U.S. patent application number 11/224844 was filed with the patent office on 2007-03-15 for dynamic constrained optimization of chemical manufacturing.
This patent application is currently assigned to Pavilion Technologies, Inc.. Invention is credited to Timothy Morrison, Michael Sugars.
Application Number | 20070059838 11/224844 |
Document ID | / |
Family ID | 37600886 |
Filed Date | 2007-03-15 |
United States Patent
Application |
20070059838 |
Kind Code |
A1 |
Morrison; Timothy ; et
al. |
March 15, 2007 |
Dynamic constrained optimization of chemical manufacturing
Abstract
System and method for chemical manufacture utilizing a dynamic
optimizer for a chemical process including upstream and downstream
processes. The dynamic optimizer includes a maximum feed
calculator, operable to receive one or more local constraints on
the downstream processes and one or more model offsets, and execute
steady state models for the downstream processes in accordance with
the local constraints and the offsets to determine maximum feed
capacities of the downstream processes; and a feed coordinator,
operable to receive the maximum feed capacities, and execute steady
state models for the upstream processes in accordance with the
maximum feed capacities and a specified objective function, subject
to global constraints, to determine upstream production parameters
for the upstream processes, which are usable to control the
upstream processes to provide feeds to the downstream processes in
accordance with the determined maximum feeds and the objective
function subject to the global constraints.
Inventors: |
Morrison; Timothy; (Houston,
TX) ; Sugars; Michael; (Elgin, TX) |
Correspondence
Address: |
MEYERTONS, HOOD, KIVLIN, KOWERT & GOETZEL, P.C.
700 LAVACA, SUITE 800
AUSTIN
TX
78701
US
|
Assignee: |
Pavilion Technologies, Inc.
|
Family ID: |
37600886 |
Appl. No.: |
11/224844 |
Filed: |
September 13, 2005 |
Current U.S.
Class: |
436/55 |
Current CPC
Class: |
Y02P 80/40 20151101;
F25J 2215/62 20130101; F25J 2215/64 20130101; Y02P 90/80 20151101;
F25J 2270/60 20130101; F25J 2270/02 20130101; F25J 2280/50
20130101; F25J 2270/12 20130101; Y10T 436/12 20150115; F25J 3/0242
20130101; Y02P 90/02 20151101; F25J 2215/66 20130101; F25J 3/0295
20130101; F25J 3/0238 20130101; F25J 2270/88 20130101; F25J 3/0233
20130101; G05B 19/41865 20130101; G05B 13/048 20130101; Y02P 90/20
20151101; F25J 3/0219 20130101; F25J 3/0247 20130101; Y02P 90/86
20151101; F25J 2210/12 20130101 |
Class at
Publication: |
436/055 |
International
Class: |
G01N 35/08 20060101
G01N035/08 |
Claims
1. A method for chemical manufacture, comprising: determining
maximum feed capacities of a plurality of downstream processes in a
chemical plant subject to one or more local constraints on the
downstream processes; determining upstream production parameters
for the plurality of upstream processes in accordance with the
determined maximum feed capacities and a specified objective
function, subject to one or more global constraints; and
controlling the plurality of upstream processes in the chemical
plant in accordance with the determined upstream production
parameters, thereby facilitating maximum production by the chemical
plant in accordance with the specified objective function.
2. The method of claim 1, wherein the specified objective function
comprises an optimal product mix for the chemical plant, wherein
said determining upstream production parameters is further based on
the determined optimal product mix.
3. The method of claim 2, wherein the specified objective function
further comprises product prices.
4. The method of claim 1, wherein the chemical plant comprises an
olefins production plant.
5. The method of claim 4, wherein the optimal product mix specifies
one or more of: ethylene production setpoint(s); propylene
production setpoint(s); and one or more C4+ production
setpoints.
6. The method of claim 1, wherein the plurality of downstream
processes comprise separation and purification processes; and
wherein the plurality of upstream processes comprise a plurality of
reactors.
7. The method of claim 6, wherein said determining maximum feed
capacities, said determining upstream production parameters, and
said controlling the plurality of upstream processes implement
overall chemical plant steady state optimization via a
multivariable predictive dynamic controller adjusting operational
targets in the reactors, including target feed rates, to achieve
optimal feed rates and product mix in the separation and
purification processes.
8. The method of claim 7, wherein the specified objective function
comprises an optimal product mix for the chemical plant, the method
further comprising: performing local steady state optimization of
the separation and purification processes to optimize operation of
the separation and purification processes at the determined maximum
feed capacities and in accordance with the optimal product mix.
9. The method of claim 7, further comprising: determining a
predicted trajectory of optimal feed rates for the separation and
purification processes; and providing the predicted trajectory as
input to one or more multivariable predictive dynamic controllers
controlling the separation and purification processes.
10. The method of claim 7, wherein the plurality of upstream
processes are controlled by one or more upstream multivariable
predictive dynamic controllers, the method further comprising:
performing local steady state optimization in the upstream
multivariable predictive dynamic controllers to optimize operation
of the plurality of reactors at the target feed rate.
11. The method of claim 6, wherein the separation and purification
processes comprise one or more of: distillation columns; and
refrigeration units.
12. The method of claim 6, wherein the plurality of reactors
comprise a plurality of furnaces.
13. The method of claim 1, wherein said controlling the plurality
of upstream processes comprises controlling one or more of: feeds
to the plurality of upstream processes; and operating parameters
for the plurality of upstream processes.
14. The method of claim 1, wherein said determining the maximum
feed capacities of the plurality of downstream processes comprises:
receiving one or more local constraints and one or more model
offsets; and executing one or more steady state models for the
downstream processes in accordance with the one or more local
constraints and the one or more model offsets to determine the
maximum feed capacities of the plurality of downstream
processes.
15. The method of claim 14, wherein the plurality of downstream
processes are controlled by one or more downstream multivariable
predictive dynamic controllers comprising respective steady state
models of the downstream processes, and wherein said executing the
one or more steady state models comprises: executing the steady
state models of the one or more downstream multivariable predictive
dynamic controllers in optimizer mode.
16. The method of claim 15, wherein the one or more local
constraints and the one or more model offsets are provided by the
one or more downstream multivariable predictive dynamic
controllers.
17. The method of claim 16, wherein the one or more local
constraints comprise one or more of: one or more product targets
for the plurality of downstream processes; one or more limits for
the plurality of downstream processes.
18. The method of claim 15, wherein said executing the one or more
steady state models comprises: executing the steady state models of
the one or more downstream multivariable predictive dynamic
controllers in combination under combinatorial constraints in
accordance with connectivity among the plurality of downstream
processes.
19. The method of claim 14, wherein said executing the one or more
steady state models in accordance with the one or more model
offsets comprises adjusting outputs of the steady state models in
accordance with the model offsets to match outputs of the chemical
plant.
20. The method of claim 14, further comprising: determining the one
or more model offsets by comparing outputs of the one or more
steady state models and outputs of the chemical plant.
21. The method of claim 14, wherein said determining the maximum
feed capacities of the plurality of downstream processes comprises:
executing combined steady state models of the one or more
downstream multivariable dynamic controllers in an optimizer.
22. The method of claim 14, wherein each of the steady state models
comprises one or more of: a neural network; and a support vector
machine.
23. The method of claim 14, wherein the steady state models
comprise neural networks, wherein the neural networks are based on
physical models of the downstream processes.
24. The method of claim 14, wherein the steady state models
comprise neural networks, wherein the neural networks are based on
empirical models of the downstream processes.
25. The method of claim 1, wherein said determining the upstream
production parameters for the plurality of upstream processes
comprises one or more of: determining target feed rates for
multiple types of feedstocks for the plurality of upstream
processes; determining cracking severity for each of the upstream
processes.
26. The method of claim 25, wherein said determining target feed
rates comprises determining the target feed rates based on one or
more of: a specified priority; and economic value of resulting
products.
27. The method of claim 1, wherein said controlling the plurality
of upstream processes in the chemical plant in accordance with the
determined upstream production parameters comprises: each of a
plurality of upstream multivariable predictive dynamic controllers
controlling a respective reactor.
28. The method of claim 1, wherein the one or more global
constraints comprise one or more of: total feedstock flow of each
of a plurality of feedstocks; ratio of each of the plurality of
feedstocks to the total feed; and maximum difference between
individual upstream feeds and the average of the upstream
feeds.
29. The method of claim 1, further comprising: performing said
determining maximum feed capacities, said determining upstream
production parameters, and said controlling the plurality of
upstream processes in an iterative manner to dynamically optimize
operation of the chemical plant.
30. The method of claim 1, wherein the chemical plant comprises a
polymer production plant.
31. A dynamic optimizer for a chemical process comprising a
plurality of upstream processes and a plurality of downstream
processes, comprising: a maximum feed calculator, operable to:
receive one or more local constraints and one or more model
offsets; execute one or more steady state models for the downstream
processes in accordance with the one or more local constraints on
the plurality of downstream processes and the one or more model
offsets to determine maximum feed capacities of the plurality of
downstream processes; and a feed coordinator, operable to: receive
the determined maximum feed capacities of the plurality of
downstream processes; and execute one or more steady state models
for the upstream processes in accordance with the determined
maximum feed capacities and a specified objective function, subject
to one or more global constraints, to determine upstream production
parameters for the plurality of upstream processes; wherein the
determined upstream production parameters are usable to control the
plurality of upstream processes to provide feeds to the plurality
of downstream processes in accordance with the determined maximum
feeds and the specified objective function subject to the one or
more global constraints.
32. A memory medium that stores program instructions for
controlling a chemical process, wherein the program instructions
are computer executable to perform: determining maximum feed
capacities of a plurality of downstream processes in a chemical
plant subject to one or more local constraints on the downstream
processes; determining upstream production parameters for the
plurality of upstream processes in accordance with the determined
maximum feed capacities and a specified objective function, subject
to one or more global constraints; and controlling the plurality of
upstream processes in the chemical plant in accordance with the
determined upstream production parameters, thereby facilitating
maximum production by the chemical plant in accordance with the
specified objective function.
Description
FIELD OF THE INVENTION
[0001] The present invention generally relates to the field of
chemical production. More particularly, the present invention
relates to systems and methods for optimizing chemical production
in a manufacturing process with downstream and/or upstream
constraints using predictive control methodologies.
DESCRIPTION OF THE RELATED ART
[0002] Like any other commercial enterprise, those in the business
of producing chemical products desire to maximize efficiencies and
profitability, while meeting various constraints, such as, for
example, raw material and energy costs, plant equipment
limitations, product prices, and so forth. The ability to produce
chemicals in such a manner may be further complicated for chemical
plants producing more than one grade or type of chemical
product.
[0003] As shown in prior art FIG. 1, a chemical plant 104 may
produce chemicals, including, for example, olefin, gasoline, and
fuel oil, among others, of varying grades, from feedstock, e.g.,
naphtha, heavy oil, liquefied petroleum gas (LPG), and ethane,
among others. Typically, a chemical plant 104 includes reactors
(e.g., furnaces) that make the product followed by separation
equipment such as distillation columns to recover the product. The
reactors/furnaces are generally referred to as the "hot side" or
"hot section" of the plant or plant unit, while the separation
portion is referred to as the "cold side" or "cold section" of the
plant or unit. It should be noted, however, that these terms are
not intended to limit the application of the techniques disclosed
herein to any particular chemical process. Rather, the various
techniques described are contemplated as being broadly applicable
to any process that includes an upstream process, e.g., a reactor
process, and a downstream process, e.g., a separation process.
[0004] A chemical plant 104 may employ one or more processing lines
that are capable of transforming raw materials 101 into chemical
products 103, e.g., olefin, gasoline, fuel oil, etc. One processing
line may be capable of producing two or more different grades of
chemicals, or even two or more different types of chemicals. For
example, production of a first product, e.g., olefin, may also
result in production of a second product, e.g., ethane, as a
byproduct or impurity. This secondary product may itself be
valuable, e.g., as a saleable product, or as feedstock for further
processing.
[0005] Such systems and processes, especially those that utilize
multiple feed/product streams, are characterized by the fact that
many different inter-related parameters contribute to the behavior
of the system or process. It is often desirable to determine values
or ranges of values for some or all of these parameters that
correspond to beneficial behavior patterns of the system or
process, such as safety, profitability, efficiency, etc. However,
the complexity of most real world systems generally precludes the
possibility of arriving at such solutions analytically, i.e., in
closed form. Therefore, many analysts have turned to predictive
models and optimization techniques to characterize and derive
solutions for these complex systems or processes.
[0006] Predictive models generally refer to any representation of a
system or process that receives input data or parameters related to
system or model attributes and/or external
circumstances/environment and generates outputs indicating the
behavior of the system or process under those parameters. In other
words, the model or models may be used to predict behavior or
trends based upon previously acquired data. There are many types of
predictive models, including linear, non-linear, analytic, and
empirical models, among others, several types of which are
described in more detail below.
[0007] Optimization generally refers to a process whereby past (or
synthesized) data related to a system or process are analyzed or
used to select or determine optimal parameter sets for operation of
the system or process. For example, the predictive models mentioned
above may be used in an optimization process to test or
characterize the behavior of the system or process under a wide
variety of parameter values. The results of each test may be
compared, and the parameter set or sets corresponding to the most
beneficial outcomes or results may be selected for implementation
in the actual system or process.
[0008] FIG. 2A illustrates a general optimization process as
applied to an industrial process 104, such as a manufacturing
plant, according to the prior art. It may be noted that the
optimization techniques described with respect to the manufacturing
plant are generally applicable to all manner of systems and
processes.
[0009] As FIG. 2A shows, the operation of the process 104 generates
information or data 106 that is typically analyzed and/or
transformed into useful knowledge 108 regarding the system or
process. For example, the information 106 produced by the process
104 may comprise raw production numbers for the plant that are used
to generate knowledge 108, such as profit, revenue flow, inventory
depth, etc. This knowledge 108 may then be analyzed in the light of
various goals and objectives 112 and used to generate decisions 110
related to the operation of the system or process 104 subject to
various goals and objectives 112 specified by the analyst. As used
herein, an "objective" may include a goal or desired outcome of an
optimization process. Example goals and objectives 112 may include
or involve profitability, schedules, energy use, inventory levels,
cash flow, production, or any other attribute that the user may
wish to minimize or maximize. These goals and objectives 112 may be
used to select from among the possible decisions 110, where the
decisions may comprise various parameter values over which the user
may exercise control. The selected decision(s) may then determine
one or more actions 114 to be applied to the operation of the
system or process 104. The subsequent operation of the system or
process 104 then generates more information 106, from which further
knowledge 108 may be generated, and so on in an iterative fashion.
In this way, the operation of the process 104 may be "tuned" to
perform in a manner that most closely meets the goals and
objectives of the business or enterprise.
[0010] FIG. 2B illustrates an optimization system where a computer
based optimization system 102 operates in conjunction with a
process 104 to optimize the process, according to the prior art. In
other words, the computer system 102 executes software programs
(including computer based predictive models) that receive process
data 106 from the process 104 and generate optimized decisions
and/or actions that may then be applied to the process 104 to
improve operations based on the goals and objectives.
[0011] Thus, many predictive systems may be characterized by the
use of an internal model that represents a process or system 104
for which predictions are made. FIG. 3A illustrates a number of
predictive model types usable in optimization systems, according to
the prior art. As mentioned above, predictive model types may be
linear, non-linear, stochastic, or analytical, among others.
However, for complex phenomena non-linear models may generally be
preferred due to their ability to capture non-linear dependencies
among various attributes of the phenomena. Examples of non-linear
models may include neural networks and support vector machines
(SVMs).
[0012] As FIG. 3A shows, the types of models used in optimization
systems include fundamental or analytic models 302 that use known
information about the process 104 to predict desired unknown
information, such as product conditions and product properties. A
fundamental model may be based on scientific and engineering
principles. Such principles may include the conservation of
material and energy, the equality of forces, and so on. These basic
scientific and engineering principles may be expressed as equations
that are solved mathematically or numerically, usually using a
computer program. Once solved, these equations may give the desired
prediction of unknown information.
[0013] Conventional computer fundamental models have significant
limitations, such as:
[0014] (1) They may be difficult to create since the process may be
described at the level of scientific understanding, which is
usually very detailed;
[0015] (2) Not all processes are understood in basic engineering
and scientific principles in a way that may be computer
modeled;
[0016] (3) Some product properties may not be adequately described
by the results of the computer fundamental models; and
[0017] (4) The number of skilled computer model builders is
limited, and the cost associated with building such models is thus
quite high.
[0018] These problems result in computer fundamental models being
practical only in some cases where measurement is difficult or
impossible to achieve.
[0019] As also shown in FIG. 3A, empirical models 304, also
referred to as computer-based statistical models, may be used to
model the system or process 104 in an optimization system. Such
models typically use known information about process to determine
desired information that may not be easily or effectively measured.
A statistical empirical model may be based on the correlation of
measurable process conditions or product properties of the process.
Examples of computer-based empirical or statistical models include
neural networks and support vector machines (SVMs).
[0020] For one example of a use of a computer-based statistical
model, assume that it is desired to be able to predict the color of
a plastic product. This is very difficult to measure directly, and
takes considerable time to perform. In order to build a
computer-based statistical model that may produce this desired
product property information, the model builder would need to have
a base of experience, including known information and actual
measurements of desired unknown information. For example, known
information may include the temperature at which the plastic is
processed. Actual measurements of desired unknown information may
be the actual measurements of the color of the plastic.
[0021] A mathematical relationship (i.e., an equation) between the
known information and the desired unknown information may be
created by the developer of the empirical statistical model. The
relationship may contain one or more constants (which may be
assigned numerical values) that affect the value of the predicted
information from any given known information. A computer program
may use many different measurements of known information, with
their corresponding actual measurements of desired unknown
information, to adjust these constants so that the best possible
prediction results may be achieved by the empirical statistical
model. Such a computer program, for example, may use non-linear
regression.
[0022] Computer-based statistical models may sometimes predict
product properties that may not be well described by computer
fundamental models. However, there may be significant problems
associated with computer statistical models, which include the
following:
[0023] (1) Computer statistical models require a good design of the
model relationships (i.e., the equations) or the predictions may be
poor;
[0024] (2) Statistical methods used to adjust the constants
typically may be difficult to use;
[0025] (3) Good adjustment of the constants may not always be
achieved in such statistical models; and
[0026] (4) As is the case with fundamental models, the number of
skilled statistical model builders is limited, and thus the cost of
creating and maintaining such statistical models is high.
[0027] As FIG. 3A also shows, predictive model types also include
procedural or recipe based models 306. These models typically
comprise a number of steps whose performance emulates or models the
phenomenon or process. Thus, procedural or recipe models are not
based on understanding of the fundamental processes of a system,
but instead, are generally constructed with an empirical or
emulative approach.
[0028] Generally, a model is trained with training data, e.g.,
historical data, in order to reflect salient attributes and
behaviors of the phenomena being modeled. In the training process,
sets of training data may be provided as inputs to the model, and
the model output may be compared to corresponding sets of desired
outputs. The resulting error is often used to adjust weights or
coefficients in the model until the model generates the correct
output (within some error margin) for each set of training data.
The model is considered to be in "training mode" during this
process. After training, the model may receive real-world data as
inputs, and provide predictive output information that may be used
to control or make decisions regarding the modeled phenomena.
[0029] In one application of optimization techniques, predictive
models may be used by a controller (or even a human decision-maker)
to select an optimal course of action or optimal course of
decision. The optimal course of action or decision may include a
sequence or combination or actions and/or decisions. For example,
optimization may be used to select an optimal course of action for
production of one or more products.
[0030] As used herein, "control variables" (or "controlled
variables") (CVs) are those variables that the controller or
decision-maker tries to bring to some objective--e.g. target,
maxima, etc. As used herein, "manipulated variables" (MVs) are
those variables that the controller or decision-maker may change to
affect the outcome of the optimization process 100, specifically,
to achieve the objectives for the CVs. For example, in an
optimization of a credit card offer conducted by a credit card
issuer, Annual Percentage Rate (APR) and credit limit may be
manipulated variables. As used herein, "disturbance variables"
(DVs) are those variables that are not under the control of the
controller or decision-maker. In other words, the disturbance
variables are not changed in the decision process but rather are
taken as givens. For example, in the credit card issuer example,
disturbance variables may include variables such as customer
addresses, customer income levels, customer demographic
information, bureau data, transaction file data, cost of funds and
capital, and other suitable variables. As another example, in a
chemical plant application, disturbance variables may include
variables such as market prices of raw materials or products,
ambient conditions, energy costs or availability, and so forth.
[0031] In general, the process of optimization may include
determining which variables in a particular problem are most
predictive of a desired outcome, and what treatments, actions, or
mix of variables under the controller's or decision-maker's control
(i.e., manipulated variables) may optimize the specified value. For
example, in a chemical plant, optimal temperatures, feed mixes,
etc., may be applied to maximize production of saleable
products.
[0032] FIG. 3B is a block diagram of a predictive model 315 as used
in an optimization system 100, according to the prior art. As FIG.
3B shows, the model 315 may receive input in the form of
disturbance variables 312 and manipulated variables 314, defined
above, and generate action variables 318. As used herein, "action
variables" are those variables that propose or suggest a set of
actions for an input set of decision and disturbance variables. In
other words, the action variables may comprise predictive metrics
for a behavior. For example, in the optimization of chemical
production, the action variables may include the amount of saleable
product per unit of feedstock. In the optimization of a credit card
offer, the action variables may include predictions of balance,
attrition, charge-off, purchases, payments, and other suitable
behaviors for the customer of a credit card issuer.
[0033] Thus, predictive models may be used for analysis, control,
and decision making in many areas, including manufacturing, process
control, plant management, quality control, optimized decision
making, e-commerce, financial markets and systems, or any other
field where predictive modeling may be useful.
[0034] FIGS. 4A and 4B illustrate a general optimization system and
process using predictive models with an optimizer to generate
optimal manipulated variables, according to the prior art.
[0035] FIG. 4A is a block diagram that illustrates an overview of
optimization according to the prior art. As shown in FIG. 4A, an
optimization process 100 may accept the following elements as
input: information 402, such as customer information records 402,
predictive model(s) such as customer model(s) 404, one or more
constraints and/or objectives 406. As used herein, a "constraint"
may include a limitation on the outcome of an optimization process.
Constraints are typically "real-world" limits on the manipulated
variables and are often critical to the feasibility of any
optimization solution. For example, control of resources, energy,
and capital, or other factors germane to financial effects or
results may be involved in setting constraints that accurately
represent their real-world environments. Setting such constraints
may realistically restrict the allowable values for the manipulated
variables. The optimization process 100 may produce as output an
optimized set of manipulated variables 412.
[0036] FIG. 4B illustrates data flow in the optimization system of
FIG. 4A. As FIG. 4B shows, input information 202 typically includes
manipulated variables 214 and disturbance variables 212, as
described above. The information 402, including manipulated
variables 214 and disturbance variables 212, is input into the
predictive model(s) 404 to generate the action variables 218. The
predictive model(s) 404 may include process model(s) as well as
other models. The predictive model(s) 404 can take any of several
forms, as described above, including trained neural nets, support
vector machines, statistical models, analytic models, and any other
suitable models for generating predictive metrics, and may take
various forms including linear or non-linear, or may be derived
from empirical data or from managerial judgment.
[0037] As FIG. 4B shows, the action variables 318 generated by the
model(s) 404 are used to formulate constraint(s) and the objective
function 406 via formulas. For example, a data calculator 420
generates the constraint(s) and objective 406 using the action
variables 318 and potentially other data and variables. The
formulations of the constraint(s) and objective 406 may include
physical formulas such as formulas for determining reaction rates,
viscosity, melting points, and so forth. The constraint(s) and
objective 406 may be input into an optimizer 324, that may
comprise, for example, a custom-designed process or a commercially
available "off the shelf" product. The optimizer may then generate
the optimal manipulated variables 412 that have values optimized
for the goal specified by the objective function and subject to the
constraint(s) 406.
[0038] FIG. 5 illustrates a simplified optimization system
according to the prior art, where a computer based optimization
system 502 operates in conjunction with an enterprise or process
504 to optimize the process. In other words, the computer system
502 executes software programs (including computer based predictive
models) that receive process data from the process 504, as shown by
arrow 503 and generate optimized decisions and/or actions that may
then be applied to the process 504 to improve operations, as shown
by arrow 505. The dynamic model feedback 506, shown within the
process 504, allows real-time data to be used as input to generate
improved versions of optimized decisions and/or actions. In turn,
the improved versions of optimized decisions and/or actions may
then be applied to the process 504 to improve operations.
[0039] In traditional APC in an olefins plant, different
practitioners may group equipment differently in an effort to
account for process interactions and properly manage process
constraints. Various software products have been used successfully
to implement APC in olefins units, generally consisting of some
combination of dynamic and steady state models of the section of
the unit to be controlled, and manipulating model inputs to
maintain controlled variables at targets or within constraints.
Traditionally, optimization has involved very large, rigorous,
steady state models of the entire olefins unit. These models
attempt to capture all interactions among all process equipment. In
addition to the cost involved in developing the models, this
approach suffers from the following deficiencies:
[0040] a) The optimizer models rarely produce the same results as
the APC models, causing conflict between optimization goals and APC
goals.
[0041] b) Purely steady state models must be reconciled to the
actual process by adjusting parameters or model biases based on
comparisons at steady state conditions. This means the models can
only be executed when the unit is at steady state, which is a rare
condition for an olefins unit.
[0042] c) Most traditional optimization models are very susceptible
to instrumentation error. It is common to find a complex data
reconciliation function adjusting inputs to the model before it is
executed.
[0043] d) Optimum conditions are not fully implemented due to the
conflicts between the optimizer models and the APC models.
[0044] As an example, an olefins unit produces light olefins (C2
through C4 typically) by cracking of feedstocks in furnaces in the
presence of steam. The cracked gas is compressed, cooled, and sent
to the cold section for separation. The cold section consists
primarily of an integrated set of distillation columns and
refrigeration compressors. The composition of the furnace effluent,
or cracked gas, is determined primarily by feedstock composition,
furnace design, and cracking severity. Typically, there are several
furnaces in an olefins unit, each operated independently, often
utilizing different types of feedstock. Feedstocks are chosen based
on availability and cost.
[0045] FIG. 6 is a simplified olefins plant process flow diagram
illustrating basic processes of the hot and cold sections of an
exemplary olefins plant, according to the prior art. As FIG. 6
shows, in the hot section, feedstock, e.g. naphtha, butane,
propane, ethane, etc., may be provided to multiple hydrocarbon
cracking furnaces. As is well-known, these furnaces may operate to
crack the feedstocks, thereby generating a mixture of hydrocarbon
products. As indicated, an initial separation process may be
performed in the hot section using oil and water quenching to
generate heavy gasoline, and the byproducts provided as input to
the cold section. These byproducts may be processed by a series of
refrigeration/distillation units to extract various products from
the mix. For example, the byproducts may be provided to a
multistage compressor with condensate recovery, which output may
then be provided to a chilling train and demethanizer. As shown,
hydrogen and methane may thus be extracted and the byproducts
passed to a deethanizer, which may extract ethane and ethylene and
pass the mixture to an ethylene fractionator, which may extract
ethylene as a product, and recycle any remaining ethane, which is
then provided as feedstock to the hot side. Byproducts from the
deethanizer may be passed to a depropanizer, which extracts propane
and propylene and pass the mixture to a propylene fractionator,
which may then extract propylene and propane as products. The
byproducts of the depropanizer may be provided to a debutanizer,
which may produce mixed butanes and light gasoline as products.
[0046] Note that the cold section is designed to recover products
for sale or for use in downstream chemical manufacturing plants.
For example, fuel gas, a mixture of hydrogen and methane, is
recovered in the cold section and burned in the furnaces. Ethane is
recovered in the cold section and recycled as feedstock to the
furnaces. Ethane is recycled to extinction. Most products from the
cold section are saleable products with maximum impurity
specifications. Optimization of relative production rates is a
simple value function based on market price of the product and cost
of feedstock. Optimization of the distillation columns comprising
the cold section involves balancing energy use with the cost
associated with impurities in the top and bottom products. In
general, improving separation to reduce impurities requires more
energy. If both the top and bottom streams from a distillation
column are saleable products then the "optimum" operating point for
the column is at the maximum impurities allowed in both streams,
which is equivalent to the minimum energy use. These types of
columns are typically "optimized" by setting impurity targets in an
advanced process controller (APC). However, certain streams allow
variability in composition that could be exploited to reduce energy
consumption and also to produce additional quantities of more
valuable products. An example is the bottoms of an ethylene
fractionator separating ethylene from ethane. The ethane from the
bottom of the column is recycled to the furnaces and is typically
cracked at 60 to 70 percent conversion. When this column is the
bottleneck in the plant, allowing additional ethylene in the bottom
could result in additional total olefins production. But when this
is not the bottleneck, lower concentrations reduce ethylene losses
in the recycle and increase total olefins production. This is a
more complex optimization problem requiring knowledge of column
constraints and interactions with other process equipment. Prior
art approaches to process control and optimization have not
adequately addressed these issues.
SUMMARY
[0047] Various embodiments of a system and method for chemical
manufacture are presented. In one embodiment, a dynamic optimizer
may operate in conjunction with standard section control
applications to optimize a chemical process (in a chemical plant)
including upstream and downstream processes, e.g., a hot side and a
cold side, such as an olefins production plant. The dynamic
optimizer preferably includes two main parts: a maximum feed
calculator that operates to perform a maximum feed calculation for
each area of the cold section, and a feed coordinator, preferably
an integrated controller/optimizer (ICO) that specifies or
implements control strategies for the hot-side of the process,
e.g., setting targets for feed flow, cracking severity, etc., for
the hot-side ICOs. These two portions of the dynamic optimizer may
communicate with the standard section control applications, e.g.,
ICOs controlling furnaces on the hot-side, and ICOs controlling
equipment on the cold side of each processing unit. Note that each
ICO preferably includes a steady-state model of the process or
sub-process being controlled. Each steady-state model may include
steady-state offsets or biases that normalize the model's
predictions to actual operating values.
[0048] First, maximum feed capacities of a plurality of downstream
processes in a chemical plant may be determined subject to one or
more local constraints on the downstream processes. In other words,
a maximum feed calculation may be performed to determine maximum
feeds for each of the plurality of downstream processes, e.g., for
each cold-side section, e.g., for each of a plurality of
refrigeration units/distillation columns, taking into account one
or more limits on the processes, such as, for example, pressure
drop across a distillation column, valve positions, and
refrigeration compressor drive amperage, among others. In one
embodiment, the maximum feed calculation may be performed by an
analysis engine, such as a real-time analysis engine (RAE), in
conjunction with one or more steady-state models of the cold-side
processes, e.g., models comprised in respective cold-side ICOs.
[0049] Thus, the maximum feed calculation preferably uses
steady-state models from the cold side section ICOs. For example, a
downstream optimizer may execute the steady state models under a
variety of conditions or states to determine a solution (the
maximum feeds), as is well known in the art of constrained
optimization. Note that in various embodiments, respective ICOs
preferably control respective cold side units (e.g., distillation
columns), although in other embodiments, one or more ICOs may cover
multiple units, or, an ICO may cover the entire cold section. Each
ICO application is preferably configurable to improve operation of
individual units subject to local process constraints (e.g.,
maximum feed rates, etc.).
[0050] As is well known in the art of constrained optimization,
control (or controlled) variables (CVs) are variables that the ICO
maintains either at a setpoint or within maximum or minimum limits.
It accomplishes this by adjusting manipulated variables (MVs). In
other words, MVs are variables that the ICO adjusts, and are
typically the setpoints of distributed control system (DCS)
controllers. Disturbance variables (DVs) are those variables that
affect the process but are not adjustable by the controller itself.
Examples of CVs in a cold-side ICO include amount of methane in the
ethylene product, amount of ethane in the bottom of the
deethanizer, depropanizer pressure drop, and demethanizer reboil
valve position, among others. Examples of MVs in a cold section ICO
include reboil flow rate, reflux flow rate, ethylene compressor
suction pressure, and refrigerant level in a chilling train heat
exchanger, among others. Examples of DVs in a cold section ICO
include column feed rate, cooling water temperature, and column
pressure, among others.
[0051] In some embodiments, a typical cold side section ICO where
all product streams are saleable products may include targets for
impurities that are ideally set three standard deviations below
product specifications. Operating at these targets may minimize
energy consumption. Of course, in other embodiments, targets may be
set otherwise as desired. In some embodiments, a typical cold side
section ICO where one or more product streams are used internally
may contain additional degrees of freedom. In some cases the
trade-off between energy costs and the value of saleable product
recovered may be neither constant nor obvious. These trade-offs may
thus be most appropriately managed by the ICO application either
through local optimization or specific tuning of targets and
constraints.
[0052] The dynamic optimizer is primarily concerned with the
maximum feed rate each area of the cold side section can handle.
Typically, even stream compositions that are not fixed by product
specifications have some practical limit. Thus every cold side
section ICO, even those that allow stream compositions to vary, is
capable of calculating a maximum feed rate. The feed maximization
calculation preferably executes each ICO steady-state model in
optimization mode with the feed rate as an MV. If product pricing
is not used in the ICO the feed may be provided a price to
encourage the optimizer to maximize it.
[0053] The feed maximization calculation may be matched to the
plant by utilizing the feedback biases (e.g., steady state offsets)
from the ICOs. In this way, the calculation may be matched to the
plant by the controllers themselves, thus ensuring consistency
between the steady state feed maximization and control models.
Additionally, the CV and MV targets and limits used in the feed
maximization calculation are preferably obtained directly from the
ICOs.
[0054] The steady state models may be implemented using a variety
of approaches, including neural networks, support vector machines,
and so forth. One benefit of a neural network (NN) based ICO
architecture is that if the steady-state model is a neural network
model, it can be solved for outputs given a set of inputs, or
conversely, for inputs given a set of target outputs. Constraints
and prices may be applied to guide the solution. Additionally,
several NN models may be combined into one model. In the case of
the feed maximization calculation, the model may be solved for a
set of inputs (the controller MVs and DVs), which achieve maximum
feed rate while respecting the controller's CV targets and limits
and the MV limits. In preferred embodiments, feed is treated as a
manipulated variable in this calculation and assigned a positive
price, which encourages it to be increased. Note that in the case
where there are multiple ICOs in the cold side section, it may not
be necessary to combine models as long as the feed flow to each
section is included as a DV in that section's ICO.
[0055] In preferred embodiments, the model(s) may be run or
executed in a single instance of the maximum feed calculator. The
output of these calculations may include a set of feed flows for
each section that would allow it to achieve all targets but subject
to a limiting constraint. For example, if the section were a single
distillation column, the maximum feed would be that which allowed
maintaining the top and bottom target compositions while reaching
another constraint such as maximum reflux/reboil or maximum column
pressure drop.
[0056] In some embodiments, achieving a desired set of operating
modes may simply be a matter of configuration of the constraints
and targets. For example, if it is desired to minimize the bottom
composition when the column is not limiting but to allow this
composition to be sacrificed to some extent to achieve a higher
feed rate, the maximum allowable bottoms content may be passed as
the target to the feed maximization calculation while the lower
target may be used by the ICO during control calculations and tuned
to be sacrificed if necessary. Thus, each cold side ICO may provide
steady state (SS) biases and targets and/or limits to the RAE for
use in calculating maximum feed values for each cold side section.
The RAE (or functional equivalent) may then determine feed targets,
i.e., the maximum feed capacities, for each of the cold-side
sections, as described above.
[0057] Then, upstream production parameters for the plurality of
upstream processes may be determined in accordance with the
determined maximum feed capacities and a specified objective
function, subject to one or more global constraints. For example,
in an olefins production example, where the upstream process
comprise a plurality of furnaces (reactors), the upstream
production parameters may include one or more of: operating
temperatures, feed rates (for one or more types of feedstock) for
each furnace, or any other operating parameters or attributes
germane to operation of the upstream processes. Examples of an
objective function include (but are not limited to) profitability,
schedule, feedstock use, energy use or efficiency, optimal product
mix, and so forth. Note that in some embodiments, the objective
function may be more complicated, e.g., may be a combination of
different aspects, e.g., an optimal product mix based on product
pricing, or any other function as desired. In one embodiment, the
optimal product mix may specify one or more of: ethylene production
setpoint(s), propylene production setpoint(s), and one or more C4+
production setpoints, among others. Examples of global constraints
include (but or not limited to) total feedstock flow of each of a
plurality of feedstocks, the ratio of each of the plurality of
feedstocks to the total feed, and the maximum difference between
individual upstream feeds and the average of the upstream feeds,
among others.
[0058] In preferred embodiments, the upstream production parameters
may be determined via constrained optimization techniques. For
example, similar to the downstream constrained optimization process
described above, an upstream optimizer may execute a plurality of
steady state models of the upstream processes under a variety of
conditions or states to determine a solution (the upstream
production parameters). Note that in various embodiments,
respective ICOs preferably control respective upstream (hot side)
units (e.g., furnaces), although in other embodiments, one or more
ICOs may cover multiple units, or, an ICO may cover the entire hot
section. Each ICO application is preferably configurable to improve
operation of individual units subject to local process constraints.
Thus, the RAE may transmit cold side section feed targets to the
feed coordinator. The feed coordinator may receive additional
information regarding feed availability (feed available switches)
for each furnace, and may determine hot-side targets (i.e., CVs),
e.g., feed flow, cracking severity, etc., for each furnace ICO.
[0059] Note that in preferred embodiments, the maximum feeds to
each area of the cold section may be determined by executing the
steady state models of the cold side ICOs in optimization mode with
the feeds treated as manipulated variables (MVs). Appropriate
prices may be set so that the optimizer will increase component
feeds in proportion to their values until all degrees of freedom in
the APC are used. The steady-state model for each cold side ICO may
be matched to the plant by mapping the ICO steady-state biases to
the appropriate biases in the steady-state model. MV and controlled
variable (CV) limits and targets may also be mapped to the
steady-state model. In performing the feed maximization calculation
for a section, the maximum feed calculator may use the same model
as the section's ICO, thus ensuring consistency. This approach may
guarantee that each section's controller is capable of maintaining
its CV targets at the feed rate and feed composition delivered by
the feed coordinator.
[0060] The feed coordinator is thus preferably an ICO that may
operate as a "master" controller for the furnace ICOs, setting
their feed and severity targets. The primary goal is to achieve the
maximum feed for each area of the cold section subject to furnace
constraints and feedstock availability. A significant advantage of
using a dynamic controller in the feed maximization is the use of
dynamic models, which may allow closer approach to the maximum feed
limit for each downstream section.
[0061] As noted above, a primary objective of the present invention
is to achieve the maximum allowable feed to each downstream section
of the plant. Note that if the ICO is allowed to move multiple
feedstocks and/or furnace severities, it may be possible to achieve
multiple downsteam constraints. Thus, the feed coordinator may set
targets for the furnace ICO applications to control the cold side
section feed rates to the maximum feed targets (determined from the
feed maximization calculation). The targets set by the feed
coordinator may include feed and/or severity targets, among others.
Severities and/or feed types may be adjusted to vary the relative
flow to different sections so as to maximize the feed to each
section until all degrees of freedom are used. In some embodiments,
switches in each furnace controller may allow the operator (or
controller) to determine which feeds are available to be
adjusted.
[0062] The feed coordinator may include dynamic models between the
furnace MVs and the cold side section feeds. The maximum cold side
section feeds (determined from the feed maximization calculation)
may be the primary CVs for the feed coordinator. The feed
coordinator may be configured to achieve a variety of objectives.
For example, if it is desired to maximize only one feed type, then
only furnaces running that feed type may be put under feed
coordinator control. If multiple feed types are to be maximized,
all furnaces with those feed types may be put under feed
coordinator control.
[0063] In some embodiments, with no other configuration provided,
the feed coordinator may tend to increase the feed that gives the
greatest increase in the section feeds. Priority may be given to
the different feeds via the use of different tuning parameters.
However, in some embodiments, for maximum benefit it may be better
not to enforce any particular priority, thus allowing the
controller to determine the combination of feeds that best fills
the capacity of all sections of the plant subject to the available
degrees of freedom.
[0064] If more control over the solution is desired, constraints
and/or targets may be added to or specified for the controller.
Standard CVs for controlling the solution may include (but are not
limited to):
[0065] Maximum difference between individual furnace feeds and the
average--Setting this target at zero may tend to keep all furnaces
at the same feed rate. The actual difference may deviate from zero
as individual furnaces become limited but there may be some penalty
for allowing the difference to become very large. Alternatively,
this variable may be kept between limits, thus allowing the
controller more freedom to shift feeds but still remain within
acceptable bounds.
[0066] Each feedstock total as a percentage of the total--This
variable may be useful for cases where it is desired to maintain a
given feedstock as a percentage of the total by setting a desired
target or to keep that percentage between some bounds using minimum
and maximum fuzzy limits. In some embodiments, target feed rates
may be determined based a specified priority, e.g., from a user or
external process, and/or the economic value of resulting
products.
[0067] Total flow of each feed type--This variable may be used to
ensure that the solution consumes at least the minimum amount of a
feedstock but not more than is available. For a given feed type,
the change in cold side section feeds for a unit change in furnace
feed may be the same. Consequently, all furnaces of that feed type
may tend to be moved equivalently.
[0068] Specific olefins production rates--This variable may be used
to allow the dynamic optimizer to achieve target production rates
when 100% plant utilization is not desired, and may also provide a
means for maximizing the more valuable olefins (for example)
streams when all areas of the cold side section can not be loaded.
This may be particularly useful when the throughput is limited by
furnace operation. The setpoints may be tuned based on relative
prices of products. If it is anticipated that these will change
often, then tuning parameters may be calculated based on prices
read from an external database. Optionally, setpoints may be
replaced with a combination of min/max constraints and prices
(e.g., read from an external database).
[0069] Then, the plurality of upstream processes in the chemical
plant may be controlled in accordance with the determined upstream
production parameters, thereby facilitating production of the
optimal product mix by the chemical plant in accordance with the
specified objective. In other words, the upstream process, e.g.,
furnaces, may be operated in a manner that makes maximum use of the
downstream processes in attempting to meet the specified objective
subject to various constraints (e.g., local and/or global). Said
another way, the determining maximum feed capacities, the
determining upstream production parameters, and the controlling the
plurality of upstream processes may implement overall chemical
plant steady state optimization via one or more multivariable
predictive dynamic controllers adjusting operational targets in the
reactors, including target feed rates, to achieve optimum feed
rates and product mix in the separation and purification processes.
The hot-side ICOs may then operate in accordance with the provided
targets, moving hot-side MVs as needed to meet the targets. In this
manner, the furnaces may be operated in such as way as to maximize
feeds to the cold side sections in accordance with the maximum feed
calculations of the RAE.
BRIEF DESCRIPTION OF THE DRAWINGS
[0070] A better understanding of the present invention can be
obtained when the following detailed description of various
embodiments is considered in conjunction with the following
drawings, in which:
[0071] FIG. 1 illustrates an industrial process, according to the
prior art;
[0072] FIG. 2A illustrates a general optimization process as
applied to an industrial process 104, such as a manufacturing
plant, according to the prior art;
[0073] FIG. 2B illustrates an optimization system where a computer
based optimization system 102 operates in conjunction with a
process to optimize the process, according to the prior art;
[0074] FIG. 3A illustrates a number of predictive model types
usable in optimization systems, according to the prior art;
[0075] FIG. 3B is a block diagram of a predictive model as used in
an optimization system 100, according to the prior art;
[0076] FIGS. 4A and 4B illustrate a general optimization system and
process using predictive models with an optimizer to generate
optimal manipulated variables, according to the prior art;
[0077] FIG. 5 illustrates an optimization system where a computer
based optimization system operates in conjunction with a process
and a dynamic model feedback to optimize the process, according to
the prior art;
[0078] FIG. 6 illustrates an exemplary system for olefins
manufacture, according to the prior art;
[0079] FIG. 7 illustrates a system for control and optimization of
a chemical manufacturing plant, according to one embodiment;
and
[0080] FIG. 8 is a flowchart diagram of a method for dynamic
optimization of a chemical manufacturing process, according to one
embodiment.
DETAILED DESCRIPTION OF SEVERAL EMBODIMENTS
Incorporation by Reference
[0081] U.S. application Ser. No. 09/827,838 titled "System and
Method for Enterprise Modeling, Optimization and Control" and filed
Apr. 5, 2001, whose inventors are Edward Stanley Plumer, Bijan
Sayyar-Rodsari, Carl Anthony Schweiger, Ralph Bruce Ferguson II,
William Douglas Johnson, and Celso Axelrud, is hereby incorporated
by reference as though fully and completely set forth herein.
[0082] U.S. application Ser. No. 10/225,093 titled "System and
Method for Real-Time Enterprise Optimization" and filed Aug. 21,
2002, whose inventors are Robert S. Golightly, John P. Havener, Ray
D. Johnson, James D. Keeler and Ralph B. Ferguson, is hereby
incorporated by reference as though fully and completely set forth
herein.
Terms
[0083] Capacity--Capacity is the established maximum production
rate of the process or unit under best operating conditions (no
abnormal constraints). Capacity is a constant within the present
capital investment. For new units it is the vendor's specified
capacity. For established units, capacity is established by
demonstrated historical production rates.
[0084] Constraints--Constraints represent limitations on particular
operating variables or conditions that affect the achievable
production rate of a production unit. Constraints are of two types:
Controllable and External, defined below. Constraints come in many
forms including but not limited to the list below:
[0085] 1. Safety constraints that ensure the safety of equipment
and personnel.
[0086] 2. Bottleneck constraints such as the maximum open position
of a control valve or other equipment limitations to the physical
throughput of the unit.
[0087] 3. Equipment availability and readiness due to maintenance
planning and scheduling or due to unexpected equipment
casualties.
[0088] 4. Authorized production level set by the supply chain and
production scheduling systems.
[0089] 5. Personnel constraints on the availability of staffing and
support functions.
[0090] 6. Business rules and constraints imposed by contract and
policy.
[0091] 7. Business execution constraints imposed by the time
required to execute associated business and contractual tasks and
obligations.
[0092] 8. Control constraints on the maximal position and rate of
change of manipulated variables.
[0093] 9. Supply chain constraints on the availability of raw
materials, energy and production supplies.
[0094] 10. Environmental Permit and Legal constraints on air
emissions, waste water, and waste disposal systems.
[0095] 11. Environmental constraints imposed upon the performance
of the unit such as river levels and current weather imposed
limitations.
[0096] Controllable Constraints--Controllable constraints are
constraints imposed on the performance of the process or unit over
which the management of the process or unit does have authority and
discretionary control. As an example, the maximum throughput in a
dryer might be constrained by outlet pressure, in turn limited by
bag house back pressure. The bag house back pressure is a function
of how often the filters are changed, and it is at management's
discretion of when to change the filters, thereby changing the
throughput constraint.
[0097] External Constraints--External constraints are limitations
imposed on the performance of the process or unit over which the
management of the process or unit does not have authority or
discretionary control. These external constraints come in two
types: external constraints that are controllable by other entities
or processes in the plant or in the supply chain, and those
constraints that are imposed by physical, safety, environmental, or
legal constraints and are not controllable by anyone in the plant
or supply chain.
[0098] Objective Function--The objective function sets the goals
for the overall operation of the process or unit. The objective
function provides one or several consistent numerical metric(s) to
which the process or unit strives to achieve and over which the
performance of the process or unit may be measured, e.g., from a
business standpoint.
[0099] Control Variables--Control variables (also called controlled
variables) are those variables that the controller/optimizer tries
to bring to some objective, e.g., to a target value, maximum, etc.
Manipulated Variables--Manipulated variables are those variables
over which the management of the process or unit has authority and
control, and which are moved or manipulated by the
controller/optimizer to achieve the targets or goals of the control
variables. These variables are the actual control variables whose
settings are limited by the constraints. This is in distinction
from controllable constraints in the sense that manipulated
variables may operate within some range of controllable or fixed
constraints.
Set Point--The set point is the target signal or value for a
manipulated variable.
[0100] Disturbance Variables--Disturbance variables are measured or
unmeasured variables over which the management of the process or
unit does not have direct authority or control. For example,
temperature, humidity, upstream flow or quality, may all be
referred to as measured disturbance variables. Unmeasured poisons
to a catalyst bed is an example of an unmeasured disturbance
variable.
Control System--The control system is the mechanism by which the
manipulated variables are driven to the set points.
[0101] Response--The response is the measurement of the current
position of the manipulated variable. The response is the feedback
of the movement of the manipulated variable to the set point in
response to the actions of the control system in its effort to
achieve the set point. [0102] Performance Metric--A performance
metric is a calculated or measured value of an interesting or key
indicator of the operation of the process or unit. For example,
common performance metrics include: throughput (production rate),
quality, amount of off-spec material produced, cost of production,
downtime, emissions or waste production, production efficiency, and
conversion, among others. [0103] Model Biases--Parameter offsets
that normalize a model's predictions to actual operating values.
For example, a model's bias may be the difference between the
model's predicted value for a parameter or attribute, and the
actual value at runtime. These biases may thus be added to the
model's predicted values to bring the model into compliance or
calibration with the actual process. Multivariable Predictive
Control and Optimization
[0104] In preferred embodiments of the present invention,
multivariable predictive control, which may be referred to as an
advanced process controller (APC), and optimization technologies
and methodologies, such as dynamic optimization, may be used to
improve some aspects or attributes of a chemical manufacturing
process, such as, for example, product yields and mixes,
profitability, efficiency, and so forth, among others. The various
techniques described below are directed to an exemplary
application, specifically, olefin, i.e., alkene, production,
although it should be noted that the techniques described are
broadly applicable in other manufacturing domains as well, such as,
for example, polymer production, pharmaceuticals, and other
hydrocarbon-based chemical products, among others, where the
process may include downstream and/or upstream constraints.
[0105] It should be noted that while some of the control and
optimization components may be described or represented as single
integrated components, i.e., control and optimization
functionalities comprised in a single software application, other
configurations of these functionalities, e.g., as distinct
applications or modules, are also contemplated. One exemplary
integrated optimization and control application, referred to as an
integrated controller/optimizer (ICO), is Process Perfecter.RTM.,
provided by Pavilion Technologies, which provides control and
optimization functionality in a single application, and may be used
to reduce process variability and continually improve plant
performance based on real-time plant data. More specifically,
Process Perfecter.RTM. may be used to optimize and perform
closed-loop dynamic control on continuous industrial processes,
such as production, energy, and environmental processes, using
non-linear modeling technologies such as neural networks, support
vector machines, etc. Using process data in the form of empirical
models, this product optimizes based on current operating
conditions, targets, constraints, and objectives, as described in
detail above. It should be noted that while neural networks (analog
or digitally implemented) are used in the embodiments described
herein, any other types of non-linear empirical modeling
technologies may be used as desired, including, for example,
support vector machines, statistical models, etc.
[0106] As another example, computation and analysis software
components may be utilized in some embodiments of the present
invention, e.g., to provide various supporting calculations for the
optimization and control methodologies described herein. An
exemplary computation and analysis tool that may be suitable for
such use is Process Insights.TM., also provided by Pavilion
Technologies, although any other software program or programs may
be used as desired.
FIGS. 7 and 8--System and Method for Multivariable Predictive
Control of a Chemical Manufacturing Process
[0107] FIG. 7 illustrates an exemplary system, e.g., a software
system, for multivariable predictive control of a chemical
manufacturing process, according to one embodiment. FIG. 8 is a
high level flowchart of a method of use for such a system,
according to one embodiment. As noted above, the systems and
methods described below are directed to the production of olefins,
although this is but one exemplary use of the present techniques.
The various techniques disclosed herein are broadly applicable to
any unit or process where capacity constraints are imposed by
downstream equipment or sub-processes. As noted above, in preferred
embodiments, the downstream processes include separation and
purification processes, and the upstream processes include
reactors, e.g., furnaces, e.g., for "cracking" feedstocks.
[0108] In preferred embodiments, the system shown in FIG. 7 may
utilize APC and optimization methodologies to more effectively
implement and manage production processes toward desired goals and
objectives. More specifically, in preferred embodiments, overall
plant optimization may be achieved by a combination of (a)
individual ICO applications managing individual equipment
constraints, and local optimizations, and (b) an ICO application
controlling and optimizing total plant product mix. . As noted
above, while in the descriptions presented herein the controllers
and optimizers are integrated into respective ICOs, in other
embodiments, some or all of the controllers and optimizers may be
separate and distinct from each other.
[0109] The dynamic optimization techniques disclosed herein take
advantage of two facts about olefins units that have not
traditionally been exploited for optimization. The first is the
fact that the product mix is determined entirely at the furnaces
and is based on feedstock composition and furnace operating
conditions. Products can only be lost in the purification area
(cold section); they cannot be created. The second fact is that the
interactions between the different pieces of equipment in the cold
section may only limit feed to those sections. Product
specifications preclude relieving constraints by changing product
composition. Since minimum energy use in any distillation column
equates to minimum separation, operating the column at maximum
allowable product impurities may, by the nature of the process,
minimize energy consumption and maximize allowable feed rate.
[0110] In some embodiments, the dynamic optimization techniques
presented herein may also take advantage of some unique features of
the integrated controller/optimizer (ICO) described herein. For
example, an ICO preferably includes a steady state model that can
not only be executed as a local optimizer as the ICO is performing
control, but can also be executed in a runtime environment as an
external optimizer with a different objective from the controller.
The dynamic optimizer may execute cold section ICO steady state
models with identical constraints as the controllers but with the
additional objective of maximizing feed rate to the group of
equipment controlled by the ICO. ICO dynamic model biases may be
shared between computer processes so that the process executing the
steady state model as an optimizer may use these biases, and thus
may dynamically match the steady state model with the actual
process. In this way, the controller may inherently account for the
process dynamics.
[0111] The collection of components and techniques described herein
may be referred to as a "dynamic optimizer", although the
particular components and their arrangements and use are meant to
be exemplary only, and are not intended to limit the invention to
any particular set or arrangement of components.
[0112] In the embodiment shown in FIG. 7, a dynamic optimizer 710
may operate in conjunction with standard section control
applications 720 to optimize the production process. As FIG. 7
shows, in one embodiment, the dynamic optimizer may include two
main parts: maximum feed calculator 714 that operates to perform a
maximum feed calculation for each area of the cold section, and a
feed coordinator 712, preferably an ICO that specifies or
implements control strategies for the hot-side of the process,
e.g., setting targets for feed flow, cracking severity, etc., for
the hot-side ICOs. As indicated in FIG. 7, these two portions of
the dynamic optimizer 710 may communicate with the standard section
control applications 720, e.g., ICOs 722 controlling furnaces on
the hot-side, and ICOs 724 controlling equipment on the cold side
of the unit. Note that each ICO preferably includes a steady-state
model of the process or sub-process being controlled. Each
steady-state model may include steady-state offsets or biases
(labeled "SS biases" in FIG. 7) that normalize the model's
predictions to actual operating values. For example, in one simple
approach, a model's bias or offset may be the difference between
the model's predicted value for a parameter or attribute, and the
actual value at runtime. These biases may thus be added to the
model's predicted values to bring the model into compliance or
calibration with the actual process. Further details of the system
of FIG. 7 are provided below in the context of the method of FIG.
8.
[0113] It should be noted that in various embodiments, some of the
method elements disclosed with reference to FIG. 8 may be performed
concurrently, in a different order than shown, or may be omitted.
Additional method elements may be performed as desired. The method
may proceed as follows.
[0114] In 802, maximum feed capacities of a plurality of downstream
processes in a chemical plant may be determined subject to one or
more local constraints on the downstream processes. In other words,
a maximum feed calculation may be performed to determine maximum
feeds for each of the plurality of downstream processes, e.g., for
each cold-side section, e.g., for each of a plurality of
refrigeration units/distillation columns, where the determination
takes into account one or more limits on the processes. For
example, limits may include pressure drop across a distillation
column, valve positions, and refrigeration compressor drive
amperage, among others. In one embodiment, the maximum feed
calculation may be performed by an analysis engine, such as a
real-time analysis engine (RAE) provided by Pavilion Technologies,
in conjunction with one or more steady-state models of the
cold-side processes, e.g., models comprised in respective cold-side
ICOs.
Maximum Feed Calculation
[0115] As noted above, the maximum feed calculation preferably uses
steady-state models from the cold side section ICO applications.
For example, a downstream optimizer may execute the steady state
models under a variety of conditions or states to determine a
solution (the maximum feeds), as is well known in the art of
constrained optimization. Note that in various embodiments,
respective ICOs preferably control respective cold side units
(e.g., distillation columns), although in other embodiments, one or
more ICOs may cover multiple units, or, an ICO may cover the entire
cold section. Each ICO application is preferably configurable to
improve operation of individual units subject to local process
constraints (e.g., maximum feed rates, etc.).
[0116] As noted above, the control (or controlled) variables (CVs)
are variables that the ICO maintains either at a setpoint or within
maximum or minimum limits. It accomplishes this by adjusting
manipulated variables. In other words, MVs are variables that the
ICO adjusts, and are typically the setpoints of distributed control
system (DCS) controllers. As also noted above, disturbance
variables are those variables that affect the process but are not
adjustable by the controller itself. Examples of CVs in a cold-side
ICO include amount of methane in the ethylene product, amount of
ethane in the bottom of the deethanizer, depropanizer pressure
drop, and demethanizer reboil valve position, among others.
Examples of MVs in a cold section ICO include reboil flow rate,
reflux flow rate, ethylene compressor suction pressure, and
refrigerant level in a chilling train heat exchanger, among others.
Examples of DVs in a cold section ICO include column feed rate,
cooling water temperature, and column pressure, among others.
[0117] In some embodiments, a typical cold side section ICO where
all product streams are saleable products may include targets for
impurities that are ideally set three standard deviations below
product specifications. Operating at these targets may minimize
energy consumption. Of course, in other embodiments, targets may be
set otherwise as desired.
[0118] In some embodiments, a typical cold side section ICO where
one or more product streams are used internally may contain
additional degrees of freedom. In some cases the trade-off between
energy costs and the value of saleable product recovered may be
neither constant nor obvious. These trade-offs may thus be most
appropriately managed by the ICO application either through local
optimization or specific tuning of targets and constraints.
[0119] The dynamic optimizer 710 is primarily concerned with the
maximum feed rate each area of the cold side section can handle.
Typically, even stream compositions that are not fixed by product
specifications have some practical limit. Thus every cold side
section ICO, even those that allow stream compositions to vary, is
capable of calculating a maximum feed rate. The feed maximization
calculation preferably executes each ICO steady-state model in
optimization mode with the feed rate as an MV. If product pricing
is not used in the ICO the feed may be provided a price to
encourage the optimizer to maximize it.
[0120] The feed maximization calculation may be matched to the
plant by utilizing the feedback biases (e.g., calibrated offsets)
from the ICOs. In this way, the calculation may be matched to the
plant by the controllers themselves, thus ensuring consistency
between the steady-state feed maximization and control models.
Additionally, the CV and MV targets and limits used in the feed
maximization calculation are preferably obtained directly from the
ICOs.
[0121] As indicated above, the steady state models may be
implemented using a variety of approaches, including neural
networks, support vector machines, and so forth. One benefit of a
neural network (NN) based ICO architecture is that if the
steady-state model is a neural network model, it can be solved for
outputs given a set of inputs, or conversely, for inputs given a
set of target outputs. Constraints and prices may be applied to
guide the solution. Additionally, several NN models may be combined
into one model. In the case of the feed maximization calculation,
the model may be solved for a set of inputs (the controller MVs and
DVs), which achieve maximum feed rate while respecting the
controller's CV targets and limits and the MV limits. In preferred
embodiments, feed is treated as a manipulated variable in this
calculation and assigned a positive price, which encourages it to
be increased.
[0122] Note that in the case where there are multiple ICOs in the
cold side section, it may not be necessary to combine models as
long as the feed flow to each section is included as a DV in that
section's ICO.
[0123] In preferred embodiments, the model(s) may be run or
executed in a single instance of the maximum feed calculator, which
in some embodiments may comprise Pavilion Technologies' Runtime
Application Engine (RAE.RTM.). The output of these calculations may
include a set of feed flows for each section that would allow it to
achieve all targets but subject to a limiting constraint. For
example, if the section were a single distillation column, the
maximum feed would be that which allowed maintaining the top and
bottom target compositions while reaching another constraint such
as maximum reflux/reboil or maximum column pressure drop.
[0124] In some embodiments, achieving a desired set of operating
modes may simply be a matter of configuration of the constraints
and targets. For example, if it is desired to minimize the bottom
composition when the column is not limiting but to allow this
composition to be sacrificed to some extent to achieve a higher
feed rate, the maximum allowable bottoms content may be passed as
the target to the feed maximization calculation while the lower
target may be used by the ICO during control calculations and tuned
to be sacrificed if necessary.
[0125] Thus, as shown in FIG. 7, each cold side ICO 724 may provide
steady state (SS) biases and targets and/or limits to the RAE 714
for use in calculating maximum feed values for each cold side
section. The RAE 714 (or functional equivalent) may then determine
feed targets, i.e., the maximum feed capacities, for each of the
cold-side sections, as described above.
[0126] In 804, upstream production parameters for the plurality of
upstream processes may be determined in accordance with the
determined maximum feed capacities and a specified objective
function, subject to one or more global constraints. For example,
in the olefins example, where the upstream process comprise a
plurality of furnaces (reactors), the upstream production
parameters may include one or more of: operating temperatures, feed
rates (for one or more types of feedstock) for each furnace, or any
other operating parameters or attributes germane to operation of
the upstream processes. Examples of an objective function include
(but are not limited to) profitability, schedule, feedstock use,
energy use or efficiency, optimal product mix, and so forth. Note
that in some embodiments, the objective function may be more
complicated, e.g., may be a combination of different aspects, e.g.,
an optimal product mix based on product pricing, or any other
function as desired. In one embodiment, the optimal product mix may
specify one or more of: ethylene production setpoint(s), propylene
production setpoint(s), and one or more C4+ production setpoints,
among others.
[0127] Examples of global constraints include (but or not limited
to) total feedstock flow of each of a plurality of feedstocks, the
ratio of each of the plurality of feedstocks to the total feed, and
the maximum difference between individual upstream feeds and the
average of the upstream feeds, among others.
[0128] In preferred embodiments, the upstream production parameters
may be determined via constrained optimization techniques. For
example, similar to the downstream constrained optimization process
described above with reference to 802, an upstream optimizer may
execute a plurality of steady state models of the upstream
processes under a variety of conditions or states to determine a
solution (the upstream production parameters). Note that in various
embodiments, respective ICOs preferably control respective upstream
(hot side) units (e.g., furnaces), although in other embodiments,
one or more ICOs may cover multiple units, or, an ICO may cover the
entire hot section. As noted above, each ICO application is
preferably configurable to improve operation of individual units
subject to local process constraints.
[0129] Thus, following the example of FIG. 7, the RAE 714 may
transmit cold side section feed targets to the feed coordinator
712, as shown. The feed coordinator 712 may receive additional
information regarding feed availability (feed available switches)
for each furnace, and may determine hot-side targets (i.e., CVs),
e.g., feed flow, cracking severity, etc., for each furnace ICO 722,
as indicated in FIG. 7.
[0130] Note that in preferred embodiments, the maximum feeds to
each area of the cold section may be determined by executing the
steady state models of the cold side ICOs in optimization mode with
the feeds treated as manipulated variables (Mvs). Appropriate
prices may be set so that the optimizer will increase component
feeds in proportion to their values until all degrees of freedom in
the APC are used. The steady-state model for each cold side ICO may
be matched to the plant by mapping the ICO steady-state biases to
the appropriate biases in the steady-state model. MV and controlled
variable (CV) limits and targets may also be mapped to the
steady-state model. In performing the feed maximization calculation
for a section, the maximum feed calculator 714 may use the same
model as the section's ICO, thus ensuring consistency. This
approach may guarantee that each section's controller is capable of
maintaining its CV targets at the feed rate and feed composition
delivered by the feed coordinator 712.
[0131] The feed coordinator 712 is thus an ICO that may operate as
a "master" controller for the furnace ICOs, setting their feed and
severity targets. The primary goal is to achieve the maximum feed
for each area of the cold section subject to furnace constraints
and feedstock availability. A significant advantage of using a
dynamic controller in the feed maximization is the use of dynamic
models, which may allow closer approach to the maximum feed limit
for each downstream section.
Feed Coordinator ICO
[0132] The feed coordinator 712 is preferably implemented as an
ICO. The main objective for this application is to achieve the
maximum allowable feed to each downstream section of the plant.
Note that if the ICO is allowed to move multiple feedstocks and/or
furnace severities, it may be possible to achieve multiple
downsteam constraints. Exemplary CVs and MVs for this application
are listed below, although it should be noted that other variables
may be used as desired. As shown, this embodiment of the
application covers a unit containing "n" furnaces, each feeding as
many as "y" different feedstocks. TABLE-US-00001 TABLE 1 Feed
Coordinator Controlled Variables Description Type Tag Max feed to
section 1 Setpoint Calc (see description below) Max feed to section
2 Setpoint Calc Max feed to section n Setpoint Calc Total flow of
Feedstock 1 Min/Max Calc Total flow of Feedstock n MinMax Calc
Total Feed n/Total feed Setpoint/Min/Max Calc Ethylene Production
Setpoint or Price Propylene Production Setpoint or Price C4+
Production (1 CV each) Setpoint or Price Max deviation, Furn n -
Avg Setpoint/Min/Max Calc
[0133] TABLE-US-00002 TABLE 2 Feed Coordinator
Manipulated/Disturbance Variables Description Type Tag Furnace 1
Feedstock 1 target MV Feedstock 1 target in Furnace 1 ICO Furnace 1
Feedstock y target MV Feedstock y target in Furnace 1 ICO Furnace 2
Feedstock 1 target MV Feedstock 1 target in Furnace 2 ICO Furnace n
Feedstock y target MV Feedstock y target in Furnace n ICO Furnace 1
Severity MV Severity target for Furnace 1 Furnace n Severity MV
Severity target for Furnace n
[0134] As mentioned above, the feed coordinator 712 may set targets
for the furnace ICO applications to control the cold side section
feed rates to the maximum feed targets (determined from the feed
maximization calculation). The targets set by the feed coordinator
may include feed and/or severity targets, among others. Severities
and/or feed types may be adjusted to vary the relative flow to
different sections so as to maximize the feed to each section until
all degrees of freedom are used. In some embodiments, switches in
each furnace controller may allow the operator (or controller) to
determine which feeds are available to be adjusted (see, e.g., the
feed available switches of FIG. 7).
[0135] The feed coordinator 712 may include dynamic models between
the furnace MVs and the cold side section feeds. The maximum cold
side section feeds (determined from the feed maximization
calculation) may be the primary CVs for the feed coordinator
712.
[0136] The feed coordinator 712 may be configured to achieve a
variety of objectives. For example, if it is desired to maximize
only one feed type, then only furnaces running that feed type may
be put under feed coordinator control. If multiple feed types are
to be maximized, all furnaces with those feed types may be put
under feed coordinator control.
[0137] In some embodiments, with no other configuration provided,
the feed coordinator may tend to increase the feed that gives the
greatest increase in the section feeds. Priority may be given to
the different feeds via the use of different tuning parameters.
However, in some embodiments, for maximum benefit it may be better
not to enforce any particular priority, thus allowing the
controller to determine the combination of feeds that best fills
the capacity of all sections of the plant subject to the available
degrees of freedom.
[0138] If more control over the solution is desired, constraints
and/or targets may be added to or specified for the controller.
Standard CVs for controlling the solution may include (but are not
limited to):
[0139] Maximum difference between individual furnace feeds and the
average--Setting this target at zero may tend to keep all furnaces
at the same feed rate. The actual difference may deviate from zero
as individual furnaces become limited but there may be some penalty
for allowing the difference to become very large. Alternatively,
this variable may be kept between limits, thus allowing the
controller more freedom to shift feeds but still remain within
acceptable bounds.
[0140] Each feedstock total as a percentage of the total--This
variable may be useful for cases where it is desired to maintain a
given feedstock as a percentage of the total by setting a desired
target or to keep that percentage between some bounds using minimum
and maximum fuzzy limits. In some embodiments, target feed rates
may be determined based a specified priority, e.g., from a user or
external process, and/or the economic value of resulting
products.
[0141] Total flow of each feed type--This variable may be used to
ensure that the solution consumes at least the minimum amount of a
feedstock but not more than is available. For a given feed type,
the change in cold side section feeds for a unit change in furnace
feed may be the same. Consequently, all furnaces of that feed type
may tend to be moved equivalently.
[0142] Specific olefins production rates--This variable may be used
to allow the dynamic optimizer to achieve target production rates
when 100% plant utilization is not desired, and may also provide a
means for maximizing the more valuable olefins (for example)
streams when all areas of the cold side section can not be loaded.
This may be particularly useful when the throughput is limited by
furnace operation. The setpoints may be tuned based on relative
prices of products. If it is anticipated that these will change
often, then tuning parameters may be calculated based on prices
read from an external database. Optionally, setpoints may be
replaced with a combination of min/max constraints and prices
(e.g., read from an external database).
[0143] In 806, the plurality of upstream processes in the chemical
plant may be controlled in accordance with the determined upstream
production parameters, thereby facilitating production of the
optimal product mix by the chemical plant in accordance with the
specified objective. In other words, the upstream process, e.g.,
furnaces, may be operated in a manner that makes maximum use of the
downstream processes in attempting to meet the specified objective
subject to various constraints (e.g., local and/or global). Said
another way, the determining maximum feed capacities (802), the
determining upstream production parameters (804), and the
controlling the plurality of upstream processes (806) may implement
overall chemical plant steady state optimization via one or more
multivariable predictive dynamic controllers adjusting operational
targets in the reactors, including target feed rates, to achieve
optimum feed rates and product mix in the separation and
purification processes.
[0144] Continuing with the example of FIG. 7, the hot-side ICOs may
then operate in accordance with the provided targets, moving
hot-side MVs as needed to meet the targets. In this manner, the
furnaces may be operated in such as way as to maximize feeds to the
cold side sections in accordance with the maximum feed calculations
of the RAE 714.
[0145] In some embodiments, the feed to each furnace may be
increased until either the downstream section feed targets are
reached or until the furnace is limited, i.e., has reached
capacity. Furnaces that are limited will not be able to achieve a
higher feed flow, and so in some embodiments, may have a small
offset between the target feed flow and the achieved flow. This
offset may indicate that that furnace is at capacity, i.e., is
"wound up", and so the feed coordinator should not increase its
feed further. Thus, any further feed increases may instead be made
to other furnaces, i.e., those that are not at capacity.
[0146] The predicted trajectories for the feed coordinator CV's may
be passed to the cold side section ICOs, allowing these controllers
to anticipate feed changes and better maintain targets.
Calculations of CVs
[0147] In some embodiments, the CVs may all be calculated
variables. A primary functionality of the present invention is the
calculation of the maximum feed to each section. Additional
calculations may generate CVs that are used to further guide the
solution. For example, in some embodiments, the following
supporting calculations may be performed by the same application
(e.g., RAE) used to calculate maximum feed to the cold side
section:
[0148] Total flow of Feedstock n=sum of all Feedstock n feed to all
furnaces;
[0149] Total Feed n/Total feed=Total flow of Feedstock n/Total feed
to all furnaces; and
[0150] Furnace n Deviation=Furnace n feed flow-Average furnace feed
flow.
It should be noted that any other calculations, e.g., supporting
calculations, may be performed and used as desired, those shown
being exemplary calculations, and not intended to limit the
calculations to any particular set of computations.
[0151] It should be noted that in preferred embodiments, the method
elements 802-806 may be performed in an iterative manner to
dynamically optimize operation of the chemical plant. In other
words, a feedback loop between the actual plant behavior and the
optimization processes described above may be established and
maintained to dynamically operate the chemical plant in an optimal
manner, i.e., in a manner that substantially meets specified
objectives subject to local and global constraints.
[0152] Thus, various embodiments of the dynamic optimizer described
herein may implement constrained optimization of downstream
processes to determine maximum feeds for downstream processes of a
chemical plant subject to local constraints, and constrained
optimization of upstream processes in the plant may be performed,
subject to the determined maximum (downstream) feeds and one or
more global constraints, in order to meet a specified objective
(function). Thus, by using both local and global optimization, the
chemical plant may be operated in a manner that makes efficient
(i.e., maximum) use of downstream capacity to meet specified
objectives.
Operators and Engineers GUI, Operators' Display
[0153] In preferred embodiments, a standard Graphical User
Interface (GUI) may be provided, e.g., for operators and engineers.
The GUI may operate in various different modes, e.g., Operator or
Engineer modes, depending upon the particular user's needs and/or
privileges. For example, according to one embodiment, in both
Operator and Engineer modes all parameters in the ICO may be
visible, while tuning parameters may only be changeable in Engineer
mode. The standard GUI may operate on or under any of various
execution environments or operating systems, including, for example
(but not limited to), Linux, Unix, MacOS, Microsoft Windows, Exceed
X-windows emulator (for Windows), or in a X-window client remote
computer, among others. In preferred embodiments, several GUIs may
be used at the same time.
[0154] In some cases, a client may wish to have a display, e.g., a
DCS display, resident on the operator's console for the operator's
use either instead of or in addition to the GUI. Any such display
or displays may be used as desired.
[0155] Thus, in various embodiments of the systems and methods
described herein, dynamic optimization may operate in conjunction
with APC on individual equipment throughout each process unit,
e.g., each olefins unit, to optimize the overall product mix of the
unit. Maximum cold side section feed rates may be determined by
executing steady-state models of underlying APC applications (e.g.,
ICOs) in optimization mode. These targets may then be implemented
by an integrated controller/optimizer, i.e., a dynamic controller,
e.g., Process Perfecter.RTM., acting as a "master" APC (or ICO)
controller to all of the individual furnace APC (or ICO)
controllers. This strategy may thus exploit local optimization
capabilities of an integrated controller/optimizer, e.g., Pavilion
Technologies' Process Perfecter.RTM., to optimize trade-offs
between product losses and energy used for separation. As described
above, the product mix optimization may be constrained by maximum
allowable feed rates to groups of separation equipment downstream
of the reactors (e.g., furnaces) producing the product. These
maximum rates may be determined by executing individual ICO
controller steady state models in optimization mode with the
objective of maximizing feed rate. The steady state models are
preferably copies of the steady state models used in the ICO
applications and may be matched to the plant by using the dynamic
model biases determined by the ICO. Furthermore, all targets and
limits applied to the ICO may be shared with the steady state
models used in the optimization step.
[0156] While the present invention has been described with
reference to particular embodiments, it may be understood that the
embodiments are illustrated and that the invention scope is not so
limited. Any variations, modifications, additions and improvements
to the embodiments described are possible. These variations,
modifications, additions and improvements may fall within the scope
of the invention as detailed within the following claims.
* * * * *