- Email: [email protected]
On-line optimization of an ethylene plant
ON-LINE OPTIMIZATION OF AN ETHYLENE PLANT
U.E. Iauks", R.J. Vasbinder", P.J. Valkenburg2) and C. van Leeuwen2)
2)
1) OMV Deutschland GmbH, Burghausen, Germany KTI B.V. PYROTEC Division, Zoetermeer, The Netherlands
ABSTRACT
A simulation model has been developed for the petrochemical complex of OMV Deutschland GmbH, Burghausen (Germany) as part of the Advanced Plant Management System (APMS&). The scope of the work was to make a model that matched plant data with sufficient accuracy to benefit from reconciled plant data and optimized setpoints. This paper describes the plant model, the development phase, the tuning of the model and the on-line and off-line performance. Themodel of the petrochemical complexconsists of the refinery unit, the ethylene plantand downstream treatment units including their interactions. The emphasis in modelling was put on the ethylene plant, composed of ten furnaces with five different geometries and a complexfeedstock system. The furnaces are modelled rigorously with the use of the programs SPYRO&, FIREBOX&, CONVEC and TES&. Models related to other units, e.g. coker, HDS and Pyrotol are based upon empirical relations. The equation oriented flowsheetlnq program TISFLO& is used to ensure fast solutions of the large and complex problem. In total, the plant model consists of more than 5000 linearand non-linear equations. Final model tuning was done on-site in periods of stable plant operation. The predictive power of the model has been confirmed by the reconciliation of over 450 plant data. In optimization a total of 106 constraints and 37 decision variables are present. The results of optimization showthat it is beneficial to modify prevailing operating conditions. The improvements on profit registered in the past are between 1 and 3%.
INTRODUCTION
Computers are nowadays widely used in the chemical industry. When they were first introduced in the 70's it was mainlyin the commercial area and administration. On the other hand, also in the 70'sand 80's a big step forward was done in the field instrumentation and process control area with the introduction of PLC's, DCS and process computers. There have been large improvements on the applied computer technology on both sides. Since the second half of the 80's the gap between these two levels of computerapplications is filled by plant management systems. Already in 1986, KTI's subsidiary, PYROTEC, introduced its concept for such a system (Pyrotechnical News, 1986) APMS. (Advanced Plant Management System). This system is based on plant models and resulted from a long time cooperation with DSM (de Leeuw den Bouter et al., 1982). Recently, the literature reveals a growing interest in such systems, which will be summarized below. An extensive system description has been given (Asbjornsen, 1989). In this articlemaximum profit overa timewindow is mentioned as one of the objectives. In view of this, scheduling operations (Paardekooper et al., 1990) over this time window and optimizing dailyoperations (Barendregtet. al., 1987) arenecessary features. Theoptimization function should supply the operational staff with the required setpoints, according to production demands set forth by the schedule, within plant constraints. 5213
S214
European Symposium on Computer Aided Process Engineering-I
Also data filtering and data reconciliation is recognized as a major task in process information systems (Asbjomsen, 1989).
A consistent material balance as well as trustworthy, hence meaningful, temperatures and pressures are of considerable importance for properplantoperation. Simultaneously, data reconciliation validates the model used for optimization. In this paper, we describe a model for an ethylene plant, developed for above mentioned purposes. As it is important that the model matches the plant accurately, the tuning aspect is addressed. Also experience withon andoff-line useis presented. Butfirstwedigress anddiscuss some recently published achievements.
RECENT APPLICATIONS Systems for on-line optimization have been developed and used overa considerable period of time, but success or failure has largely gone unnoticed. Recently, a series of achievements for large chemical plants have been published. Examples are given In Table 1, which is meant to illustrate that on-line optimization can be profitable. Moreover, the authors seem to agree upon profitability; generally about 3% or 4 M$jyear. Table 1: Examples of the application and benefits of on-line optimization
'::.:::,.11:;:::: :::;::.o.l'li~11: :!!~~::1::::::::~1..:::::::::::::::::::::::::::, :::::::::::::·li9fn;::::•. : 1983 Shell Oil Ethylene Plant 3 - 5% 1986 1988 1990 1990 1990 1990 1991 1991
Ethylene Plant Power Station Refinery GasPlant Crude Unit Ethylene Plant Ethylene Plant Ethylene Plant
Wilton Texaco Amaco Painter Star Enterprise Chevron USA OMV Deutschland Lyondell
4 M$jyr 2 6% 4 M$jyr 4 M$jyr 3 M$jyr 5 . 10% 1 3% 9 mnth
-
Generally, intangible profit in terms of a better understanding of piant behaviour is indicated and acceptance by plantpersonnel is mentioned as a keycondition for successful introduction of the system. The system consists generally of a simulator and a (series of) shell program(s) around it. This shell is rather important as it determines the ease of useand the functionality of the system. The simulator - the heart of the system - mayverywell bea blackbox to the user. Its basicrequirements are accuracy, flexibility, robustness and speed. Hence, for global optimization of large plants, mostly equation solvers have been applied (Cutler et al., 1983, Foster et al., 1987, Saha et al., 1990, Lauks et al., 1991, Davenport et al., 1991). But also the sequential modular method has been used (to solve tear streams) with some optimization algorithm on top (DeHaan et al., 1986, Gibbons et al., 1990). A combination of both methods has been applied (Bi/berry et et., 1990) as well. Also distributed optimization (Ramsey et al., 1990) has been used. Several approaches have been reported in literature to reconcile plant data: from mass balance only to complete model based reconciliation. Also plant section wise reconciliation (Lawrence P.J., 1990)as opposed to global reconciliation has been described. Obviously, global model based reconciliation (as applied here) is the most complex and CPU demanding problem. Its solution, however, is a set of mutually consistent plantdata, adjusted according to technological know-how, as reflected in the models. From an engineering pointof viewthis is the bestonecando. Commercially, the efforts of model building and the contribution to benefits need to be balanced. As solution methods and hardware cost involved clearly pose no problem anymore in this field, the emphasis may be on modelling technology in the future. The key issue will be the suppliers ability to provide an adequate model, I.e. onethat matches the plantover its full operating range. In our opinion, this does not necessarily imply the use of a rigorous or design model for all pieces of equipment.
S215
European Symposium on Computer Aided Process Engineering-l
FACTORY STRUCTURE AND COMPUTER SYSTEM The production facilities of the OMV Deutschland GmbH in Burghausen consists of a refinery part with an atmospheric crude distillation, a coker, a hydro desulphurlzation unit and a petrochemical part with gas treatment, steam crackingfurnaces, quenching, compression andseparation anda gasoline treatment unit with a Pyrotol unit. The refinery is supplied with mainlyUbyancrude. The products of the complex are ethylene, propylene, C4 cut, benzene, Cg cut, jet, diesel, house heating fuel, coke and sulphur. An overview of the plant units Is depicted in Figure 1 which also shows the interconnections.
HYDOODE-
SULPHUR IZATICI'-I
Ethylene Q"ucIe
Naphtha, Gas 011
ATM.
Propylene
CRUDE
C4-eut
DISTIL.
Benzene C9-cut CS -cut
!
~':
Dieselfuel
Coke
~ SYSTEM
•
~ SYSTEM
Fig. 1: Production Facilities of 6MV Deutschland Process control is executed via the TDC 2000 system. All process supervisory controlsare located in the Honeywell 45000 (PMX) computer. It interfaces with the administrational computer hardware for the transfer of process and laboratory data. Thecomputers on the plantmanagement level are connected via Ethernet, serving as process Information system and calculation platform for commercial andadminlstrational purposes. Intothis existing computer grid, an APMS. simulation platform hadto be integrated, making mostuseof the existing facilities. Figure 2 Illustrates the hierarchical structure of the computer system.
THE PLANT MODEL The plant model had to account for the plant wide cross connections of feed and utilities. Recycle streams from up and down stream units havean influence on the amount of fresh feed consumption of the ethylene plant. Hence for optimization the modelling of all refinery and petrochemical units was required. The heart of the plant are the steam crackers with a high impact on the plant's profit. In 10 heaters with 5 different designs, 11 feedstocks have to be handled, which makes up a total of 37 different combinations. For eachheater/feed combination a detailed model for mass flow and energy balance was developed.
CACE 16 Suppl.-P
European Symposium on Computer Aided Process Engineering-l
S216
The convection sections were modelled rigorously with the program CONVEC to supplya detailed mass and energy flow across each of the heat exchanger banks. Each bank was set up as a heat exchanger with a fouling factor as tuning parameter.
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Theperformances of the fireboxes werecalculated rigorously with the program FIREBOXe. This program uses the excess air, the heating value of the fuel, the cokethickness in the coil and the radiant duty from the SPYROf> program to evaluate the maximum tube skin temperature and radiant box efficiency. Fig. 2: System Set-Up and Functionality The SPYRO. program has been used to rigorously calculate the heater effluent composition, coking, absorbed heat duty and the coil outlet temperature for the entire heater operating ranges for the 37 heater/feed combinations. The pressure drop, the outlet temperature and the produced steam of the transfer line exchangers (TLE) were simulated by the program TEse. All rigorously calculated results of the heater sections were regressed by polynomial regression in order to save model run time. The regression data made up the basis for the heater model. During comparisons in the tuning phase it turned out thatthe loss in calculation accuracy by regressing the data is minor. The plant model takesthe different modes of operation (on-line, decoke, stand-by, off-line) into account. The hot section and the raw gas compression were modelled in a simplified manner. The requirements to these models were to estimate the amounts of pyrolysis tar and gasoline, to calculate the produced steam in the hot section and the consumed high pressure steam of the compressor turbine. The cold section consists of models for the hydrogenation reactors. Theamounts of productand recycle streams are calculated according to on-line analysis and product specs. Thegasoline treatment, the Pyrotol and refinery unitswere modelled asmultivariable splitters and reactors based on experimental data. All these models were provided with a number of tuning possibilities to accountfor e.g. the catalyst activity, mode of operation and feed quality changes. Theapproach of modelling the heaters In detailwhilethe restof the plantwas modelled fairly rough, was chosen to achieve accuracy in the area where highest Impact on profitwas expected. The up and down stream units were Intended to supply Information for the material balance and plant constraints which might become relevant for the operation of the heaters. Thisstrategy hasproved itself since it keeps the
5217
European Symposium on Computer Aided Process Engineering-1
model simple and leaves the possibility to substitute a rough approach by a detailed one if it turns out to be necessary. The APMS. simulator has an open model structure. In a set of FORTRAN subroutines, unit operations or parts of unit operations are represented. In standard routines functions like splitters and mixers or interfaces to regression dataarerepresented. In so called application routines functionalities are included, representing empirical models, specific calculations as for Instance the outlet velocity of the radiant coil or sometailor made calculations. Functions maybe linearor non-linear. A consistent solution for all the routines is generated by the equation solver (TISFLQe). Table 2 gives some characteristic figures about the application, describing the size and complexity of the application.
DEVELOPMENT PHASES In the first phase of the project, the plantmodel topologywas developed by drawing a kind of f1owsheet, defining all nodes, stream connections and stream components. After this, a detailed definition of the functionalities represented by a node, were settled. This activity was done in very close cooperation between KTI and OMV Deutschland. Table 2: Plant Model Characteristics Nodes
314
Application routines
71
Streams
777
Standard routines
26
Components
84
Model calls
1063
Decision variables
37
Total equations
5383
Optimization constraints
106
Non-linear equations
:!:1000
Objectivefunctions
4
Derivatives (modelsonly)
12303
In the second phase, the rigorous simulations of the heater sections were executed and the regression coefficients generated. The FORTRAN routines, specific to the OMV application, had to be written and the topology, as defined In the first phase, had to be built up in the strict and specific TISFLO. terminology. Plant data were used to cross check the requested functionality in a factory test. After 3 months of factory development on VAX/VMS and HPjUNIX a basicversion of the simulator was Installed on OMV's Data General AOSfVS for integration in OMV's environment. The complete version was installed 9 months later. In the tuning and testing phase on site, which took about half a year, tuning factors were adjusted and missing topology connections were identified and Implemented. The accuracy of the model was demonstrated to operations by a plant/model test. The heater run length prediction was checked and tuned in long term tests over several decoking periods. The project was closed by the end of 1990. During the development time, two engineers from the contractor and one from the plant were involved. In 1991, the model has been extended to include two new steam crackers. Also new modelling technology has been implemented.
TUNING Thegeneral purpose of thetuningactivityIsto getthe mostaccurate match between plantmeasurements and model predictions. TheSPYROGP program had been In useby OMV Deutschland for a period of over 10years. There wasa clearunderstanding of the qualityof the program. Theresults of this andthe other rigorous programs integrated Into the static OMV application hadto match the wide operating ranges of the production plant. On the other hand, the qualityof tuningIs verymuchdepending on the knowledge aboutthe accuracyof plantmeasurements. Consequently, a compromise Is necessary to keepthe effort Justifiable. Mainly due to the semi-continuous operation of the coker, a reasonable match of the plant material balance was only possible by averaging plant data of a period of 24 hours. The cracking furnaces are
S218
European Symposium on Computer Aided Process Engineering-l
controlled and operated by the ratio of propylene to ethylene or the coil outlet temperature. A full heater effluent analysis was not available. The check of the model accuracy was therefore not based on Individual heaters ratherthanon theoverall plantmaterial balance. Duringthe elaborate processof tuning, there was an effect of learning and understanding the true behaviour of the process. After smoothing and correctingplantdata and with the tuned model, a match of the material balance was achieved as close as 1-3% from previously 3·5%. This match can be kept with the once selected tuning factors as long as there is no significant change in the operation procedure, feedstock quality or the process equipment. Hence tuning factors are not continuously updated. The total number of tuning parameters for the application is about 300. Maintenance of the model is imperative. Theeffort spentwas about half a day per week. Below we will discuss two problems associated with tuning: The hydrogen system was originally modelled without taking the recycle streams into account. This system is rathercomplicated, consisting of 5 hydrogen consumers plus methanation. It usesthe hydrogen from the ethylene plantand a 100% hydrogen from batterylimits. Reconciliation of all plantdata, including datafor the hydrogensystem led to heavydisturbances evenIn the heaterarea. A satisfactory result was gained with the introduction of a recycle factor and adjustments of the hydrogen concentrations with lab analysis. Later in applying the program in routine operation, a problem in the backend acetylene convertorwas discovered. A check of the system indicated a shift of the base line of the inlet acetylene analyzer and as a consequence the hydrogen ratio controller received a wrong setpoint. A very time consuming and long term process was the tuning of the heater run length. The model is equipped with a tuning possibility for base and slope of the radiant and TlE fouling. These parameters were pre-adjusted in a way that the model gave a standard run length for a heater/feed combination. Basis for the run length prediction is the coking rateand the coke precursoras calculated by the SPYRO~ program. For properly operated heaters, the model prediction is satisfactory. Any mal-operation of the heater, badfiring profileor bad feedswitch maycorrupt the performance and requires therefore additional tuning. With pre-defined tuning procedures, this action can be performed rather quickly.
ON-L1NE/OFF-UNE PERFORMANCE The program has been in routine use for almost one year. It's results are applied in the plant once or twice a month, depending on price and production changes. The areas of program applications are: find optimal heater operating setpoints for the actual production goals check for erroneous measurements in off-line mode the program is used for analyzing different feed types or feed distributions, developing sensitivity parameters for operation parameters
As objectivefunctionsareavailable: the profit,maximum ethylene, maximum propylene or maximum olefin. Any other objective function e.g. minimum C. production can easily be implemented. The program is commonly in useas an optimizer. The steps reqolred to get to the optimum setpoints are depicted in Figure 3. The first step Is the simulation of the actual plant situation by using the minimum inputof approxlrnately 100values nextto the standard parameters of the models. After 10to 15 iterations, the program finds a converged solution. In the next step, the program is fed with all available plant data (approximately 450) which are only checked on validity. After about 15 iterations. the program finds the minimum error for all the measurements, which makes up the reconciled solution. The reconciled simulation result is the base case for the optimization. The simulator-reconciliation sequence is applied to reduce computation time, as a simulation iteration takesabout half the time of a reconciliation iteration. It Is particularly important in the case of significant changes in operating conditions. Then, also, it Improves the convergence properties of the calculation. Withthe userdefined constraints for feed, product and operating parameters, the optimization starts and will be commonly converged after about 30 iterations. The optimized setpoints are:
European Symposium on Computer Aided Process Engineering-I
S219
the heater throughput mixing ratios of feedstocks steam to oil ratio severity of cracking the raw gas compressor suction pressure The refinery operation is fixed to the base case conditions. The program is running on a OG-AVIION4200 Unix system. It can be used in batch or interactive mode. The total computation time is 60 to 70 minutes. The optimization results are summarized in a setpoint report and manually implemented by the plant operators on the TOC 2000 system.
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Fig. 3: On-Line Optimization Procedure The plant model has to be implemented in a working environment that includes e.g. a data base management system, a user interface and a plant data pre-processor. This will allow the use by people other than the system engineer and enhance the maintenance of the plant model.
CONCLUSIONS For on-line optimization, a plant model has to match the plant with sufficient accuracy. The current plant model uses rigorous models, regressions derived from rigorous models, and empirical models based upon plant data. After tuning these models, plant measurements could be reconciled with a 1 to 3% overall accuracy . For on-line application, the simulator has to meet a set of performance requirements, in particular with respect to speed, robustness and flexibility. These requirements could be met by using TISFLO~\ a state of the art equation oriented flowsheeting package. The program is currently running on a OG-AVIION 4200 computer. The total computation time to obtain optimized setpoints is 60 to 70 minutes. The program has run over a year on site without any major convergence problems and without significant Involvement of the contractor.
5220
European Symposium on Computer Aided Process Engineering-l
Use of the simulator has provided a better insight in the plantand has revealed systematic deviation on measurements. TheImprovements on profit registered in the pastarebetween 1-3% depending on price structure and state of experience with the situation.
REFERENCES Asbjornsen, OA (1989). Control & Operability of Process Plants. Comput. chem. Engng. 13, 4/5, pp.351-364 Barendregt, S. and H.M. Woerde, P.J.M. Boeren, C. Meottl (1987). The Application of Equation Based Rowsheetlng in Advanced Plant Management Systems for Olefin Plants. In: Proceedings CEF '87, Taormina, Sicily, Italy Bllberry, RD. and DA Eastman (1990). Experiences with On-Une Data Reconciliation and Optimization. ISA Int. Cant., New Orleans, USA Cutler, C.R and RT. Perry (1983). Real Time Optimization with Multivariable Control is Required to Maximize Profits. Comput. chem. Engng., 7, Vol. 5, pp. 663-667 Davenport, S.L and F.C. Fatora, D.N. Kelly (1991). Implementation of a Closed Loop Real-Time Optimization System on a Large Scale Ethylene Plant. ISA Int. Cont., Oct. DeHaan, S. and J. Derbyshire, M. Kutten (1986). An Optimization Package For Oletins Plants. AIChE Spring National Meeting, New Orleans, Louisiana Foster, D. and J.S. Anderson (1987). In: Proceedings Ot The IMechE, Vol. 201, A-3 Gibbons, C. and C. Kania (1990). On-Une Optimization for an Ethylene Plant. ISA Int. Cont., pp. 891-920 Lauks, U.E. and R.J. Vasbinder (1991). On-Une.Optimization of an Ethylene Plant. In: Proceedings ot the KTI Symposium, Scheveningen, The Netherlands Lawrence, P.J. (1990). Data Reconciliation: The Art of Converting Data to Information. A1ChE Spring National Meeting, Orlando, Rorlda Leeuw den Bouter, J.A. de, and A.G. Swenker, M.G.G. Vanmeulenbrouk, S. Barendregt (1982). Overall Oletins Plant Optimization Combines Models. Oil & Gas Journal, Sept, pp. 84-94 . Paardekooper, PA and C. van Leeuwen, H. Koppelaar, A.G. Montfoort (1990). Computer Applications in Chemical Engineering. Elsevier Science Publishers a.v., Amsterdam. pp. 153-158 Pyrotechnical News (1986). First quarter. Ramsey, J.R and P.B. Truesdale (1990). Oil & Gas Journal, March, pp. 40-44 Saha, LE. and A.J. Chontos, D.R. Hatch (1990). Optimization at Wyoming Gas Plant Improves Quality. Oil & Gas Journal, May.
U.E. Iauks", R.J. Vasbinder", P.J. Valkenburg2) and C. van Leeuwen2)
2)
1) OMV Deutschland GmbH, Burghausen, Germany KTI B.V. PYROTEC Division, Zoetermeer, The Netherlands
ABSTRACT
A simulation model has been developed for the petrochemical complex of OMV Deutschland GmbH, Burghausen (Germany) as part of the Advanced Plant Management System (APMS&). The scope of the work was to make a model that matched plant data with sufficient accuracy to benefit from reconciled plant data and optimized setpoints. This paper describes the plant model, the development phase, the tuning of the model and the on-line and off-line performance. Themodel of the petrochemical complexconsists of the refinery unit, the ethylene plantand downstream treatment units including their interactions. The emphasis in modelling was put on the ethylene plant, composed of ten furnaces with five different geometries and a complexfeedstock system. The furnaces are modelled rigorously with the use of the programs SPYRO&, FIREBOX&, CONVEC and TES&. Models related to other units, e.g. coker, HDS and Pyrotol are based upon empirical relations. The equation oriented flowsheetlnq program TISFLO& is used to ensure fast solutions of the large and complex problem. In total, the plant model consists of more than 5000 linearand non-linear equations. Final model tuning was done on-site in periods of stable plant operation. The predictive power of the model has been confirmed by the reconciliation of over 450 plant data. In optimization a total of 106 constraints and 37 decision variables are present. The results of optimization showthat it is beneficial to modify prevailing operating conditions. The improvements on profit registered in the past are between 1 and 3%.
INTRODUCTION
Computers are nowadays widely used in the chemical industry. When they were first introduced in the 70's it was mainlyin the commercial area and administration. On the other hand, also in the 70'sand 80's a big step forward was done in the field instrumentation and process control area with the introduction of PLC's, DCS and process computers. There have been large improvements on the applied computer technology on both sides. Since the second half of the 80's the gap between these two levels of computerapplications is filled by plant management systems. Already in 1986, KTI's subsidiary, PYROTEC, introduced its concept for such a system (Pyrotechnical News, 1986) APMS. (Advanced Plant Management System). This system is based on plant models and resulted from a long time cooperation with DSM (de Leeuw den Bouter et al., 1982). Recently, the literature reveals a growing interest in such systems, which will be summarized below. An extensive system description has been given (Asbjornsen, 1989). In this articlemaximum profit overa timewindow is mentioned as one of the objectives. In view of this, scheduling operations (Paardekooper et al., 1990) over this time window and optimizing dailyoperations (Barendregtet. al., 1987) arenecessary features. Theoptimization function should supply the operational staff with the required setpoints, according to production demands set forth by the schedule, within plant constraints. 5213
S214
European Symposium on Computer Aided Process Engineering-I
Also data filtering and data reconciliation is recognized as a major task in process information systems (Asbjomsen, 1989).
A consistent material balance as well as trustworthy, hence meaningful, temperatures and pressures are of considerable importance for properplantoperation. Simultaneously, data reconciliation validates the model used for optimization. In this paper, we describe a model for an ethylene plant, developed for above mentioned purposes. As it is important that the model matches the plant accurately, the tuning aspect is addressed. Also experience withon andoff-line useis presented. Butfirstwedigress anddiscuss some recently published achievements.
RECENT APPLICATIONS Systems for on-line optimization have been developed and used overa considerable period of time, but success or failure has largely gone unnoticed. Recently, a series of achievements for large chemical plants have been published. Examples are given In Table 1, which is meant to illustrate that on-line optimization can be profitable. Moreover, the authors seem to agree upon profitability; generally about 3% or 4 M$jyear. Table 1: Examples of the application and benefits of on-line optimization
'::.:::,.11:;:::: :::;::.o.l'li~11: :!!~~::1::::::::~1..:::::::::::::::::::::::::::, :::::::::::::·li9fn;::::•. : 1983 Shell Oil Ethylene Plant 3 - 5% 1986 1988 1990 1990 1990 1990 1991 1991
Ethylene Plant Power Station Refinery GasPlant Crude Unit Ethylene Plant Ethylene Plant Ethylene Plant
Wilton Texaco Amaco Painter Star Enterprise Chevron USA OMV Deutschland Lyondell
4 M$jyr 2 6% 4 M$jyr 4 M$jyr 3 M$jyr 5 . 10% 1 3% 9 mnth
-
Generally, intangible profit in terms of a better understanding of piant behaviour is indicated and acceptance by plantpersonnel is mentioned as a keycondition for successful introduction of the system. The system consists generally of a simulator and a (series of) shell program(s) around it. This shell is rather important as it determines the ease of useand the functionality of the system. The simulator - the heart of the system - mayverywell bea blackbox to the user. Its basicrequirements are accuracy, flexibility, robustness and speed. Hence, for global optimization of large plants, mostly equation solvers have been applied (Cutler et al., 1983, Foster et al., 1987, Saha et al., 1990, Lauks et al., 1991, Davenport et al., 1991). But also the sequential modular method has been used (to solve tear streams) with some optimization algorithm on top (DeHaan et al., 1986, Gibbons et al., 1990). A combination of both methods has been applied (Bi/berry et et., 1990) as well. Also distributed optimization (Ramsey et al., 1990) has been used. Several approaches have been reported in literature to reconcile plant data: from mass balance only to complete model based reconciliation. Also plant section wise reconciliation (Lawrence P.J., 1990)as opposed to global reconciliation has been described. Obviously, global model based reconciliation (as applied here) is the most complex and CPU demanding problem. Its solution, however, is a set of mutually consistent plantdata, adjusted according to technological know-how, as reflected in the models. From an engineering pointof viewthis is the bestonecando. Commercially, the efforts of model building and the contribution to benefits need to be balanced. As solution methods and hardware cost involved clearly pose no problem anymore in this field, the emphasis may be on modelling technology in the future. The key issue will be the suppliers ability to provide an adequate model, I.e. onethat matches the plantover its full operating range. In our opinion, this does not necessarily imply the use of a rigorous or design model for all pieces of equipment.
S215
European Symposium on Computer Aided Process Engineering-l
FACTORY STRUCTURE AND COMPUTER SYSTEM The production facilities of the OMV Deutschland GmbH in Burghausen consists of a refinery part with an atmospheric crude distillation, a coker, a hydro desulphurlzation unit and a petrochemical part with gas treatment, steam crackingfurnaces, quenching, compression andseparation anda gasoline treatment unit with a Pyrotol unit. The refinery is supplied with mainlyUbyancrude. The products of the complex are ethylene, propylene, C4 cut, benzene, Cg cut, jet, diesel, house heating fuel, coke and sulphur. An overview of the plant units Is depicted in Figure 1 which also shows the interconnections.
HYDOODE-
SULPHUR IZATICI'-I
Ethylene Q"ucIe
Naphtha, Gas 011
ATM.
Propylene
CRUDE
C4-eut
DISTIL.
Benzene C9-cut CS -cut
!
~':
Dieselfuel
Coke
~ SYSTEM
•
~ SYSTEM
Fig. 1: Production Facilities of 6MV Deutschland Process control is executed via the TDC 2000 system. All process supervisory controlsare located in the Honeywell 45000 (PMX) computer. It interfaces with the administrational computer hardware for the transfer of process and laboratory data. Thecomputers on the plantmanagement level are connected via Ethernet, serving as process Information system and calculation platform for commercial andadminlstrational purposes. Intothis existing computer grid, an APMS. simulation platform hadto be integrated, making mostuseof the existing facilities. Figure 2 Illustrates the hierarchical structure of the computer system.
THE PLANT MODEL The plant model had to account for the plant wide cross connections of feed and utilities. Recycle streams from up and down stream units havean influence on the amount of fresh feed consumption of the ethylene plant. Hence for optimization the modelling of all refinery and petrochemical units was required. The heart of the plant are the steam crackers with a high impact on the plant's profit. In 10 heaters with 5 different designs, 11 feedstocks have to be handled, which makes up a total of 37 different combinations. For eachheater/feed combination a detailed model for mass flow and energy balance was developed.
CACE 16 Suppl.-P
European Symposium on Computer Aided Process Engineering-l
S216
The convection sections were modelled rigorously with the program CONVEC to supplya detailed mass and energy flow across each of the heat exchanger banks. Each bank was set up as a heat exchanger with a fouling factor as tuning parameter.
COFPOAATE
LP(PMS)
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I
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I
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I I
5TAATEGIC &
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Theperformances of the fireboxes werecalculated rigorously with the program FIREBOXe. This program uses the excess air, the heating value of the fuel, the cokethickness in the coil and the radiant duty from the SPYROf> program to evaluate the maximum tube skin temperature and radiant box efficiency. Fig. 2: System Set-Up and Functionality The SPYRO. program has been used to rigorously calculate the heater effluent composition, coking, absorbed heat duty and the coil outlet temperature for the entire heater operating ranges for the 37 heater/feed combinations. The pressure drop, the outlet temperature and the produced steam of the transfer line exchangers (TLE) were simulated by the program TEse. All rigorously calculated results of the heater sections were regressed by polynomial regression in order to save model run time. The regression data made up the basis for the heater model. During comparisons in the tuning phase it turned out thatthe loss in calculation accuracy by regressing the data is minor. The plant model takesthe different modes of operation (on-line, decoke, stand-by, off-line) into account. The hot section and the raw gas compression were modelled in a simplified manner. The requirements to these models were to estimate the amounts of pyrolysis tar and gasoline, to calculate the produced steam in the hot section and the consumed high pressure steam of the compressor turbine. The cold section consists of models for the hydrogenation reactors. Theamounts of productand recycle streams are calculated according to on-line analysis and product specs. Thegasoline treatment, the Pyrotol and refinery unitswere modelled asmultivariable splitters and reactors based on experimental data. All these models were provided with a number of tuning possibilities to accountfor e.g. the catalyst activity, mode of operation and feed quality changes. Theapproach of modelling the heaters In detailwhilethe restof the plantwas modelled fairly rough, was chosen to achieve accuracy in the area where highest Impact on profitwas expected. The up and down stream units were Intended to supply Information for the material balance and plant constraints which might become relevant for the operation of the heaters. Thisstrategy hasproved itself since it keeps the
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model simple and leaves the possibility to substitute a rough approach by a detailed one if it turns out to be necessary. The APMS. simulator has an open model structure. In a set of FORTRAN subroutines, unit operations or parts of unit operations are represented. In standard routines functions like splitters and mixers or interfaces to regression dataarerepresented. In so called application routines functionalities are included, representing empirical models, specific calculations as for Instance the outlet velocity of the radiant coil or sometailor made calculations. Functions maybe linearor non-linear. A consistent solution for all the routines is generated by the equation solver (TISFLQe). Table 2 gives some characteristic figures about the application, describing the size and complexity of the application.
DEVELOPMENT PHASES In the first phase of the project, the plantmodel topologywas developed by drawing a kind of f1owsheet, defining all nodes, stream connections and stream components. After this, a detailed definition of the functionalities represented by a node, were settled. This activity was done in very close cooperation between KTI and OMV Deutschland. Table 2: Plant Model Characteristics Nodes
314
Application routines
71
Streams
777
Standard routines
26
Components
84
Model calls
1063
Decision variables
37
Total equations
5383
Optimization constraints
106
Non-linear equations
:!:1000
Objectivefunctions
4
Derivatives (modelsonly)
12303
In the second phase, the rigorous simulations of the heater sections were executed and the regression coefficients generated. The FORTRAN routines, specific to the OMV application, had to be written and the topology, as defined In the first phase, had to be built up in the strict and specific TISFLO. terminology. Plant data were used to cross check the requested functionality in a factory test. After 3 months of factory development on VAX/VMS and HPjUNIX a basicversion of the simulator was Installed on OMV's Data General AOSfVS for integration in OMV's environment. The complete version was installed 9 months later. In the tuning and testing phase on site, which took about half a year, tuning factors were adjusted and missing topology connections were identified and Implemented. The accuracy of the model was demonstrated to operations by a plant/model test. The heater run length prediction was checked and tuned in long term tests over several decoking periods. The project was closed by the end of 1990. During the development time, two engineers from the contractor and one from the plant were involved. In 1991, the model has been extended to include two new steam crackers. Also new modelling technology has been implemented.
TUNING Thegeneral purpose of thetuningactivityIsto getthe mostaccurate match between plantmeasurements and model predictions. TheSPYROGP program had been In useby OMV Deutschland for a period of over 10years. There wasa clearunderstanding of the qualityof the program. Theresults of this andthe other rigorous programs integrated Into the static OMV application hadto match the wide operating ranges of the production plant. On the other hand, the qualityof tuningIs verymuchdepending on the knowledge aboutthe accuracyof plantmeasurements. Consequently, a compromise Is necessary to keepthe effort Justifiable. Mainly due to the semi-continuous operation of the coker, a reasonable match of the plant material balance was only possible by averaging plant data of a period of 24 hours. The cracking furnaces are
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European Symposium on Computer Aided Process Engineering-l
controlled and operated by the ratio of propylene to ethylene or the coil outlet temperature. A full heater effluent analysis was not available. The check of the model accuracy was therefore not based on Individual heaters ratherthanon theoverall plantmaterial balance. Duringthe elaborate processof tuning, there was an effect of learning and understanding the true behaviour of the process. After smoothing and correctingplantdata and with the tuned model, a match of the material balance was achieved as close as 1-3% from previously 3·5%. This match can be kept with the once selected tuning factors as long as there is no significant change in the operation procedure, feedstock quality or the process equipment. Hence tuning factors are not continuously updated. The total number of tuning parameters for the application is about 300. Maintenance of the model is imperative. Theeffort spentwas about half a day per week. Below we will discuss two problems associated with tuning: The hydrogen system was originally modelled without taking the recycle streams into account. This system is rathercomplicated, consisting of 5 hydrogen consumers plus methanation. It usesthe hydrogen from the ethylene plantand a 100% hydrogen from batterylimits. Reconciliation of all plantdata, including datafor the hydrogensystem led to heavydisturbances evenIn the heaterarea. A satisfactory result was gained with the introduction of a recycle factor and adjustments of the hydrogen concentrations with lab analysis. Later in applying the program in routine operation, a problem in the backend acetylene convertorwas discovered. A check of the system indicated a shift of the base line of the inlet acetylene analyzer and as a consequence the hydrogen ratio controller received a wrong setpoint. A very time consuming and long term process was the tuning of the heater run length. The model is equipped with a tuning possibility for base and slope of the radiant and TlE fouling. These parameters were pre-adjusted in a way that the model gave a standard run length for a heater/feed combination. Basis for the run length prediction is the coking rateand the coke precursoras calculated by the SPYRO~ program. For properly operated heaters, the model prediction is satisfactory. Any mal-operation of the heater, badfiring profileor bad feedswitch maycorrupt the performance and requires therefore additional tuning. With pre-defined tuning procedures, this action can be performed rather quickly.
ON-L1NE/OFF-UNE PERFORMANCE The program has been in routine use for almost one year. It's results are applied in the plant once or twice a month, depending on price and production changes. The areas of program applications are: find optimal heater operating setpoints for the actual production goals check for erroneous measurements in off-line mode the program is used for analyzing different feed types or feed distributions, developing sensitivity parameters for operation parameters
As objectivefunctionsareavailable: the profit,maximum ethylene, maximum propylene or maximum olefin. Any other objective function e.g. minimum C. production can easily be implemented. The program is commonly in useas an optimizer. The steps reqolred to get to the optimum setpoints are depicted in Figure 3. The first step Is the simulation of the actual plant situation by using the minimum inputof approxlrnately 100values nextto the standard parameters of the models. After 10to 15 iterations, the program finds a converged solution. In the next step, the program is fed with all available plant data (approximately 450) which are only checked on validity. After about 15 iterations. the program finds the minimum error for all the measurements, which makes up the reconciled solution. The reconciled simulation result is the base case for the optimization. The simulator-reconciliation sequence is applied to reduce computation time, as a simulation iteration takesabout half the time of a reconciliation iteration. It Is particularly important in the case of significant changes in operating conditions. Then, also, it Improves the convergence properties of the calculation. Withthe userdefined constraints for feed, product and operating parameters, the optimization starts and will be commonly converged after about 30 iterations. The optimized setpoints are:
European Symposium on Computer Aided Process Engineering-I
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the heater throughput mixing ratios of feedstocks steam to oil ratio severity of cracking the raw gas compressor suction pressure The refinery operation is fixed to the base case conditions. The program is running on a OG-AVIION4200 Unix system. It can be used in batch or interactive mode. The total computation time is 60 to 70 minutes. The optimization results are summarized in a setpoint report and manually implemented by the plant operators on the TOC 2000 system.
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Fig. 3: On-Line Optimization Procedure The plant model has to be implemented in a working environment that includes e.g. a data base management system, a user interface and a plant data pre-processor. This will allow the use by people other than the system engineer and enhance the maintenance of the plant model.
CONCLUSIONS For on-line optimization, a plant model has to match the plant with sufficient accuracy. The current plant model uses rigorous models, regressions derived from rigorous models, and empirical models based upon plant data. After tuning these models, plant measurements could be reconciled with a 1 to 3% overall accuracy . For on-line application, the simulator has to meet a set of performance requirements, in particular with respect to speed, robustness and flexibility. These requirements could be met by using TISFLO~\ a state of the art equation oriented flowsheeting package. The program is currently running on a OG-AVIION 4200 computer. The total computation time to obtain optimized setpoints is 60 to 70 minutes. The program has run over a year on site without any major convergence problems and without significant Involvement of the contractor.
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Use of the simulator has provided a better insight in the plantand has revealed systematic deviation on measurements. TheImprovements on profit registered in the pastarebetween 1-3% depending on price structure and state of experience with the situation.
REFERENCES Asbjornsen, OA (1989). Control & Operability of Process Plants. Comput. chem. Engng. 13, 4/5, pp.351-364 Barendregt, S. and H.M. Woerde, P.J.M. Boeren, C. Meottl (1987). The Application of Equation Based Rowsheetlng in Advanced Plant Management Systems for Olefin Plants. In: Proceedings CEF '87, Taormina, Sicily, Italy Bllberry, RD. and DA Eastman (1990). Experiences with On-Une Data Reconciliation and Optimization. ISA Int. Cant., New Orleans, USA Cutler, C.R and RT. Perry (1983). Real Time Optimization with Multivariable Control is Required to Maximize Profits. Comput. chem. Engng., 7, Vol. 5, pp. 663-667 Davenport, S.L and F.C. Fatora, D.N. Kelly (1991). Implementation of a Closed Loop Real-Time Optimization System on a Large Scale Ethylene Plant. ISA Int. Cont., Oct. DeHaan, S. and J. Derbyshire, M. Kutten (1986). An Optimization Package For Oletins Plants. AIChE Spring National Meeting, New Orleans, Louisiana Foster, D. and J.S. Anderson (1987). In: Proceedings Ot The IMechE, Vol. 201, A-3 Gibbons, C. and C. Kania (1990). On-Une Optimization for an Ethylene Plant. ISA Int. Cont., pp. 891-920 Lauks, U.E. and R.J. Vasbinder (1991). On-Une.Optimization of an Ethylene Plant. In: Proceedings ot the KTI Symposium, Scheveningen, The Netherlands Lawrence, P.J. (1990). Data Reconciliation: The Art of Converting Data to Information. A1ChE Spring National Meeting, Orlando, Rorlda Leeuw den Bouter, J.A. de, and A.G. Swenker, M.G.G. Vanmeulenbrouk, S. Barendregt (1982). Overall Oletins Plant Optimization Combines Models. Oil & Gas Journal, Sept, pp. 84-94 . Paardekooper, PA and C. van Leeuwen, H. Koppelaar, A.G. Montfoort (1990). Computer Applications in Chemical Engineering. Elsevier Science Publishers a.v., Amsterdam. pp. 153-158 Pyrotechnical News (1986). First quarter. Ramsey, J.R and P.B. Truesdale (1990). Oil & Gas Journal, March, pp. 40-44 Saha, LE. and A.J. Chontos, D.R. Hatch (1990). Optimization at Wyoming Gas Plant Improves Quality. Oil & Gas Journal, May.