R&D on Optimum Production System Using Large-Scale Nonlinear ...

2001.L.1.1.6
R&D on Optimum Production System
Using Large-Scale Nonlinear Model
(Nonlinear model optimum production system group)
Kenzo Fuji, Hiromitsu Yamaguchi, Eiji Furuichi, Hideto Itagaki, Masataka
Okazaki, Hiroshi Zinro, Noburo Shibahara, Seiji Terado, Koji Sagara,
Kuniyoshi Fukumoto, Oudou Kobayashi, Yasuhide Kano, Yasuhiro Kimura,
Toshio Okamuro, Hitomi Takuma, Hideaki Mito, Shigeki Kobayashi,
Kazumasa Okada, Shigeru Ishiguchi, Masao Yamakawa, Toshio Saeki, Ken
Kitamura, Masao Yanagibashi
1. Contents of Empirical Research
1.1 Objective of R&D
Liberalization and internationalization are permeating the petroleum industry, and bolstering
international competitiveness has become an urgent issue. In the future, therefore, the key
point will be how to manufacture high-quality products at low cost, using low-cost raw materials,
while coping with fluctuations in demand and prices.
For this purpose, activities at oil refineries must be controlled through a unified system, from
compilation of daily production schedules to control of operations, in response to daily and
hourly changes in market trends, including changes in raw material prices and in product prices.
Accordingly, a system is required for achieving optimum running conditions, using models
crafted to replicate at high accuracy facilities throughout the oil refinery. It is also imperative to
construct a control system linked to this system, thereby constantly operating facilities under
optimum running conditions. It is believed that constructing these systems will contribute
toward strengthening international competitiveness at oil refineries, toward conservation of
energy and human resources, and toward greater safety in operations.
1.2 Contents of R&D in 2000
(1) Database construction
In FY 2000, a database will be constructed for a practical system aimed at both general
and detailed optimization. A prototype model of a data reconciliation system will also be
developed for conversions to compatible data.
(2) Construction of nonlinear process model for individual unit
A modeling and interface system will be developed in 2000 for incorporation into the
general optimization system, and R&D will be aimed at basic technology for linking the
general optimization system with nonlinear models.
(3) Investigation of optimization method
In FY 2000, research will be conducted for development of a general optimization model
that can replicate practical equipment, and for development of a nonlinear model up to
quadratic replication of nonlinear components.
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In having the optimum running conditions determined by the general optimization system
reflected in practical equipment, a scheme is required for canceling out differences
between the model and actual equipment characteristics. In addition, the measurement
features of a near infrared spectral analyzer will be bolstered so as to measure fluid
properties in real time. In this way, a base model of the general optimization system can
be built.
(4) Investigation of method for drafting unit/blender operation schedule
Methods for drafting unit/blender operation schedule (solution of scheduling problems)
were investigated, but in the optimization of input/output scheduling, there were more
variables than initially anticipated, and resolutions involving intelligent optimization
methods (i.e., GA) were planned, but matters could not be covered with present levels of
technology. In view of these and other considerations, this development plan was
ultimately abandoned. The impact of this change on the general optimization system in
the initial plan was very slight.
(5) Preparation of basic technology for developing robust control system
In contrast to running conditions established through optimization functions of the general
optimization system, basic technology for development of a robust control system that
follows up on equipment will be developed and applied. In particular, stabilization control
similar to general optimization will be developed, as will robust control technology to
withstand changes in running modes, etc. These developments will make it clear
whether or not operations can be conducted stably based on the results of general
optimization.
2. Empirical Research Results and Analysis Thereof
2.1 Construction of Nonlinear Process Model for Individual Unit
(1) Development of total optimization system interface
In order to link models of the four major units developed in 1999 (TOP unit, VH unit, FCC
unit, ET unit) to the general optimization system and operate the same, an interface is
required.
The tools used in the general optimization system serve to input the ∆ model (described
later) in the worksheet format of spreadsheet software. It was decided that the function
of the spreadsheet software would be to perform nonlinear process model (neural network
model) calculations, using a function whereby arithmetic takes place by automatic
computation.
More specifically, the program that performs neural network arithmetic is stored in the
optimization system; the arithmetic is performed by calling out the program via
spreadsheet software, and the results are written in the spreadsheet software worksheet.
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However, whereas the ∆ model required for the general optimization system is one of the
rate of change in dependent variable as opposed to independent variable, the nonlinear
process model created represents the relationships between independent and dependent
variables. Hence a program was created within the spreadsheet software for determining
rate of change based on the results of minute changes in variables in the vicinity of the
given independent variables, and transfer of data required for the general optimization
system was enabled.
When a working test was performed with the general optimization system linked up, it was
confirmed that the gain of ∆ model calculated by neural network model was read correctly
and that there were no problems in convergence.
(2) Nonlinear process model in individual detailed optimization system
In detailed optimization type stabilized control (to be described later), detailed optimization
involves the search for optimum points based on a detailed model of the unit. In this
system, the bulk of calculation time is devoted to calculation of process characteristics by
detailed model.
An investigation was made of whether or not a neural network can be used in this system,
as in the general optimization system, instead of a detailed model.
On the assumption of a simple response process, a response model was replaced with a
neural network model. From a comparison of optimization calculations, it was confirmed
that optimization could be achieved at adequate precision. Yet it was also discovered
that there are issues in practical application to be resolved.
2.2 Development of Total Optimization System
(1) Creation of oil-refining, petrochemical large-scale model
The large-scale, nonlinear model can be roughly divided into two categories: a basic
model component and a nonlinear model component (neural network component). The
basic model component is comprised of a unit flow sheet model based on a linear model,
a channel synthesis gain correction calculation component (hereinafter called “pooling”)
manifested by secondary model component, and a gain linear correction component
(hereinafter called “∆ model”). In addition, respecting product blend, a blend algorithm
was used in which the nonlinear characteristics in the product specifications of gasoline,
heavy oil, light oil and kerosene were each taken into consideration. The tax models
which must be considered in transfers between oil refining and petrochemicals were
modeled in consideration of the tax network. In addition, the unit of measurement
covering oil refining is on a volume base while that of petrochemicals is on a weight base.
Modeling was implemented so that transactions between these two sectors can be
converted between volume and weight without contradictions. A model outline is shown
in Figure 2.1. In the oil refining/petrochemical complex, nonlinear factors are especially
pronounced in nonlinearity and nonlinear modeling was applied to the four units to be
considered.
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Gasoline blend
Jet fuel
blend
Kerosene blend
Light oil blend
A heavy oil blend
C heavy oil blend
Crude
oil
Tax model
(Tariff, Petroleum tax)
Nonlinear reaction
simulator
Figure 2.1
Outline of Oil-Refining, Petrochemical Large-Scale Model
(2) ∆ base model
A ∆ model employing secondary nonlinear model was used as the compositional element
of the basic model. In this research, firstly, ∆ modeling was implemented in that portion
of the basic model with strong nonlinearity, and a structure was developed in which an
external nonlinear model (neural network model) is connected to a ∆ model by means of
an interface. Presented below is an explanation of the ∆ model developed for ethylene
unit. In the regular LP model of the ethylene unit, either a yield model of fixed value or of
virtually parallel yield is used, together with a collector at the output. However, this
method cannot be considered as expressing nonlinearity and blatant inaccuracies are
possible.
One approach is to add raw material composition and running factors, the main factors of
change in yield, and apply linear correction to the standard yield. In this case, the LP
model must match with the raw material property factors created by pooling.
Presented in Figure 2.2 is a sample composition of ∆ model with ethylene unit. The four
factors at the lower left are yield correction factors. The standard yield is corrected by
each factor. The gain of each factor with respect to yield is presented in the rows of the
table. In a normal LP, properties (raw material correction factors) cannot be calculated,
but by using the nonlinear method, properties can be calculated from crude oil and
conveyed on a flow sheet. This function is called “pooling.” In ∆ modeling, data on
properties obtained by pooling are used to correct the yield.
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Hydrogen
Fuel gas
Ethylene
Ethane
Propylene
BBF
Pentane
Raffinate
Bezene
Toulene
Xylene
C9 aroma
CBO
Loss
Composition factor 1
Composition factor 2
Selectivity
Severity
Standard
gain
Composition
factor 1
Composition
factor 2
Selectivity
Example of ∆ modeling of ethylene cracking furnace
Severity
Figure 2.2
Sample Composition of ∆ Model (Ethylene unit)
(3) Consolidation of nonlinear model
Yield correction by ∆ model is limited because it is correction based on a linear
relationship, and it can only secondary, nonlinear characteristics at the highest can be
considered. Hence it is inadequate for representing units having strong nonlinear
characteristics as exemplified by the ethylene unit. Accordingly, further expansion and
the addition of a re-centering function can be considered. In other words, this is the
remaking of nonlinear characteristics in the search process.
This method is called sequential LP (SLP), and here a stabilized method of solution was
developed by having the ∆ model as interface. The steps involved are given below.
Step 1 Operate LP at initial values and obtain solution.
Step 2 Perform nonlinear processing of pooling.
Step 3 Perform nonlinear processing of ∆ model.
Step 4 Operate external nonlinear model and generate ∆ model.
Step 5 Renew ∆ model inside LP.
Step 6 Repeat steps 1 to 5.
Step 7 Make convergence assessment.
The above calculation steps are illustrated in Figure 2.3. At this time, the relationship
between initial data (∆ model) and renewed values, and the problem of simulator
convergence tolerance, are inhibiting factors in stable LP operation. Simulator by the
neural network model used in the present R&D not only resolves the aforesaid problem; it
is believed to be equipped with adequate practicality in terms of resolution speed and/or
stability.
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Nonlinear reaction simulator
Interface
∆ base model coefficient
Input
First LP
model
Second LP
model
Third LP
model
Output
Figure 2.3
Calculation Procedure for Optimization by SLP
(4) Analysis of running mode by basic model
Using the system, an attempt was made to compute summary values on the benefits in
operational schedules at an oil-refining/petrochemical complex. At this time, however,
the nonlinear model was linked only to the CCR unit. Computed results are given below.
(a) Catalytic reformer operation mode
(b) Light oil desulfurization unit operation mode
(c) Fluid catalytic cracker operation mode
(d) Ethylene unit operation mode
2.4% (oil refining vs critical gain)
1.1% (oil refining vs critical gain)
0.7% (oil refining vs critical gain)
1.2% (petrochemical vs critical gain)
It should be noted, however, that these results derive from a comparison with the
company’s current operational pattern. And with some units, it was confirmed as a result
of checking actual precision that the material balance matches well. Concerning
variations is gain, long-term tests and/or confirmation of fluid properties are required, and
these are issues for the future.
(5) Unit for measuring fluid properties
The large-scale nonlinear optimization system ranks as a system that facilitates
optimization of both product planning and operational planning simultaneously.
Consequently, for the operational mode obtained as the solution, a scheme for absorbing
the error in forecast of the final product is required. For measuring fluid properties, a
general-purpose, near infrared spectral analyzer, which can measure multiple ingredients,
is currently being installed. As target fluids, attention was focused on product types or oil
types related to 4 base materials, with reference to volatile oils having a major impact on
gain at oil refineries. In FY1999 the measurement accuracy of the near infrared spectral
analyzer was checked, and practical application tests were done on sample equipment,
then based on the results thus obtained, product and base material measuring points were
augmented in FY2000. Measurement results are scheduled to be evaluated from next
year.
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4.1.2 Investigation of Method for Drafting Unit/Blender Operation Schedule
Initially, it was believed that the objective could be reached by developing a scheduling system
mainly based on raw materials. Accordingly, a case study tool with no automatic functions was
first created in 1999. In 2000, case study functions were expanded, and an attempt was made
to optimize scheduling by using GA and other intelligent methods. Nevertheless, it became
evident that in scheduling limited to raw materials independent of research advances, the
objectives of total optimization cannot be met.
It was also found that GA and other methods initially conceived for optimizing large-scale
scheduling, including products as well as raw materials, are problematic technologically. To
overcome this hurdle, a technological survey was conducted, and it was found that the
standards of generally accepted scheduling methods or of methods that exceed those findings
obtained through PEC research thus far were not exceeded. For this reason, it was judged
that progress with the current research could not be assured, and development was thus
abandoned.
4.1.3 Development of Per Unit Stabilized Control System
(1) Necessity of a robust control system
(a) Ranking of robust control in an optimum production system using large-scale model
In having the results of an optimum production system using large-scale model
reflected in units, the variables which remain roughly fixed under conventional
running can be varied, based on the optimum solution, in accordance with
fluctuations in market conditions or materials. In this case, by shifting the
operational values of reactor or main distillation tower, which constitute the major
variables, dramatic fluctuations occur when viewed from the standpoint of process
characteristics. Hence the impact on control loop becomes manifest as fluctuations
in gain. To resolve these problems and coordinate process control with optimum
production planning, a robust control system that runs stably even when
characteristics fluctuate is required. Nevertheless, to accommodate such
fluctuations in characteristics, which affect the entire equipment, by conventional
methods such as model predictive control, an enormously large volume of operations
is required, and this is not practical.
Operational variables other than the main variable of general optimization must be
resolved by separate, detailed optimization, and unit operation must be followed in
real time. Such optimization is established under the conditions of static material
balance; dynamic patterns are not considered. Consequently, if the solutions of
detailed optimization are output as such to the process, inconsistencies arise,
constraints cannot be maintained, and conditions arise whereby shift cannot be made
to operations as instructed. Such control in which static solutions and dynamic
control are incorporated and maintained is called gain maintenance type control.
This report covers practical research in which an FCC unit serves as the model unit.
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In the case of units for which such detailed optimization has not been implemented
but which can be strongly affected by it, PID control must be made secure, and
performance must be improved to withstand fluctuations. This application was
attempted in a sulfolane unit positioned downstream of a catalytic reformer. The
unit is strongly influenced by the upstream operational unit, and its need is great in
terms of operation. The rankings of each control factor are illustrated in Figure 2.4.
From the standpoint of increasing unit stability, performance improvement by PID is
practical, and the establishment of this technology is indispensable for multi-mode
operation. Moreover, when the operation of reactors, etc., is considered, there are
cases in which response cannot be made with simple PID tuning. This is a
nonlinear problem. In the present R&D, an approach involving GMDH network
model was adopted for this problem. An investigation of system basics and of
desktop model was conducted.
Large-scale nonlinear optimization system (total planning)
Major unit operation mode
(Major operation variables,
intermediate price, constraints)
Unit detailed optimization by general optimization homogenous model
Unit operation target value
(key operation variable)
Robust control system with
optimization homogeneous model
Robust control system tolerating
multiple operation modes
Unit operation control value
(operation variable)
Nonlinear
tuning
PID stabilization
tuning
Process
Figure 2.4
Ranking of Robust Control in an Optimum
Production System
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(b) Detailed optimization type stabilized control
Solutions for satisfying the gain of gigantic oil refinery/petrochemical complexes are
computed by resolving nonlinear, large-scale optimizations. In actuality, however,
these solutions are at the planning level, and they are resolved at monthly or weekly
intervals. The optimum solution includes all the major operational variables of each
unit, but from the standpoint of model objective, modeling down to minute operational
variables is not possible. In actual operations, each unit has a large degree of
freedom, and the optimum gain can be produced by eliminating this freedom. Here
optimum control of actual operations is required, along with detailed optimization
whereby the solutions of general optimization are resolved in still greater detail. The
problem here is concern that if separate evaluative functions are newly defined and
solutions are reached as partial optimizations, inconsistencies with general
optimizations might arise. To reach solutions in which partial optimizations match
with general optimizations, the following conditions are to be satisfied.
a) Balance achieved in intermediate prices.
b) No inconsistencies between overall constraints and partial optimization
constraints.
c) Assumed conditions (i.e, raw materials) for general optimization and partial
optimization are the same.
A partial optimization system conceived in this manner can be linked to a general
optimization system. The conditions for linking general optimization to partial
optimization, taking the FCC unit as an example, are illustrated in Figure 2.5.
Nonlinear large-scale
optimization modeling
Crude
oil
Compatibility of local solution and
general optimum solution
Projection of intermediate price and
overall constraints
Detailed optimization
Optimization homogenous
type control
Similarity of
evaluation functions
Similarity of
evaluation functions
Figure 2.5
Conditions for Linkage of Total and Partial Optimization
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(c) Control system in detailed optimization
In order to obtain solutions efficiently, from general optimization to actual working
terminals, layers must be stratified as necessary and optimization layers must be
constructed in accordance with each function. In addition, the conditions for
preventing inconsistencies between such laminated child optimizations and parent
optimizations have been presented. Nevertheless, in the composition of detailed
optimizations, solutions are reached on the assumption of static conditions over a
certain time interval. Actual plants, on the other hand, are constantly exposed to
disturbances, and it is difficult to maintain constraints dynamically with detail
optimization alone. The problem here is the association between the optimum value
of detailed optimization and the optimum value maintenance function for control
acting in the minute unit. Normally, detailed optimization is conducted in the hour
unit. Consequently, control, including optimization, must be maintained over this
time interval. To resolve this problem, the methods undertaken by general and
detailed optimization are employed also in relation to control. In other words, as
shown in Figure 2.6, a gain function is defined in the evaluative function of the control
system, not just ISE minimum. This evaluative function and the evaluative function
of detailed optimization must be established with no inconsistencies. By taking this
measure, the control system, while emphasizing dynamic factors, can shift toward the
maximum unit gain when freedom has been generated. In view of the load put on
the calculator, the model inserted into the control system must be a simple one.
Here the composition is by linear model. In light of the above results, it was decided
to use the system with expanded model forecast control because a control system
having evaluative functions is absolutely necessary. From model ranking, it
becomes evident that in detailed optimization, gain as opposed to gain evaluation
function becomes Jacobian.
General optimization
• incorporation into control of gain
functions
• Compositional method improving
robustness
Local optimum
solution
Stabilization,
Detailed optimization
Expansion of model forecast control
• Interpolation of optimization
yielding homogeneity
• Integration of control internal
model and detailed optimization
Stabilization,Robust control
Development technology
under investigation with
FCC unit
Figure 2.6
Composition of Control System in Detailed Optimization
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(2) Stabilization by PID tuning
(a) Present status of PID tuning
In the control of an actual plant, virtually all controller operate under PID control.
From a survey of PID parameter setting conditions, it was discovered that over 50%
of all controllers are operated at inappropriate parameters. Overall, the trend in
parameter tuning was clearly found to be the so-called operator’s tuning in which the
gain is set low and the integral time is made short. The characteristic feature of this
type tuning is that under stable conditions with no disturbances, relatively
problem-free control takes place. But if there is rainfall or large process fluctuations,
there is the danger that overall control can become unstable. From the standpoint
of operator, however, it is reassuring because operations are aimed at curtailing
output.
(b) PID tuning support system
In order to clarify the configuration of a robust controller, the design standards of a
more stable controller will be linked and evaluated from an analysis of unit operating
conditions. In other words, by combining event analysis tool with controller design
standards, the concept of robust control in the oil refining process will be
implemented as depicted in Figure 2.7. Here the event analysis tool takes out event
information on the operation of alarms, manipulations, etc. as character time series
information. The tool uses a method whereby the controllability of the unit is
evaluated through analysis of the time series and through frequency analysis.
Heretofore, only the evaluation of controllability by independent loop has been
discussed. The present approach makes it possible to objectively evaluate
controllability of the entire unit in practical terms. In process control, even if the
individual loops are adjusted optimally, the stability of the unit is not assured at all.
Thus far, however, control theories have taken as their standard an evaluation of the
controllability of individual loops. The present R&D focuses on such contradictions
and aims to improve the controllability and stability of the unit as a whole. The steps
involved are as follows.
a) Evaluate unit controllability with an alarm analyzer. At this time, also abstract
the problem loop and evaluate its impact on related loops.
b) Determine tuning strategy for the whole unit.
c) Measure loop features and perform step 1 tuning.
d) While evaluating controllability, apply step 2 tuning to the targeted response.
e) Evaluate mutual interference with other loops and review the range setting.
f) Repeat (1) to (5) and evaluate the unit’s stability.
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Operating alarm Work history
Alarm,Work event analysis
Control performance evaluation,
Tuning indices
Tuning indices of process
as a whole
Time series evaluation of
Interference between loops
Controller unsuitability
• Control standard matching objectives of process control
• Controller selection
• MIN-MAX robust
step1: Initial tuning (Kitamori method base + MIN-MAX robust)
step2: Repeated tuning (Improved critical sensitivity method base + MIN-MAX robust)
Figure 2.7
Outline of PID Tuning Support System
Employed as the tuning tool at this time with step 1 was the Kitamori method. At
step 2 the improved type critical sensitivity method was used. The range model was
determined in consideration of loop mutual characteristics at over-dumping and
under-dumping, with critical dumping as the standard. Respecting flow volume
control and fluid level control, however, since these cannot be expressed by such a
range, it was decided to set up control based on a rule of thumb. Furthermore, in
consideration of the changes in unit characteristics or vagueness, the MIN-MAX
robust concept was adopted, and allowances were made for constantly having the
appropriate margin vis-à-vis critical stability. The tuning support system is illustrated
in Figure 2.7.
(c) Application results at a practical plant
In order to investigate the efficacy of process total tuning, a special unit was selected
and evaluated for applicability. The target unit is positioned downstream of the
catalytic reformer. It is a sulfolane extractor that extracts benzene from gasoline
ingredients. This unit is one of the most difficult to operate at an oil refinery because
upstream operations change and the raw materials brought from other refineries are
drastically different in composition. Over several hours in a benzene distillation
tower, the unit produces load changes to a maximum of about 40%, and when the
raw material changes in composition, the operator must remain on hand for manual
operations. A trial was conducted to see how far improvements could be made by
adopting the total tuning approach for this unit. From event analysis tool data, it was
learned that the following measures are required.
a) Total tuning with sulfolane unit
b) Critical dumping tuning for control of the benzene distillation column top
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c) Rearrangement of variables and change of controller for control of the benzene
distillation column bottom.
Results from the aforesaid total tuning are presented in Figure 2.8. It can be seen
that purity control, which in the past changed drastically whenever the raw material
was changed, can now be controlled stably. Moreover, after operating this control,
the volume of reboiler upwash was lowered 10% or more, and there were benefits in
energy conservation. Following this investigation, the unit could be made totally
manipulation- free.
From the aforesaid results, it has thus become evident that great benefits in terms of
process stabilization can be gained by switching from the conventional loop-unit
tuning to systematic total tuning.
Feed volume (KL/D)
After improvements
Impurities in benzene (wtPPM)
Figure 2.8
Operating Conditions After Total Tuning
3. Results of Empirical Research
(1) A basic model of the general optimization system was developed and operation mode
effects were tabulated. An investigation was made of the input/output variables,
methods, etc., actually employed for connecting the basic model to the nonlinear model(s)
used in the general optimization system.
(2) A database was constructed for detailed optimization and the prototype of a data
consistency system was created.
(3) Units for measuring fluid properties were augmented.
(4) Development of a support system for unit operation schedule creation was abandoned
because of technical problems.
(5) Construction of a robust control system was investigated and its application to practical
units was evaluated.
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4. Synopsis
4.1 R&D in FY2000
Developments were made covering the basic technology of the general optimization system.
Individual technologies of the general optimization system were developed and evaluated. It
was also confirmed that these individual technologies function effectively.
4.2 Future Issues
(1) Construction of nonlinear process model for individual unit
Technology for linking developed nonlinear models to general optimization should be
developed, along with technology for stable operations. The nonlinear model is to be
reviewed as necessary.
(2) Development of total optimization system
Using the integration model developed in FY2000, tests of linkage to detailed optimization
are to be conducted; unit gain should be analyzed, and efficacy should be verified.
Reviews should also be made as necessary.
(3) Development of per-unit stabilized control system
Gain-like stabilized control should be reviewed and evaluated. In addition, a control
system linked to general optimization in concert with properties analyzer should be
investigated and developed.
(4) Review of unit stabilization technology by PID tuning method; expansion of application
scope and establishment of technology
Investigations should be made of still more practical applications for nonlinear tuning.
Copyright 2001 Petroleum Energy Center all rights reserved.
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