.4 Diesel 11.4 1.0 7.1 AGO 11.5 1.0
5.7 HGO 11.7 1.0 4.4 LVGO 12.8 1.0 3.0
TABLE-US-00006 TABLE 500 Automated Advice using the "Performance
Key" derived from Vo-Vi-Vrr. Performance Key Advice2 Advice3 1-1-1
Excellent overall performance. Excellent variability reduction with
controls. 1-2-1 Excellent overall performance. Excellent
variability reduction with controls. 1-3-1 Excellent overall
performance. Excellent variability reduction with controls. 1-4-1
Excellent overall performance. Excellent variability reduction with
controls. 1-1-2 Excellent overall performance. Better than average
variability reduction with controls. 1-2-2 Excellent overall
performance. Better than average variability reduction with
controls. 1-3-2 Excellent overall performance. Better than average
variability reduction with controls. 1-4-2 Excellent overall
performance. Better than average variability reduction with
controls. 1-1-3 Excellent overall performance. Below average
variability reduction with controls. 1-2-3 Excellent overall
performance. Below average variability reduction with controls.
1-3-3 Excellent overall performance. Below average variability
reduction with controls. 1-4-3 Excellent overall performance. Below
average variability reduction with controls. 1-1-4 Excellent
overall performance. Poor variability reduction with controls.
1-2-4 Excellent overall performance. Poor variability reduction
with controls. 1-3-4 Excellent overall performance. Poor
variability reduction with controls. 1-4-4 Excellent overall
performance. Poor variability reduction with controls. 2-1-1 Better
than study average overall performance. Excellent variability
reduction with controls. 2-2-1 Better than study average overall
performance. Excellent variability reduction with controls. 2-3-1
Better than study average overall performance. Excellent
variability reduction with controls. 2-4-1 Better than study
average overall performance. Excellent variability reduction with
controls. 2-1-2 Better than study average overall performance.
Better than average variability reduction with controls. 2-2-2
Better than study average overall performance. Better than average
variability reduction with controls. 2-3-2 Better than study
average overall performance. Better than average variability
reduction with controls. 2-4-2 Better than study average overall
performance. Better than average variability reduction with
controls. 2-1-3 Better than study average overall performance.
Below average variability reduction with controls. 2-2-3 Better
than study average overall performance. Below average variability
reduction with controls. 2-3-3 Better than study average overall
performance. Below average variability reduction with controls.
2-4-3 Better than study average overall performance. Below average
variability reduction with controls. 2-1-4 Better than study
average overall performance. Poor variability reduction with
controls. 2-2-4 Better than study average overall performance. Poor
variability reduction with controls. 2-3-4 Better than study
average overall performance. Poor variability reduction with
controls. 2-4-4 Better than study average overall performance. Poor
variability reduction with controls. 3-1-1 Overall unit performance
is below study average. Excellent variability reduction with
controls. 3-2-1 Overall unit performance is below study average.
Excellent variability reduction with controls. 3-3-1 Overall unit
performance is below study average. Excellent variability reduction
with controls. 3-4-1 Overall unit performance is below study
average. Excellent variability reduction with controls. 3-1-2
Overall unit performance is below study average. Better than
average variability reduction with controls. 3-2-2 Overall unit
performance is below study average. Better than average variability
reduction with controls. 3-3-2 Overall unit performance is below
study average. Better than average variability reduction with
controls. 3-4-2 Overall unit performance is below study average.
Better than average variability reduction with controls. 3-1-3
Overall unit performance is below study average. Below average
variability reduction with controls. 3-2-3 Overall unit performance
is below study average. Below average variability reduction with
controls. 3-3-3 Overall unit performance is below study average.
Below average variability reduction with controls. 3-4-3 Overall
unit performance is below study average. Below average variability
reduction with controls. 3-1-4 Overall unit performance is below
study average. Poor variability reduction with controls. 3-2-4
Overall unit performance is below study average. Poor variability
reduction with controls. 3-3-4 Overall unit performance is below
study average. Poor variability reduction with controls. 3-4-4
Overall unit performance is below study average. Poor variability
reduction with controls. 4-1-1 Overall unit performance is 4th
quartile. Excellent variability reduction with controls. 4-2-1
Overall unit performance is 4th quartile. Excellent variability
reduction with controls. 4-3-1 Overall unit performance is 4th
quartile. Excellent variability reduction with controls. 4-4-1
Overall unit performance is 4th quartile. Excellent variability
reduction with controls. 4-1-2 Overall unit performance is 4th
quartile. Better than average variability reduction with controls.
4-2-2 Overall unit performance is 4th quartile. Better than average
variability reduction with controls. 4-3-2 Overall unit performance
is 4th quartile. Better than average variability reduction with
controls. 4-4-2 Overall unit performance is 4th quartile. Better
than average variability reduction with controls. 4-1-3 Overall
unit performance is 4th quartile. Below average variability
reduction with controls. 4-2-3 Overall unit performance is 4th
quartile. Below average variability reduction with controls. 4-3-3
Overall unit performance is 4th quartile. Below average variability
reduction with controls. 4-4-3 Overall unit performance is 4th
quartile. Below average variability reduction with controls. 4-1-4
Overall unit performance is 4th quartile. Poor variability
reduction with controls. 4-2-4 Overall unit performance is 4th
quartile. Poor variability reduction with controls. 4-3-4 Overall
unit performance is 4th quartile. Poor variability reduction with
controls. 4-4-4 Overall unit performance is 4th quartile. Poor
variability reduction with controls. Performance Key Advice4
Advice5 1-1-1 Excellent low input variability. This unit is a good
candidate for RTO. 1-2-1 Good low input variability. This unit is a
good candidate for RTO. 1-3-1 Higher input variability than the
study Reduce input variability for additional performance. average.
1-4-1 Excessively high input variability. Reduce input variability
for additional performance. 1-1-2 Excellent low input variability.
This unit is a good candidate for RTO. 1-2-2 Good low input
variability. This unit is a good candidate for RTO. 1-3-2 Higher
input variability than the study Reduce input variability to
improve performance. average. 1-4-2 Excessively high input
variability. Reduce input variability to improve performance. 1-1-3
Excellent low input variability. Tune existing controls for
improved performance. 1-2-3 Good low input variability. Tune
existing controls for improved performance. 1-3-3 Higher input
variability than the study Reduce input variability and tune
existing controls. average. 1-4-3 Excessively high input
variability. Reduce input variability for additional performance.
1-1-4 Excellent low input variability. Tune existing controls for
improved performance. 1-2-4 Good low input variability. Tune
existing controls for improved performance. 1-3-4 Higher input
variability than the study Tune existing controls for improved
performance. average. 1-4-4 Excessively high input variability.
Tune existing controls and reduce input variability. 2-1-1
Excellent low input variability. This unit is a good candidate for
RTO. 2-2-1 Good low input variability. This unit is a good
candidate for RTO. 2-3-1 Higher input variability than the study
Reduce input variability to improve performance. average. 2-4-1
Excessively high input variability. Reduce input variability to
improve performance. 2-1-2 Excellent low input variability. This
unit is a good candidate for RTO. 2-2-2 Good low input variability.
This unit is a good candidate for RTO. 2-3-2 Higher input
variability than the study Reduce input variability for additional
performance. average. 2-4-2 Excessively high input variability.
Reduce input variability for additional performance. 2-1-3
Excellent low input variability. Tune existing controls for
improved performance. 2-2-3 Good low input variability. Tune
existing controls for improved performance. 2-3-3 Higher input
variability than the study Reduce input variability and tune
existing controls. average. 2-4-3 Excessively high input
variability. Reduce input variability to improve performance. 2-1-4
Excellent low input variability. Tune existing controls for
improved performance. 2-2-4 Good low input variability. Tune
existing controls for improved performance. 2-3-4 Higher input
variability than the study Tune existing controls for improved
performance. average. 2-4-4 Excessively high input variability.
Tune existing controls and reduce input variability. 3-1-1
Excellent low input variability. Factors not measured by this study
are affecting performance. 3-2-1 Good low input variability.
Factors not measured by this study are affecting performance. 3-3-1
Higher input variability than the study Reduce input variability to
improve performance. average. 3-4-1 Excessively high input
variability. Reduce input variability to improve performance. 3-1-2
Excellent low input variability. Factors not measured by this study
are affecting performance. 3-2-2 Good low input variability.
Factors not measured by this study are affecting performance. 3-3-2
Higher input variability than the study Reduce input variability to
improve performance. average. 3-4-2 Excessively high input
variability. Reduce input variability to improve performance. 3-1-3
Excellent low input variability. Tune existing controls then
consider improved control applications. 3-2-3 Good low input
variability. Tune existing controls then consider improved control
applications. 3-3-3 Higher input variability than the study Reduce
input variability, tune existing controls, consider average.
control applications. 3-4-3 Excessively high input variability.
Reduce input variability, tune existing controls, consider control
applications. 3-1-4 Excellent low input variability. Tune existing
controls and consider control application improvements. 3-2-4 Good
low input variability. Tune existing controls and consider control
application improvements. 3-3-4 Higher input variability than the
study Reduce input variability, tune existing controls, consider
average. control applications. 3-4-4 Excessively high input
variability. Reduce input variability, tune existing controls,
consider control applications. 4-1-1 Excellent low input
variability. Factors not measured by this study are affecting
performance.
4-2-1 Good low input variability. Factors not measured by this
study are affecting performance. 4-3-1 Higher input variability
than the study Reduce input variability to improve performance.
average. 4-4-1 Excessively high input variability. Reduce input
variability to improve performance. 4-1-2 Excellent low input
variability. Factors not measured by this study are affecting
performance. 4-2-2 Good low input variability. Factors not measured
by this study are affecting performance. 4-3-2 Higher input
variability than the study Reduce input variability for additional
performance. average. 4-4-2 Excessively high input variability.
Reduce input variability for additional performance. 4-1-3
Excellent low input variability. Tune existing controls then
consider improved control applications. 4-2-3 Good low input
variability. Tune existing controls then consider improved control
applications. 4-3-3 Higher input variability than the study Reduce
input variability, tune existing controls and consider average.
control applications. 4-4-3 Excessively high input variability.
Reduce input variability, tune existing controls and consider
control applications.. 4-1-4 Excellent low input variability. Tune
existing controls and consider control application improvements
4-2-4 Good low input variability. Tune existing controls and
consider control application improvements. 4-3-4 Higher input
variability than the study Reduce input variability, tune existing
controls and consider average. control applications. 4-4-4
Excessively high input variability. Reduce input variability, tune
existing controls and consider control applications.
* * * * *