- Email: [email protected]
Improvement of the design and operation of desalination plants by computer modelling and simulation
253
Desalination, 92 (1993) 253-269 Elsevier Science Publishers B.V., Amsterdam
Improvement of the design and operation of desalination plants by computer modelling and simulation Mohammad T. Rahbar Aspen Tech UK, Sheraton House, Castle Park, Cambridge, CB3 OAX (U.K.) Tel. + 44 223-312220: Fax. + 44 223-66980
SUMMARY
This paper describes the challenge and benefits of improving the design and operations of desalination and co-generation plants. Both the design and the control aspects of these processes are considered. It explains how steady state and dynamic simulation can lead to better design and improved control. The paper highlights the economic benefits which can be achieved by using computer modelling and process simulation. It presents some industrial applications of ASPEN PLUS”’ and SPEEDUPTUfor design, optimization, and control system studies of desalination and co-generation plants.
INTRODUCTION
The dependency on desalination plants to produce fresh water from sea water is increasing throughout the world. This has created considerable interest in investigating ways to improve the design and optimize the operation of desalination and co-generation plants. Three major problems have been identified with existing plants: 1. These plants are typically overdesigned by a significant ‘safety’ factor. The factor is large due to a lack of fundamental understanding of the desalination process. This leads to excessively conservative designs which means that the plants are more expensive to build and are more expensive to operate. OOl l-91fj4/93/$06.00
cb 1993 Elsevier Science Publishers B.V.
All rights ,reserved.
254
2. The plants are difficult to operate. Existing Multi-Stage Flash (MSF) desalination processes are integrated, with strong interactions between the stages. The Single Input and Single Output (SISO) control schemes currently employed only handle one single unit operation. Because they do not consider the whole process, their span of control is inadequate to optimize the integrated process and as a result are difficult to tune. Such poor control leads to frequent plant shutdowns, which are costly due to loss of product. 3. The co-generation plant associated with a desalination process often operates inefficiently. The operating conditions followed aresub-optimal and can be substantially improved. In order to optimize the process, the above problems need to be investigated simultaneously in an integrated fashion rather than separately. As experience in the chemical industry has shown, the degree of difficulty in controlling a plant is closely related to its design. In many cases some modifications to the plant has been necessary to improve the controlof the process. We must, therefore, check the design of the plant and its associated control schemes before they are implemented. A powerful method of checking and studying the design and its operability is by the use of computer modelling and simulation. Steady state and dynamic simulation help to test and validate the design before it is implemented both to avoid costly modifications and to ensure safe operations. Steady-state simulation allows alternative designs to be investigated in order to choose the best one. Dynamic simulation enables engineers to study the control systems and investigate the operability and controllability of the plant. The mathematical models developed during the modelling work can be used for operator training, on-line optimization, model-based control, and fault diagnostics. This paper describes ASPEN PLUS and its applications in power plant design. It presents the facilities available in the SPEEDUP system for dynamic modelling and simulation and explains the interfaces available to control system design packages and to the industrial digital control systems for on-line optimization.
ASPEN PLUS WITH MODELMANAGERtY
ASPEN PLUS is generally considered to be the world’s most advanced steady state simulator. Its interactive, expert system based graphical front end called ModelManager allows new and experienced users to build models
Fig. 1. Model definition in ModelManager.
of a process graphically. The model of each unit is either selected from the library or defined by the user. They are then placed on the screen and connected to define the total flowsheet, which can then be simulated. Fig. 1 shows the graphical presentation of a process in ASPEN PLUS. For certain power plant design studies often being simulated in ASPEN PLUS, advanced users may also wish to employ STEAMSYS”, a customized module which enables the user to simulate steam and power generation systems quickly and rigorously. STEAMSYS reports heat and material balances and equipment performance.
STEAM SYSTEM SIMULATOR:
STEAMSYS’”
STEAMSYS uses an equation-solving approach designed to specifically address the unique characteristics of steam systems, such as flexible specifications, multiple stream recycles, and non-sequential information flow. These calculations may be included as part of a fully integrated ASPEN PLUS flowsheet model, enabling engineers to simulate and optimize processes together with their steam system. STEAMSYS is a layered product which runs together with ASPEN PLUS. It can be used both at the design stage and during the plant operation. In the design phase of a new project, STEAMSYS enables engineers to perform steam system design more quickly. Initial design can be done in days rather than weeks; design modifications and steam reduction cases can be performed in minutes rather than hours. The results are completely rigorous. The overall process design phase can thus be completed more quickly and reliably. Because users are able to consider more design cases
more quickly, better designs are created which reduce both capital and operating costs. In the operating plant, a STEAMSYS model of the working steam system enables quick response to short-term process changes as well as optimization of operating conditions for longer-term process modifications. The ability to perform “what if” scenarios with STEAMSYS enables more reliable and efficient operation of steam utilities, resulting in lower operating costs. STEAMSYS is used by major international companies involved in the design and implementation of power plants.
SPEEDUPm DYNAMIC SIMULATION
ENVIRONMENT
SPEEDUP is a Differential and Algebraic Equation (DAE) based equation oriented simulation language. It may be used for steady state simulation, dynamic simulation, optimization, parameter estimation, and data reconciliation. Fig. 2 shows the structure of the SPEEDUP environment. The executive maintains a database which contains the problem description defined by the user. The executive will, on command, translates the problem which is then passed to the run time system to be solved by I /
user \
/ SpedIicalion
He’p 1-H
I
Fig. 2. SPEEDUP environment.
Fbwsheet
Consrmcfon
257
numerical method chosen by the user. The run time system consists of a collection of numerical methods for solving large systems of nonlinear DAEs, routines for physical property calculation, and a run time database. The presentation tools provide the user with a number of facilities for tabulating and plotting results. It also allows the generation of user defined reports for documentation purposes. The executable program generated as a result of the translation may be transferred and used as a stand alone program. In this case the user cannot modify the problem structure but can perform experiments on it. This is useful as the expert modeller develops the model and tunes the simulation using tbe SPEEDUP environment and generates the executable program for use by the plant operator or a client. The stand alone executable can be interfaced to a graphical front-end for operator training, an expert system for plant diagnostics, or Distributed Control Systems for on-line simulation. The External Data Interface in SPEEDUP is used to interface these systems to the simulation program. INTERFACES TO SPEEDUP
SPEEDUP has an open architecture, which includes a high level language for defining models and generic interfaces for coupling the executable code to any external program or system. The generic interfaces available in SPEEDUP include: l
l
l
l
Generalized Physical Property Interface (GPPI) allows interfacing SPEEDUP to commercial and in house physical property packages. Standard interfaces already exist for packages such as PPDS and PROPERTIES PLUS. Graphical Management System (GMS) allows commercial graphical routines to be used with SPEEDUP. Currently SPEEDUP uses GINO routines for its graphics plotting environment. External Data Interface (EDI) is a versatile interface allowing SPEEDUP to communicate with a software package or a plant. For example, expert systems such as G2, control design packages such as CONNOISSEUR, distributed control systems such as Eckardt and Advanced Control System (ACS), and IBM’s Real-time Plant Management System (RTPMS). Control Design Interface (CDI) generates linearized state space model of a the SPEEDUP nonlinear model. The state space model can then be loaded into CACSD packages such as MATLAB, MatrixX, etc. for control design and synthesis.
258
Fig. 3. Interfaces to SPEEDUP.
Fig. 3 shows the generic interfaces available in SPEEDUP. Here, we only consider the external data interface and the control design interface. EXTERNAL
DATA INTERFACE
The External Data Interface (EDI) is a mechanism for passing data to and from a simulation from other software tools. ED1 may be used in steady state, optimization and dynamic run modes. It is very flexible, and has been used for a wide range of applications. ED1 consists of six main routines. The tasks that each routine performs may be divided into the following categories: initialization of the interface, data acquisition from external system/process, interrupt handling, data transmission to external system, synchronization, and termination. These routines are easily customized for a particular system. Three main uses of ED1 in industry are to interface the simulation program to expert systems for plant diagnostics, to DCS or DCS emulators for operator training and on-line optimization and control. CONTROL DESIGN INTERFACE
This facility enables a linearized version of a model to be produced. The linearization is in the form: dx/dt = Ax(t) + Bu(t) Y(t) = Cx(t) + Du(t) where t is time, x(t) is a vector of differential variables in the model, u(t) is
259
a vector of chosen control inputs, y(t) is a vector of chosen control outputs, and A ,B ,C, and D are constant matrices of appropriate dimensions. They are in fact the products of CDI, written to a file for use by other packages. The x, y, and u are the perturbation variables, representing the deviations of the original variables from the chosen point of linearization. The state space matrices generated by SPEEDUP can be loaded to control design packages such as MATLAB, MatrixX, etc. for controllability studies, controller design and synthesis. When used with a control design package such as MATLAB, CD1 helps the user to select a controllable process design. It also helps to choose the best combination of controlled and manipulated variables for the control system. Both of these will result in better process control, leading to a better quality product. INDUSTRIAL
APPLICATIONS OF ASPEN PLUS AND SPEEDUP
ASPEN PLUS with STEAMSYS has been used for: Simulation of Multi Bed Combustion boilers for process design, project engineering, and evaluation of site measurements. Calculation of performance of waste heat recovery boilers. Design study of Kalina cycle, which is an organic Rankin Cycle. Design of HAT cycle, which includes gas turbine with heavy injection of water or steam. Study of topping combuster of a Pressurized Fluidized Bed Combustion.
Sdsr feed water pump
Fig. 4. Flowsheet for sample problem.
260
Fig. 4 and Table I show a sample example of STEAMSYS with its operating conditions. TABLE I
Operating conditions for sample problem Unit name
Pressure (PSW
HPSTEAM IPSTEAh4 LPSTEAM
1000 500 20
Unit name
Efficiency
Power (HP)
Outlet pressure (PSIA)
HPTuRF3 LPTURB
0.85 0.80
2000
500 20
Pressure headers: High pressure steam Intermediate pressure steam Low pressure steam
TMJhK?S: High pressure Low pressure
Deaerator: Pressure Vent flow
20 PSIA 10 LB/h
Boiler feed water pump: Outlet pressure Efficiency
1000 PSIA 0.6
Boiler: Blow-down fraction Pressure
0.01 1010 PSIA
Superheater: 50 degrees of superheat Import condensate
(from process): Flow Pressure
1000 lb/h 20 PSIA
Export steam (no process): Pressure
1000 PSIA
261 DYNAMIC MODELLING AND SIMULATION OF A DESALINATION PLANT
The desalination plant under study is a 18 stage Multi-Stage Flash (MSF) desalination process. A schematic diagram of the process is shown in Fig. 5. Several problems have been identified with the process: 1. The control of the level in each flash stage is crucial in maintaining the stability of the process. The efficiency of the plant is substantially reduced if the levels are not controlled properly. The controllers employed is of type PI and its performance is poor. Large dead time, continuous changes in the process condition such as heat exchanger fouling, ambient condition, brine condition (e.g. temperature, conductivity) make it difficult to achieve “good” control. 2. The thermal efficiency (freshwater/steam ratio) of the plant is currently at 7 % , which can be substantially improved. 3. The plant is operating below its maximum capacity. The objective was to investigate these problems using computer modelling and simulation. A rigorous dynamic model of the process was developed and implemented using SPEEDUP. The model was executed in steady state and the results were compared with the design data. The model was then run dynamically open loop and results showed good agreement with the data from the plant. Disturbances were introduced in the steam flow to the brine heater, sea water temperature, and sea water flow to obtain the open loop responses. Once the confidence in the model was established then the following main control loops were added to the model one by one and tuned. Top brine temperature
The brine temperature at the brine heater outlet is controlled by manipulating the steam flow into the heater. A PID controller was employed to achieve the desired control. Lust stage brine level
The level in the last stage was controlled by manipulating the brine blow down valve. The levels in the preceding stages are directly related to the last stage level so a good control is essential in keeping the whole process stable. However, the counter current flow of sea water supply and the brine flow
262
~)________________~ I
L 8%
j
s*
i 0
m--K: ,
___-
Y
:
P
-1
1
2
e 5
!--El45[ I
w
u.
7
33 1 -_-_-____-____A
r
P
263
in the evaporators means that the level in the evaporators are influenced not only by changes in the feed but also by changes in the process and ambient conditions. Also, the dead time in the process prevents good control to be achieved. Effort was spent in tuning the controller to eliminate the oscillation in the levels. A tight control was achieved by setting the gain to a large value. Brine recirculation flow
The brine recirculation flow from the rejection stage to the recovery stage has to be controlled as the product flow rate changes. Any changes in the recycle flow will also affect the levels in all stages. Figs. 6-9 show SPEEDUP simulation results to step a change to the steam flow to the brine-heater. The dynamic simulation study showed some very interesting results. First, the mathematical model was a good representation of the process. Both the steady state and the open loop dynamic results showed good agreement with the design and the operating data. The difficulties faced during the simulation in tuning the controllers and also the oscillation of the brine level in the evaporators were confirmed by the plant operator. Second, the MSF process is an integrated and multivariable system and strong interaction exist among stages. A multivariable control study of the plant would identify the best paring of the manipulated and control variables in order to minimize the interaction among them. Third, optimization of the process will help to improve the operating conditions to increase the throughput and minimize the steam consumption. Fourth, the models can be used to teach the operators how to run the plant efficiently and how to respond to upsets in the plant.
OPTIMIZATION OF A COMBINED HEAT AND POWER SYSTEM
The Combined Heat and Power (CHP) system shown in Fig. 10 produces superheated high pressure (HP) steam and saturated intermediate (IP) and low pressure (LP) steam. The turbines provide the necessary let-down between the steam offtake, and hence produce electricity by driving alternatars .
This process is similar to a co-generation plant which produces electricity from high pressure steam and passes the LP steam to the brine heater in the desalination plant.
264 SpeedUp
Axial
Profiles
Plot
x10-2
60:
O
59.
t I_ 5
58:
iii
57:
5 I 564.........,.........,.........,......... 0
s
10
‘0
15
INDEX
Fig. 6. Level in each stage at steady state.
SpeedUp
x&J-2
Dynamic
Simulation
x10-1
I 61.
=
60:
9:
=
..,,,,..__...,,,,_.__,._. 0
2
_,....,,, 4
Tmc
Fig. 7. Level response to disturbance in steam flow.
6
._.,___ 6 CntnuLcs
.,._,._,, 10
>
2 x10’
265 SpeedUp
Axial
?roFlles
Plot
x10’ 7s_
75
70:
8 t ; U
c
3
40
__,._.___,,.___,__.,__. 0
5
1s
IO
I NOEX
Fig. 8. Brine temperature profile in each stage.
SpeedUp
Dynamic
Simulation
: I
\ i i 1
2
$
6
Tms
Fig. 9. Brine temperature change to disturbance in steam flow.
6 (mruLcs
10 )
x10’
-a
Lp
TAZB
ke
IPexpotl
5 IP
omk
expoll
i
Fig. 10. CHP schematic.
Since the CHP process is highly energy intensive then small improvement in the operation can result to large savings. The objective of this study was to optimize the process to maximize the profit, taking into account factors such as fuel cost and utility import/export costs. Process description
Cold feed water is fed to a deaerator via a pre-heater. The deaerator pressure is set at 2.8 bar. The feed water mixes with the recycle flows and is then pumped up to a pressure of 42.39 bar and fed to six boilers in parallel, after further pre-heating. The boilers produce the required high pressure steam which is then fed to the first turbine set. A blowdown from the boilers of 5% of steam flow is
267
flashed to IP steam pressure. the liquid fraction is used to preheat the deaerator feed, and the vapour fraction is fed to the IP steam section. A pressure drop across the turbine and a temperature drop based on real gas laws produces work, which is transformed into electricity by an alternator. IP steam is used to preheat the feed water to the boiler; Any remaining IP steam is recycled to the deaerator. Optimim’on The optimum cold water feed rate to the CHP plant, and IP steam recycle rate are determined in order to maximize plant profit. The objective function is included in the simulation. Each Profit/Cost function is calculated separately, and incorporated into the overall profit function, which is to be maximized. The objective function to be maximized is: (15 * Turbine power produced) - (400 * oil flowrate to boiler) (37* Electricity duty to boiler feed pump) - (1 .O * flow of cold water to deaerator) Where the electricity export value is $lS/MWhr, the electricity import cost is $37/tonne and the cost of cold water is $l/tonne. The objective function includes the constraints that the minimum flow through the turbine is 1 tonne/hr and the maximum boiler feed-water temperature is 254°C.
RESULTS
Running the problem under steady state condition produces the base case results in Table II. This shows that with a cold water feed rate of 200 tonne per hour, and an IP steam recycle factor of 0.25, the plant is running at a loss of $175.96 per hour. Following an optimization run, the cold water feed rate and IP steam recycle factors are calculated to produce a maximized value of the profit function as defined in the simulation. The optimized case shows that the plant would make a profit of $39.90 per hour with an increased cold water feed rate and IP- steam recycle factor. The results also show that, at the optimum operating point, both turbines produce more power. The boiler uses half the oil whilst the boiler feed pump uses only marginally greater power.
268 TABLE II
Optimization results Descrivtion
Base case
Optimized
Cold water feed rate IP steam recycle factor Profitability TURBINE1 A power TURBINE1 B power BOILER1 oil flow PUMP1 work
200 t/h 0.25 - 175.96 El/h 8.75 MW 8.41 MW 0.15 t/h 1.37 GJ/h
217.43 t/h 0.345 39.90 E/h 10.80 MW 9.11 Mw 0.072 t/h 1.63 JG/h
This simulation could be run on-line to advise optimum feed and recycle rates under changing conditions of import/export steam flowrates and cost factors.
CONCLUSIONS
The efficient operation of a plant and the quality of the product are dictated by a good design and an effective control strategy. It is, therefore, very important that both the design and the control system be checked and simulated before they are implemented. An increasing number of companies are now aware that steady state and dynamic simulation are powerful tools for plant design, control system studies, safety studies, and plant optimization. The flowsheeting capabilities of ASPEN PLUS with ModelManager and SPEEDUP provide the process engineer with the flexibility to investigate systems to any level of complexity and he or she can quickly create, adapt, or expand simulations of sub systems into full system simulations and ultimately complete plants. ASPEN PLUS and SPEEDUP are becoming increasingly integrated. Already they share a common physical properties system called PROPERTIES PLUS“ for SPEEDUP. Generic interfaces in SPEEDUP make it easy to integrate the modelling work with existing control system design tools as well allowing the user to satisfy individual requirements and applications. ASPEN PLUS and SPEEDUP simulation environments offer the flexibility to perform steady state, dynamic and optimization studies. They provide a consistent framework within which these tasks can be performed enabling the various disciplines to work together more effectively in the provision of higher quality engineering solution.
269 ACKNOWLEDGEMENT
The author wishes to express his appreciation to Dr. Darwish Al-Gobaisi, Director General of Water and Electricity Department in Abu Dhabi, for his pioneering vision to sponsor this work. The author also wishes to thank Mr. Sven Gunnar Sundkvist of ABB Stal and Mr. Kenneth Morse and Dr. S. Venkataraman of ASPEN Technology Inc. for their swift and thoughtful review of earlier version of this manuscript.
Desalination, 92 (1993) 253-269 Elsevier Science Publishers B.V., Amsterdam
Improvement of the design and operation of desalination plants by computer modelling and simulation Mohammad T. Rahbar Aspen Tech UK, Sheraton House, Castle Park, Cambridge, CB3 OAX (U.K.) Tel. + 44 223-312220: Fax. + 44 223-66980
SUMMARY
This paper describes the challenge and benefits of improving the design and operations of desalination and co-generation plants. Both the design and the control aspects of these processes are considered. It explains how steady state and dynamic simulation can lead to better design and improved control. The paper highlights the economic benefits which can be achieved by using computer modelling and process simulation. It presents some industrial applications of ASPEN PLUS”’ and SPEEDUPTUfor design, optimization, and control system studies of desalination and co-generation plants.
INTRODUCTION
The dependency on desalination plants to produce fresh water from sea water is increasing throughout the world. This has created considerable interest in investigating ways to improve the design and optimize the operation of desalination and co-generation plants. Three major problems have been identified with existing plants: 1. These plants are typically overdesigned by a significant ‘safety’ factor. The factor is large due to a lack of fundamental understanding of the desalination process. This leads to excessively conservative designs which means that the plants are more expensive to build and are more expensive to operate. OOl l-91fj4/93/$06.00
cb 1993 Elsevier Science Publishers B.V.
All rights ,reserved.
254
2. The plants are difficult to operate. Existing Multi-Stage Flash (MSF) desalination processes are integrated, with strong interactions between the stages. The Single Input and Single Output (SISO) control schemes currently employed only handle one single unit operation. Because they do not consider the whole process, their span of control is inadequate to optimize the integrated process and as a result are difficult to tune. Such poor control leads to frequent plant shutdowns, which are costly due to loss of product. 3. The co-generation plant associated with a desalination process often operates inefficiently. The operating conditions followed aresub-optimal and can be substantially improved. In order to optimize the process, the above problems need to be investigated simultaneously in an integrated fashion rather than separately. As experience in the chemical industry has shown, the degree of difficulty in controlling a plant is closely related to its design. In many cases some modifications to the plant has been necessary to improve the controlof the process. We must, therefore, check the design of the plant and its associated control schemes before they are implemented. A powerful method of checking and studying the design and its operability is by the use of computer modelling and simulation. Steady state and dynamic simulation help to test and validate the design before it is implemented both to avoid costly modifications and to ensure safe operations. Steady-state simulation allows alternative designs to be investigated in order to choose the best one. Dynamic simulation enables engineers to study the control systems and investigate the operability and controllability of the plant. The mathematical models developed during the modelling work can be used for operator training, on-line optimization, model-based control, and fault diagnostics. This paper describes ASPEN PLUS and its applications in power plant design. It presents the facilities available in the SPEEDUP system for dynamic modelling and simulation and explains the interfaces available to control system design packages and to the industrial digital control systems for on-line optimization.
ASPEN PLUS WITH MODELMANAGERtY
ASPEN PLUS is generally considered to be the world’s most advanced steady state simulator. Its interactive, expert system based graphical front end called ModelManager allows new and experienced users to build models
Fig. 1. Model definition in ModelManager.
of a process graphically. The model of each unit is either selected from the library or defined by the user. They are then placed on the screen and connected to define the total flowsheet, which can then be simulated. Fig. 1 shows the graphical presentation of a process in ASPEN PLUS. For certain power plant design studies often being simulated in ASPEN PLUS, advanced users may also wish to employ STEAMSYS”, a customized module which enables the user to simulate steam and power generation systems quickly and rigorously. STEAMSYS reports heat and material balances and equipment performance.
STEAM SYSTEM SIMULATOR:
STEAMSYS’”
STEAMSYS uses an equation-solving approach designed to specifically address the unique characteristics of steam systems, such as flexible specifications, multiple stream recycles, and non-sequential information flow. These calculations may be included as part of a fully integrated ASPEN PLUS flowsheet model, enabling engineers to simulate and optimize processes together with their steam system. STEAMSYS is a layered product which runs together with ASPEN PLUS. It can be used both at the design stage and during the plant operation. In the design phase of a new project, STEAMSYS enables engineers to perform steam system design more quickly. Initial design can be done in days rather than weeks; design modifications and steam reduction cases can be performed in minutes rather than hours. The results are completely rigorous. The overall process design phase can thus be completed more quickly and reliably. Because users are able to consider more design cases
more quickly, better designs are created which reduce both capital and operating costs. In the operating plant, a STEAMSYS model of the working steam system enables quick response to short-term process changes as well as optimization of operating conditions for longer-term process modifications. The ability to perform “what if” scenarios with STEAMSYS enables more reliable and efficient operation of steam utilities, resulting in lower operating costs. STEAMSYS is used by major international companies involved in the design and implementation of power plants.
SPEEDUPm DYNAMIC SIMULATION
ENVIRONMENT
SPEEDUP is a Differential and Algebraic Equation (DAE) based equation oriented simulation language. It may be used for steady state simulation, dynamic simulation, optimization, parameter estimation, and data reconciliation. Fig. 2 shows the structure of the SPEEDUP environment. The executive maintains a database which contains the problem description defined by the user. The executive will, on command, translates the problem which is then passed to the run time system to be solved by I /
user \
/ SpedIicalion
He’p 1-H
I
Fig. 2. SPEEDUP environment.
Fbwsheet
Consrmcfon
257
numerical method chosen by the user. The run time system consists of a collection of numerical methods for solving large systems of nonlinear DAEs, routines for physical property calculation, and a run time database. The presentation tools provide the user with a number of facilities for tabulating and plotting results. It also allows the generation of user defined reports for documentation purposes. The executable program generated as a result of the translation may be transferred and used as a stand alone program. In this case the user cannot modify the problem structure but can perform experiments on it. This is useful as the expert modeller develops the model and tunes the simulation using tbe SPEEDUP environment and generates the executable program for use by the plant operator or a client. The stand alone executable can be interfaced to a graphical front-end for operator training, an expert system for plant diagnostics, or Distributed Control Systems for on-line simulation. The External Data Interface in SPEEDUP is used to interface these systems to the simulation program. INTERFACES TO SPEEDUP
SPEEDUP has an open architecture, which includes a high level language for defining models and generic interfaces for coupling the executable code to any external program or system. The generic interfaces available in SPEEDUP include: l
l
l
l
Generalized Physical Property Interface (GPPI) allows interfacing SPEEDUP to commercial and in house physical property packages. Standard interfaces already exist for packages such as PPDS and PROPERTIES PLUS. Graphical Management System (GMS) allows commercial graphical routines to be used with SPEEDUP. Currently SPEEDUP uses GINO routines for its graphics plotting environment. External Data Interface (EDI) is a versatile interface allowing SPEEDUP to communicate with a software package or a plant. For example, expert systems such as G2, control design packages such as CONNOISSEUR, distributed control systems such as Eckardt and Advanced Control System (ACS), and IBM’s Real-time Plant Management System (RTPMS). Control Design Interface (CDI) generates linearized state space model of a the SPEEDUP nonlinear model. The state space model can then be loaded into CACSD packages such as MATLAB, MatrixX, etc. for control design and synthesis.
258
Fig. 3. Interfaces to SPEEDUP.
Fig. 3 shows the generic interfaces available in SPEEDUP. Here, we only consider the external data interface and the control design interface. EXTERNAL
DATA INTERFACE
The External Data Interface (EDI) is a mechanism for passing data to and from a simulation from other software tools. ED1 may be used in steady state, optimization and dynamic run modes. It is very flexible, and has been used for a wide range of applications. ED1 consists of six main routines. The tasks that each routine performs may be divided into the following categories: initialization of the interface, data acquisition from external system/process, interrupt handling, data transmission to external system, synchronization, and termination. These routines are easily customized for a particular system. Three main uses of ED1 in industry are to interface the simulation program to expert systems for plant diagnostics, to DCS or DCS emulators for operator training and on-line optimization and control. CONTROL DESIGN INTERFACE
This facility enables a linearized version of a model to be produced. The linearization is in the form: dx/dt = Ax(t) + Bu(t) Y(t) = Cx(t) + Du(t) where t is time, x(t) is a vector of differential variables in the model, u(t) is
259
a vector of chosen control inputs, y(t) is a vector of chosen control outputs, and A ,B ,C, and D are constant matrices of appropriate dimensions. They are in fact the products of CDI, written to a file for use by other packages. The x, y, and u are the perturbation variables, representing the deviations of the original variables from the chosen point of linearization. The state space matrices generated by SPEEDUP can be loaded to control design packages such as MATLAB, MatrixX, etc. for controllability studies, controller design and synthesis. When used with a control design package such as MATLAB, CD1 helps the user to select a controllable process design. It also helps to choose the best combination of controlled and manipulated variables for the control system. Both of these will result in better process control, leading to a better quality product. INDUSTRIAL
APPLICATIONS OF ASPEN PLUS AND SPEEDUP
ASPEN PLUS with STEAMSYS has been used for: Simulation of Multi Bed Combustion boilers for process design, project engineering, and evaluation of site measurements. Calculation of performance of waste heat recovery boilers. Design study of Kalina cycle, which is an organic Rankin Cycle. Design of HAT cycle, which includes gas turbine with heavy injection of water or steam. Study of topping combuster of a Pressurized Fluidized Bed Combustion.
Sdsr feed water pump
Fig. 4. Flowsheet for sample problem.
260
Fig. 4 and Table I show a sample example of STEAMSYS with its operating conditions. TABLE I
Operating conditions for sample problem Unit name
Pressure (PSW
HPSTEAM IPSTEAh4 LPSTEAM
1000 500 20
Unit name
Efficiency
Power (HP)
Outlet pressure (PSIA)
HPTuRF3 LPTURB
0.85 0.80
2000
500 20
Pressure headers: High pressure steam Intermediate pressure steam Low pressure steam
TMJhK?S: High pressure Low pressure
Deaerator: Pressure Vent flow
20 PSIA 10 LB/h
Boiler feed water pump: Outlet pressure Efficiency
1000 PSIA 0.6
Boiler: Blow-down fraction Pressure
0.01 1010 PSIA
Superheater: 50 degrees of superheat Import condensate
(from process): Flow Pressure
1000 lb/h 20 PSIA
Export steam (no process): Pressure
1000 PSIA
261 DYNAMIC MODELLING AND SIMULATION OF A DESALINATION PLANT
The desalination plant under study is a 18 stage Multi-Stage Flash (MSF) desalination process. A schematic diagram of the process is shown in Fig. 5. Several problems have been identified with the process: 1. The control of the level in each flash stage is crucial in maintaining the stability of the process. The efficiency of the plant is substantially reduced if the levels are not controlled properly. The controllers employed is of type PI and its performance is poor. Large dead time, continuous changes in the process condition such as heat exchanger fouling, ambient condition, brine condition (e.g. temperature, conductivity) make it difficult to achieve “good” control. 2. The thermal efficiency (freshwater/steam ratio) of the plant is currently at 7 % , which can be substantially improved. 3. The plant is operating below its maximum capacity. The objective was to investigate these problems using computer modelling and simulation. A rigorous dynamic model of the process was developed and implemented using SPEEDUP. The model was executed in steady state and the results were compared with the design data. The model was then run dynamically open loop and results showed good agreement with the data from the plant. Disturbances were introduced in the steam flow to the brine heater, sea water temperature, and sea water flow to obtain the open loop responses. Once the confidence in the model was established then the following main control loops were added to the model one by one and tuned. Top brine temperature
The brine temperature at the brine heater outlet is controlled by manipulating the steam flow into the heater. A PID controller was employed to achieve the desired control. Lust stage brine level
The level in the last stage was controlled by manipulating the brine blow down valve. The levels in the preceding stages are directly related to the last stage level so a good control is essential in keeping the whole process stable. However, the counter current flow of sea water supply and the brine flow
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in the evaporators means that the level in the evaporators are influenced not only by changes in the feed but also by changes in the process and ambient conditions. Also, the dead time in the process prevents good control to be achieved. Effort was spent in tuning the controller to eliminate the oscillation in the levels. A tight control was achieved by setting the gain to a large value. Brine recirculation flow
The brine recirculation flow from the rejection stage to the recovery stage has to be controlled as the product flow rate changes. Any changes in the recycle flow will also affect the levels in all stages. Figs. 6-9 show SPEEDUP simulation results to step a change to the steam flow to the brine-heater. The dynamic simulation study showed some very interesting results. First, the mathematical model was a good representation of the process. Both the steady state and the open loop dynamic results showed good agreement with the design and the operating data. The difficulties faced during the simulation in tuning the controllers and also the oscillation of the brine level in the evaporators were confirmed by the plant operator. Second, the MSF process is an integrated and multivariable system and strong interaction exist among stages. A multivariable control study of the plant would identify the best paring of the manipulated and control variables in order to minimize the interaction among them. Third, optimization of the process will help to improve the operating conditions to increase the throughput and minimize the steam consumption. Fourth, the models can be used to teach the operators how to run the plant efficiently and how to respond to upsets in the plant.
OPTIMIZATION OF A COMBINED HEAT AND POWER SYSTEM
The Combined Heat and Power (CHP) system shown in Fig. 10 produces superheated high pressure (HP) steam and saturated intermediate (IP) and low pressure (LP) steam. The turbines provide the necessary let-down between the steam offtake, and hence produce electricity by driving alternatars .
This process is similar to a co-generation plant which produces electricity from high pressure steam and passes the LP steam to the brine heater in the desalination plant.
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Since the CHP process is highly energy intensive then small improvement in the operation can result to large savings. The objective of this study was to optimize the process to maximize the profit, taking into account factors such as fuel cost and utility import/export costs. Process description
Cold feed water is fed to a deaerator via a pre-heater. The deaerator pressure is set at 2.8 bar. The feed water mixes with the recycle flows and is then pumped up to a pressure of 42.39 bar and fed to six boilers in parallel, after further pre-heating. The boilers produce the required high pressure steam which is then fed to the first turbine set. A blowdown from the boilers of 5% of steam flow is
267
flashed to IP steam pressure. the liquid fraction is used to preheat the deaerator feed, and the vapour fraction is fed to the IP steam section. A pressure drop across the turbine and a temperature drop based on real gas laws produces work, which is transformed into electricity by an alternator. IP steam is used to preheat the feed water to the boiler; Any remaining IP steam is recycled to the deaerator. Optimim’on The optimum cold water feed rate to the CHP plant, and IP steam recycle rate are determined in order to maximize plant profit. The objective function is included in the simulation. Each Profit/Cost function is calculated separately, and incorporated into the overall profit function, which is to be maximized. The objective function to be maximized is: (15 * Turbine power produced) - (400 * oil flowrate to boiler) (37* Electricity duty to boiler feed pump) - (1 .O * flow of cold water to deaerator) Where the electricity export value is $lS/MWhr, the electricity import cost is $37/tonne and the cost of cold water is $l/tonne. The objective function includes the constraints that the minimum flow through the turbine is 1 tonne/hr and the maximum boiler feed-water temperature is 254°C.
RESULTS
Running the problem under steady state condition produces the base case results in Table II. This shows that with a cold water feed rate of 200 tonne per hour, and an IP steam recycle factor of 0.25, the plant is running at a loss of $175.96 per hour. Following an optimization run, the cold water feed rate and IP steam recycle factors are calculated to produce a maximized value of the profit function as defined in the simulation. The optimized case shows that the plant would make a profit of $39.90 per hour with an increased cold water feed rate and IP- steam recycle factor. The results also show that, at the optimum operating point, both turbines produce more power. The boiler uses half the oil whilst the boiler feed pump uses only marginally greater power.
268 TABLE II
Optimization results Descrivtion
Base case
Optimized
Cold water feed rate IP steam recycle factor Profitability TURBINE1 A power TURBINE1 B power BOILER1 oil flow PUMP1 work
200 t/h 0.25 - 175.96 El/h 8.75 MW 8.41 MW 0.15 t/h 1.37 GJ/h
217.43 t/h 0.345 39.90 E/h 10.80 MW 9.11 Mw 0.072 t/h 1.63 JG/h
This simulation could be run on-line to advise optimum feed and recycle rates under changing conditions of import/export steam flowrates and cost factors.
CONCLUSIONS
The efficient operation of a plant and the quality of the product are dictated by a good design and an effective control strategy. It is, therefore, very important that both the design and the control system be checked and simulated before they are implemented. An increasing number of companies are now aware that steady state and dynamic simulation are powerful tools for plant design, control system studies, safety studies, and plant optimization. The flowsheeting capabilities of ASPEN PLUS with ModelManager and SPEEDUP provide the process engineer with the flexibility to investigate systems to any level of complexity and he or she can quickly create, adapt, or expand simulations of sub systems into full system simulations and ultimately complete plants. ASPEN PLUS and SPEEDUP are becoming increasingly integrated. Already they share a common physical properties system called PROPERTIES PLUS“ for SPEEDUP. Generic interfaces in SPEEDUP make it easy to integrate the modelling work with existing control system design tools as well allowing the user to satisfy individual requirements and applications. ASPEN PLUS and SPEEDUP simulation environments offer the flexibility to perform steady state, dynamic and optimization studies. They provide a consistent framework within which these tasks can be performed enabling the various disciplines to work together more effectively in the provision of higher quality engineering solution.
269 ACKNOWLEDGEMENT
The author wishes to express his appreciation to Dr. Darwish Al-Gobaisi, Director General of Water and Electricity Department in Abu Dhabi, for his pioneering vision to sponsor this work. The author also wishes to thank Mr. Sven Gunnar Sundkvist of ABB Stal and Mr. Kenneth Morse and Dr. S. Venkataraman of ASPEN Technology Inc. for their swift and thoughtful review of earlier version of this manuscript.