ISA EXPO98 October 22, 1998
AUTO04 Applying state-of-the-art Technology to Control

An Application of Dynamic Simulationin Advanced Control
Kenichi Ohmura
Executive Director
Sales and Planning Division
Omega Simulation Co., Ltd.
Tokyo, Japan

Yutaka Hayashida
Assistant Director
Information Management Division
Mitsui Chemicals Inc.
Tokyo, Japan

Toshio Kujira
Senior Manager
Technical Department
Mitsui Chemicals Inc.
Osaka, Japan

Masakatsu Inaida
Assistant Manager
The 3rd Production Department
Mitsui Chemicals Inc.
Osaka, Japan

Keywords
Dynamic Simulator, Software, Online System, Polymerization, Grade Transition, Training Simulator

Abstract
There are three primary systems in which process simulators apply: online, quasi-online, and off-line
systems. In regard to conventional process simulators, both steady-state and dynamic simulators havebeen used in off-line systems.
This article, however, mainly discusses the dynamic simulators applied in the online system withthe following example of a “polypropylene ” polymerization process. First-principle-based rigorous
models were constructed in a dynamic simulator with unique functions, which accelerate thecalculations of each unit model, physical properties, and especially the pressure flow network balance ofthe entire plant. The actual operation data was then incorporated into the on-line system, and based on
the data, an initial condition was created for the models. Simulation was performed based upon theinitial condition employing a number of potential recipes. After having found the optimum recipe, it
was applied in actual operations. An integrated system was meticulously constructed, and as a result,the plant operations made significant improvements; thus greater laborsaving efficiency and productivity
were achieved.
This demonstration was successfully proven in actual operations by Mitsui Chemicals Inc., a major
Japanese chemical company.

1. Introduction
In recent years, tremendous changes have occurred in the application method and functions of dynamic
simulators. The application of dynamic simulators has extended to the areas of design/analysis,
operation training, operational support and optimization. Demand for functions, which include 1) themodeling of more rigorous plants based on first principles of chemical engineering, 2) the modeling ofcontrol systems that perform identically to actual ones, and 3) a connection to DCSs and real-timesystems such as process computers, have drastically increased. In such circumstances, because of thesignificant improvements in computer calculation ability and graphic user interfaces, coupled with theneed of safer and optimal plant operations, it is becoming relatively easier to develop rigorous dynamic
simulators.
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AUTO04 Applying state-of-the-art Technology to Control

In this article, we will introduce the dynamic online system which achieved automation andoptimization of product grade transitions in the polymerization process. First will be a description of
the application areas of dynamic simulators followed by their functions.

2. Application Areas of Dynamic Simulation
Dynamic simulation technology will play a very important role in achieving safer and optimal plant
operations. As shown in Figure 1, dynamic simulators are applied in three principal systems: off-line,
quasi-online, and online systems.

Optimization of
Plant Operations
Advancement of Plant Operations/Operational Support/Optimization

Online System• Operational support through predictive simulation• Optimal conditions during dynamic operations

Education, Training/Control System

Quasi-Online System• Operation training simulator• Development and inspection of DCS control logic• Development and inspection of plant diagnosis system• Plant training for process and control engineers and operators

Process Design/Analysis

Off-line System• Examination and improvement of the operation and control
strategies for new and/or existing plants• Examination of the revamping of existing plants• Development and inspection of advanced control systems• Inspection of the control sequence of batch plants

Figure 1. Application Areas of Dynamic Simulation(1) Off-line Systems for Process Design and Analysis
In off-line systems, results obtained from the dynamic simulator in the system are not immediately
applied to actual plant operations. This conventional method is used to 1) examine and improve theoperation and control strategies for new and/or existing plants, 2) examine the revamping of existing
plants, 3) develop and inspect advanced control systems, and 4) evaluate the control sequence of batch
plants.
(2) Quasi-Online Systems for the Education/Training and Development of Control Systems
In the quasi-online systems, the results obtained from the dynamic simulator are applied to simulated
plants. Usage of this system include 1) developing and inspecting DCS control logic and plant
diagnosis systems, and 2) training process/control engineers and operators.

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ISA EXPO98 October 22, 1998
AUTO04 Applying state-of-the-art Technology to Control(3) Online Systems for the Advancement of Plant Operations, Operational Support andOptimization
The results obtained from the dynamic simulator in the online system are fed back to the actual plant inreal-time. This online system is used to 1) support operations based on predictive simulation and 2)
optimize conditions during dynamic operations that occur at continuous process plants. The modelingof an entire plant and control systems, the integration of a plant model with real-time machines and theestimation of parameters and initial condition values corresponding to the actual plant data are threecrucial factors in this application.
A plant model constructed in the dynamic simulator is regarded as a base in all three areas. And
because of the enormous amount of financial and intellectual investment made in the development of theplant model, it is important that the infrastructure, which enables the model to be commonly used, be
provided in the simulation process.

3. Functions Required for Dynamic Simulators
This section outlines functions of the dynamic simulator (Visual Modeler®), which was developed forcommon utilization in off-line, quasi-online, and online systems.
(1) Model Fidelity
The unit models constructed in dynamic simulators (e.g., distillation column, heat exchanger, etc.)
require the same or higher level of accuracy than those in steady-state simulators. For example, thevapor-liquid equilibrium and heat balance calculations are performed at each tray in the distillation
column, and the internal pressure caused by changes in the vapor hold-ups is calculated. For the heatexchanger, the dynamic simulator must have the ability to describe the inner conditions in detail becauseof its dynamic characteristics. The key factor in plant modeling is pressure. To duplicate pressurechanges in the model (as nearly as possible), the simultaneous equations representing a relation between
the pressure and flow rate of the entire plant must be solved, and this balance calculation must be
performed at all times. Thus, it is essential that the dynamic simulator function with the ability toautomatically extract the relation of the pressure and flow rate and solve it at high speeds.
Scale of Simulation
To examine training and operation strategies, dynamic simulators require modeling of control equipment,
pipe lines, hand-valves, and safety and auxiliary equipment for the operations in dynamic circumstancessuch as shutdown and startup. Thus, for the same type of plant, dynamic simulators require 10 to 100
times more units than steady-state simulators. Some large-scale plant simulators consist of several
thousand units. To handle such large-scale plants, more efficient model developing environments andthe modeling technology that enables high-speed executions are required.
(2)Standard and User Unit Models
Units required in dynamic simulators but not in steady-state simulators include tanks, safety valves,
check valves, time lag pipes, measuring instruments and control equipment. While the same results
can be obtained from one unit model in steady-state simulators, dynamic simulators often require eachmodel to function identically to actual conditions because of its dynamic characteristics and different
handling procedures in operations. For that reason, more than 200 different types of unit models(processes and instrumentation) are registered in the standard library of the dynamic simulator.
Although various types of standard unit models to meet different conditions are typically included, thecases where these models are insufficient occur more frequently in the dynamic simulator. The reasons(3)3

ISA EXPO98 October 22, 1998
AUTO04 Applying state-of-the-art Technology to Control

are, for example, 1) for the reactor and special separator, the reaction mechanism and structural features
are easily reflected as discrepancies in models; and 2) in addition to the loop controller, special types ofmodels such as sequence control and/or the models with a calculation function of all kind are required.
It is, therefore, of utmost importance that there be features which allow users to add unit models to thelibrary according to their needs. The dynamic simulator features functions to satisfy this purpose, andfeatures an effective function for model description (EQUATRAN ®, equation solver language). A
major advantage of this function is that the user is never required to do any programming.
(4) Execution Functions
Simulation enables execution in real time and interactive responses per second. These features are
extremely important especially when training simulators are used as real-time systems. They also
become very effective in situations where engineers use them for analytical purposes and want to feel asense of realism.
This dynamic simulator uses a high-performance computer to calculate a large-scale simulation
with rigorous models on a per-second basis, featuring unique functions to accelerate the calculations ofeach unit model, physical properties, and the pressure flow network balance of the entire plant.
(5) Engineering Environment
When dealing with a large-scale plant, the development job becomes much easier if it is divided into
groups. One plant can be divided into several process models, allowing engineers assigned in eachsection to perform development tests independently. After all the tests are completed, they are
combined into an integrated plant model.
To make the development work more efficient, process flow diagrams (PFDs) are used for theconstruction and execution of a plant model. Process models are created by 1) selecting necessary unit
models from the library menu, 2) placing them on the PFD, 3) connecting them with streams and signal
cables, and 4) inputting respective parameters (size and characteristic data) for each unit model. Figure
2 shows an example of the model execution panel. It uses the same PFD as that in the model editing
panel; thus, the entire handling of execution, including running and pausing operations and changing
parameters for each unit models can be performed at any time on this panel.
(6) DCS Connection
The DCS connection is indispensable to realize a high-performance training simulator from which a
sense of realism is obtained; this makes it possible to directly develop and inspect the control system inthe DCS.
(7) Graphic Environment for General Users
As an application system environment where simulators can be used for many different purposes, thegraphic panel that allows users to design graphics at their discretion and the interface that enables
simulation data to communicate with other applications must be provided. As other application systems,
simple-type training simulators and the simulators for process education, operational support andoptimization are included.

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ISA EXPO98 October 22, 1998
AUTO04 Applying state-of-the-art Technology to Control

Figure 2. Model Execution Panel

4. Actual Example of the Optimization of Grade Transitions for Polymerization
Processes in the Online System
In the polypropylene process where product grade transitions are frequently performed, the major
objectives that must be achieved are reducing the transition time with proper production schedule,
stabilizing reactor behavior for safe operations and minimizing off-spec production. This section
describes the role that the dynamic simulator actually played in the construction of a system and that
contributed to the automation and optimization of grade change operations.
Process Overview and Grade Change Operations
Figure 3 illustrates the continuous liquid pool processes of polypropylene. In this example, a monomer,
comonomers, hydrogen and a catalyst are continuously fed to a continuous stirred tank reactor (CSTR),
and the polymerization is conducted in the liquefied monomer in the CSTR. To maintain constantreactor temperatures, the heat of polymerization is removed using a cooling jacket of the reactor and/or
the condenser. The catalyst implemented here is a high activity Ziegler-Natta catalyst. The melt
index (MI) and the amount of comonomers, such as ethylene, contained in the polymer are controlled byadjusting the concentration of hydrogen and comonomers. The catalyst activity is also affected by theconcentration of the two materials; thus when changes in the molecular weight and/or the amount ofcomonomer during a grade transition, the catalyst rate must be adjusted accordingly in order to remain
the reaction rate constant. The grade change operations require a lot of time and efforts because theamount of each material injected to the reactor must be adjusted to maintain inner conditions of thereactor within a range where removal of the heat can be controlled.
4.1

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ISA EXPO98 October 22, 1998
AUTO04 Applying state-of-the-art Technology to Control

Hydrogen
Propylene
Comonomer
Catalyst

ReactorA

ReactorB

Figure 3. Continuous Liquid-Pool Process Flow
4.2 System Configuration Required for Grade Transitions

As shown in Figure 4, the grade transition system configuration consists of two components. The
first component represents a DCS and a process computer for the automation of grade transitions, and in
the second, a simulation computer is utilized to achieve optimization of grade transition patterns.
Automation System

Optimization System
Pattern data

Process Computer

Feasibility
Determination

Simulation Computer

Optimum pattern
data

Process
data

Control

DCS

Polymerization

Figure 4. System Structure for Automatic Grade Transitions

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ISA EXPO98 October 22, 1998
AUTO04 Applying state-of-the-art Technology to Control(1) System for the Automation of Grade Change Operations
The process control is performed in the DCS, and prior to the DCS is a process computer, which is used
to achieve advanced control (e.g., grade transitions) and operating support functions (e.g., detection ofabnormal conditions). The process computer contains the following databases for grade transitions.
ll

The modification logic required to create transition pattern data of setpoint variables
The default setpoint variables for the catalyst rate, the amount of monomer, comonomers, hydrogen,etc. for each grade

The following operating procedures are performed during a grade transition.
1)
2)
3)
4)

With the modification logic, automatically creating transition pattern data from the current setpoint
variables and the default setpoint variables of the post-grade transition.
Sending a command from the DCS console to start a transition.
Transmitting transition pattern data from the process computer to the DCS.
The grade change operations are automatically performed at the DCS based upon the transition
pattern data. In addition to the loop control, the sequence control is incorporated in the DCS for
grade transitions (modifications of setpoint variables) to be safely performed. In any situations
where the continuation of an operation is likely to interfere with the safety of the plant, it isimperative that the operation be immediately stopped and the situation be notified to the operator.

(2) System for the Optimization of Grade Transition Patterns
The key factor for the optimization of a grade change is to reduce the amount of off-spec productproduced during a grade transition. To achieve this, it is sometimes required to temporarily increase
the feed rate of como nomers and hydrogen and then reduce the rate to the fixed level once theirconcentration levels have reached normal. However, this action could cause significant changes in the
catalyst activity resulting in situations where temperatures cannot be controlled. Thus, it is importantthat the constant rate reaction and the optimum condition of the reactor are maintained while a grade
transition is being performed. In the dynamic simulation system, which was developed to achieve thegoal of creating effective and optimal grade transition strategies, normal conditions are registered foreach grade as default, and either the registered normal conditions (referred to #1 below) or the normal
conditions modified in response to the actual operating conditions (referred to #2 and #3 below) is used
as an initial condition. The execution of the dynamic simulation is effectively performed based upon
the initial condition and the transition pattern data created in the process computer.
The following are three possible simulations.
1) Standard Simulation
This is a case study mode executed for the registration and modification of grade transition pattern data.
After the transition patterns between grades are determined by comparing the typical operating
conditions (e.g., cooling water temperatures and the catalyst activity) with the standard operating
conditions of the pre-and post-grades, they are registered into the process computer.
2) Simulation to Verify Before a Transition
This mode is used to verify the actual transition pattern after the operating condition data immediately
prior to the start of a grade transition is downloaded from the process computer. Of all the patterns
registered in the mode (1), the optimal pattern, which is applicable in the actual plant and offers
minimum transition time, is selected and determined.

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AUTO04 Applying state-of-the-art Technology to Control

3) Simulation for Actual Comparison
This mode is used to fine-tune and inspect models, and compare simulation results with actual operation
data (e.g., SV value) stored in the process computer.
The following is the operations performed during a grade transition, and the block flow diagram
describing the actual grade transition system is shown below.
1) Receive operating condition data immediately prior to the start of a grade transition.
2) Execute the simulation that provides normal condition values in response to the operating condition
data (calculation of initial values).
3) Receive transition pattern data from the process computer.
4) Perform a simulation based upon the initial values and transition data, and evaluate the data to see if
it is executable in the actual plant.
5) Repeat the above two steps 3 and 4 until the optimal transition pattern is found.

Compilation of process data
Creation of an initialcondition

DCS
PID+FF+ Sequence

Data
Exchange

Simulation during
operating procedures
Evaluation of atransition pattern

Estimation of model
parameters in responseto plant conditions

Prediction by
high-speed simulation

Transition pattern(1)

Determination of
optimal operations

(2)

(3)
(4)

Operating procedure
set-up for a DCS

time

Plant

Figure 5. Determination of Optimal Operating Proceduresand Integration with Control Systems
4.3 Simulator Overview
This section describes the dynamic simulator contributing as a key factor in the constructed system.
The components of the dynamic simulator include: plant models and control system models.
(1) Plant Models
The plant models represented in the dynamic simulator include polymerization reactors, heat exchangers,
control valves and measurement equipment. In the reactor model, the reaction rate is calculated
through equations considering the occurrence of changes in the concentration of the catalyst, monomer,

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ISA EXPO98 October 22, 1998
AUTO04 Applying state-of-the-art Technology to Control

comonomer, hydrogen and etc. In addition, the average molecular weight that expresses the physicalproperties of a polymer and the copolymer compounds are calculated in the dynamic simulator. The
controlling of the reactor temperatures is the key factor in the polymerization processes. Thus, the heattransfer coefficients of the heat exchanger and the characteristics of the control valves are carefully finetuned so that the temperatures are controlled in the same manner as in the actual processes.
(2) Control System Models
The modeling of DCS logic control, which functions in the same manner as the actual one, is performedin the dynamic simulator. The actual control parameters are also utilized.

Heat of Reaction(3) Simulator Fidelity
The dynamic simulator must have the ability to determine if the resulting set of transition pattern data isapplicable to the actual plant. Fidelity and reliability are the two most important factors required in the
dynamic simulator. For this reason, thorough testing and inspection of the models were conducted in
our application to ensure that their qualities were satisfactory. Figure 6 shows the results of the heat ofreaction in two different reactors A and B during a grade transition, comparing the actual data andsimulation calculation values. It is clear that the actual and simulation data are very closely matched inboth reactors.

Reactor A (Simulation)
Reactor A (Actual Data)
Reactor B (Simulation)
Reactor B (Actual Data)

Pre-grade

Transition

Post-gradeTime

Figure 6. Heat of Reaction during a Grade Transition
4.4 Achievements
The following are the effects of this system for the automation and optimization of grade transitions.
1) After the optimization of a transition pattern was implemented through simulation, plant operations
were stabilized and the transition time was minimized. Thus, the production of off-spec products
was effectively reduced, resulting in a significant reduction of the total production cost of the
products.

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AUTO04 Applying state-of-the-art Technology to Control

2) Through the automation of grade change operations, the operators’ tasks were reduced andstandardized.
3) It became easier to create new grade transition patterns.
4) Delivery of the technology information on the creation of transition patterns was successfully
achieved.
5.

Conclusion

It is of utmost importance that the plant model constructed in a dynamic simulator be commonly appliedin the off-line, quasi-online and online systems as a “simulated plant.” We particularly believe that it isan extremely effective approach to fully utilize the dynamic simulator for operational support and thedevelopment of advanced control to realize the advancement of plant operations.

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