Modular Integrated Framework for Process Synthesis and Optimization Based on Sequential Process Simulator

Transcription

1 A publcaton of VOL. 35, 2013 CHEMICAL ENGINEERING TRANSACTIONS Guest Edtors: Petar Varbanov, Jří Klemeš, Panos Seferls, Athanasos I. Papadopoulos, Spyros Voutetaks Copyrght 2013, AIDIC Servz S.r.l., ISBN ; ISSN The Italan Assocaton of Chemcal Engneerng DOI: /CET Modular Integrated Framework for Process Synthess and Optmzaton Based on Sequental Process Smulator Donglang Wang, Xao Feng*, Chun Deng College of Chemcal Engneerng, Chna Unversty of Petroleum, Bejng, Chna, A novel modular ntegrated framework s developed n ths work to perform process synthess and optmzaton based on a sequental process smulator. The modular ntegrated framework conssts of three platforms: smulaton, parameter and optmzaton platforms. Ths framework ntegrates process smulator, nterface program, optmzaton algorthm to form a mult-platform envronment ntegrated system. In the framework, the alternatve processes models are frstly establshed through a process smulator (Aspen Plus).After that, parameter extracton and processng are mplemented by Excel wth the nterface (Aspen Smulaton Workbook). And then the MINLP problem of process synthess s solved by the multdscplnary ntegraton software (Isght). The rgorous models of chemcal processes or unts, regarded as Black box, are solved by the process smulator, rather than shortcut or aggregated models n the form of equatons. Therefore, not only s the process synthess problem smplfed but also does mprove the accuracy of the soluton. Several examples are provded to llustrate the effectveness of the proposed method. 1. Introducton Process synthess has a sgnfcant mpact on development, desgn and operaton of petro-chemcal processes. It can be consdered as the cornerstone of a process desgn actvty. Conventonally, the synthess problem can be descrbed as follows: gven a set of feedstocks and a set of desred fnal products wth specfcatons, the topology (unt selecton and nterconnecton) and operatng parameters (temperature, flow, pressure, etc.) are optmzed to acheve optmal objectves (to maxmum yeld, energy effcency, proft, etc). Generalzed dsjunctve programmng (GDP) and mxed nteger nonlnear programmng (MINLP) have been proved to be powerful tools n the synthess and desgn of subsystems such as heat exchanger networks, mass exchanger networks, dstllaton sequencng, and utlty systems. However, n order to avod complex numercal solvng problems, t s common to use shortcut or aggregated models rather than rgorous models n large scale chemcal producton process. The shortcut or aggregated models reduces the precson of the process models, and hence may predct unrelable results. On the other hand, chemcal process smulators (such as Aspen Plus, Hysys, Pro II, etc.) have become relable tools that are wdely used n process engneerng (Lam et al., 2010). Process smulators contan strct models for most chemcal process unts, talored numercal algorthms for partcular unts, and large databases of physcochemcal thermodynamc and transport propertes. The capablty of process smulators can make modellng and optmzaton of the process more easly. Although most of process smulator contans some optmzaton tools, the optmzaton capablty s lmted to the fxed topology structure, and cannot be drectly appled n the process synthess problems. Therefore, t s desred to construct process synthess capablty based on a process smulator. 2. Process synthess based on process smulators Recently, process synthess based on process smulators has attracted ncreasng attentons. Accordng to the types of modular envronment, the research can be dvded nto two felds: equaton-orented and sequental modular envronment. Kravanja and Grossmann (1990) frst mplemented a modellng and decomposton strategy n equaton-orented smulator PROSYN (the successor s called MpSyn), and developed an automated topology and parameter process syntheszer. Kravanja (2010) revewed the capabltes of MpSyn and the challenges n sustanable ntegrated process synthess, and ponted out that the future research would Please cte ths artcle as: Wang D., Feng X., Deng C., 2013, Modular ntegrated framework for process synthess and optmzaton based on a sequental process smulator, Chemcal Engneerng Transactons, 35, DOI: /CET

2 be orented towards the development of an even more advanced and robust syntheszer that can be appled n large-scale sustanable applcatons n dfferent engneerng domans. The sequental modular smulators are more desrable modular ntegrated tools for process synthess and optmzaton because of the more extensve applcatons n complex chemcal processes. Ths paper ams at the process synthess capablty based on sequental modular smulators. Dwekar et al (1992) mplemented a varant of the generalzed Benders decomposton (GBD) and the outer approxmaton (OA) algorthm n the ASPEN smulator, and proposed a two-level method that conssted of NLP subproblem (wth all 0-1 varables fxed) and an MILP master problem. Several examples were presented ncludng the synthess of the hydrodealkylaton (HDA) process and ntegrated gasfcaton combned cycle (IGCC) system. Reneaume et al. (1995) presented a three-level soluton strategy ncludng superstructure level, structure level and module level. In ther work, the modular smulator ProSm had been chosen snce gradent nformaton was easly accessble. The smlarty between Dwekar and Reneaume methods s that the topologcal bnary varables (y) are represented usng FSPLIT blocks by varyng the flow fracton between 0 and 1. So ther methods cannot be used to handle the stuaton wth more than three alternatves. Gross and Roosen (1998) put forward an optmzaton method that ntegrated evolutonary algorthms wth Aspen Plus, and presented the applcatons n synthess of separaton sequences and overall process synthess wth lmted degrees of freedom. Although the optmzaton was tme consumng, the easy formulaton of the optmzaton task saved preparaton tme owng to the use of the relable process smulator. Leborero and Acevedo (2004) provded an optmzaton framework for the synthess and desgn of complex dstllaton sequences, n whch a modfed genetc algorthm (GA) were coupled wth Aspen plus to obtan the combned capablty of solutons wth complex non-convex mathematcal problems and the formulaton of rgorous models. The method was appled to the synthess and desgn of extractve dstllaton systems and the optmzaton of a superstructure nvolvng several varatons of heat-ntegrated columns. Caballero et al. (2005) presented a superstructure-based optmzaton method that combned the capabltes of commercal process smulator Hysys and generalzed dsjunctve programmng (GDP). The operatonal condtons (reflux and rebol ratos, recoveres, etc.) and structural parameters (number of trays, locaton of feed and product streams, etc.) were smultaneously optmzed for the rgorous desgn of dstllaton. The same method was also used to acheve the flowsheet optmzaton for the selecton of dfferent equpments whch were gven n a set of alternatves wth complex cost and sze functons (Caballero et al., 2007). All aforementoned researches amed at sngle objectve synthess problem and had to crcumvent the mplct constrant problem (black-box) n process synthess based on process smulators. To meet the requrement of the sustanable system, mult-objectve process synthess has also drew a lot of attentons. Xu and Dwekar (2005) proposed the mult-objectve process synthess problem for the envronmentally frendly solvent and the separaton of n-process solvents, and the mult-objectve optmzaton framework was composed of smulated annealng algorthm, process smulator Aspen plus and external FORTRAN routne. The better and more Pareto solutons than the conventonal heurstc scheme were obtaned for the CH 3COOH-H 2O azeotrope system. Yue et al. (2009) proposed a three-level strategy of mult-objectve process synthess based on modular smulator that ntegrated the process smulator Aspen Plus, mult-objectve genetc algorthm (NSGA II) and hybrd coordnaton strategy. The abovementoned process synthess methods based on modular envronment are approprate for the specfc smulaton system and algorthm. Due to the complexty and dversty of chemcal process, applcaton felds wll be subject to certan restrctons. So t s necessary to propose such a general modular ntegrated and optmzaton approach. 3. Modular ntegrated framework for process synthess and optmzaton A novel modular ntegrated framework s developed n ths work to perform process synthess and optmzaton based on a sequental process smulator. The modular ntegrated framework ntegrates process smulator (Aspen Plus n ths paper), nterface program, optmzaton algorthms to form a mult-platform envronment ntegrated system ncludng smulaton, parameter and optmzaton sub-platforms (as shown n Fgure 1). In the framework, the alternatves (feedstocks, processes and products) are smulated as an Aspen smulaton superstructure model. The extracton and processng of necessary data (decson, constrant, objectve varables, etc.) s then mplemented by Excel wth the nterface (Aspen Smulaton Workbook). Fnally the MINLP problem of process synthess s solved by multdscplnary ntegraton software (Isght). The rgorous models of chemcal processes or unts, regarded as Black box, are solved by the process smulator, rather than shortcut or aggregated models n the form of equatons. Therefore, not only s the process synthess problem smplfed but also does mprove the accuracy of the soluton. (1) Smulaton Platform Aspen Plus and Hysys have been wdely appled n ndustral and academc process smulaton and desgn for cogeneraton plants, Petroleum refnng, bomass gasfcaton system, etc. The key equpments such as heat 50

3 exchangers and dstllaton columns can be rgorously szed or rated wthn the smulaton envronment (Lam et al., 2010). Aspen Plus s chosen to llustrate the smulaton platform n ths study. Smulatons and models can be developed by varous researchers usng dfferent software packages such as Aspen Plus and Aspen Custom Modeler. The alternatves dentfed are used to construct a superstructure smulaton model n the same fle, or the superstructure approaches to process desgn can also be mplemented n the optmzaton platform usng the logcal workflow blocks whch allow for error checkng and process confguraton changes. In the former method, Fsplt and Mxer blocks n smulaton platform are treated as logcal nodes n MINLP process synthess but not topologcal bnary varables. The topologcal bnary varables are defned n the followng parameter platform so that more than three alternatves can be handled n our method. Ths s dfferent from the work of Dwekar and Reneaume. Optmzaton platform Optmzaton Algorthm Scheduler Alternatve algorthms Input Smulaton Workflow Output Parameter platform Excel Aspen Smulaton Workbook Interested varables: Decson, constrant, objectve varables and others Smulaton platform Aspen smulaton superstructure model Alternatve feedstocks Alternatve processes Alternatve products Fgure 1: Modular ntegrated framework for process synthess and optmzaton (2) Parameter Platform The key for the proposed modular ntegrated framework for process synthess and optmzaton s the data exchange between modular smulaton envronment and optmzaton algorthms. The specfed and calculated varables extracted from the smulaton platform together wth user-defned bnary varables are transformed to the decson varables, constrant varables and objectve varables n MINLP problem. Excel s used as the data processng platform n ths study. There are several ways for data exchange between Excel and Aspen Plus. One of them s to use VBA (Vsual Basc for Applcaton) programmng language. A standard smulaton nterface (snter) s developed by the U.S. department of energy n conjuncton wth Excel and Aspen Plus. Another way s to use ASW tool for nterfacng AspenTech s process smulaton models wth Excel worksheets. In terms of compatblty and applcablty, t s a better way to exchange data between smulaton platform and parameter platform. In accordance wth the prncple of generalty, ASW s more approprate for modular ntegrated optmzaton framework because programmng s not requred. The bnary varables are defned n parameter platform, and slack varables (SK), multple choce varables (MS) and condtonal selecton varables (CS) are also defned so that bnary varables are assocated wth contnuous varables. Takng flow as an example, the defnton formulas Eqs.(1)-(3) are formulated as: SK F F y (1) max MS y (2) CS y y (3) y 01,, 01,,..., n k (3) Optmzaton Platform The ranges of varables are specfed n optmzaton platform. It s necessary to ntegrate varous optmzaton algorthms n optmzaton platform for solvng dfferent process synthess. The multdscplnary desgn optmzaton software Isght s used as optmzaton platform. Isght has bult-n Excel standard nterface to transfer nformaton between optmzaton platform and parameter platform, and t also provdes a seres of optmzaton algorthms for dfferent stuatons. Sngle and mult-objectve algorthms for dscrete and contnuous varables ncludng genetc algorthms, smulated annealng and partcle swarms as well as a 51

4 number of smpler algorthms are all ncluded n Isght. So the proposed modular ntegrated framework for process synthess and optmzaton can be appled to sngle objectve process synthess as well as mult-objectve process synthess. 4. Case Study 4.1 Selecton optmzaton of the bolers The proposed modular ntegrated framework s appled to the selecton of boler operaton. The steam utlty system s composed of three bolers, each of whch has featured effcency, fuel cost and overhead cost. The objectve s to determne the most economcal use of each boler for the gven steam demand (14,400 t/h). Ths case ncludes the optmal confguraton and operaton condton of bolers and s typcal MINLP problem. A superstructure smulaton model s bult n Aspen Plus smulaton platform (as show n Fgure 2). The bolers are modelled as smple heaters, and STEAMNBS s chosen as property method. Specfy a pressure drop of 0 kpa for each boler. All necessary data for the case s gven n Table 1. Feed B1IN B2IN B3IN Boler1 Boler2 Boler3 B1OUT B2OUT B3OUT Demand Fgure 2: ASPEN PLUS superstructure representaton for steam utlty system Table 1: Featured data for the bolers Boler1 Boler2 Boler3 Eff Max (%) 85 % 87 % 90 % Flow Max (kg/s) Flow Max-5% (kg/s) Fuel ($/MJ) OverHead ($/h) Inlet Temperature ( C) Outlet Temperature ( C) Pressure (kpa) 4, , ,101.3 The effcency and actual heat flow for each boler are calculated wthn the parameter platform. The effcency of the three bolers are characterzed by the followng relatonshp Eq.(4): F F Max Eff EffMax 5% FMax5% FMax 2 where Eff s the effcency of boler, Eff MAX s the maxmum effcency of boler, F s the nlet mass flowrate of boler, Flow MAX s the nlet mass flowrate of boler at maxmum effcency and Flow MAX-5% s the nlet mass flowrate at an effcency 5 % less than the maxmum effcency. Then, the actual heat duty s calculated from the followng equaton Eq.(5): 100 Qact Q (5) Eff (4) where Q act s the actual heat duty of boler and Q s the heat duty of boler calculated n Aspen Plus. The operatng cost for each boler s the sum of fuel consumpton cost and overhead cost assocated wth the operaton status. When the boler s operatng, t ncurs overhead cost. The total operatng cost (TOC) s defned as the addtve operatng costs of each boler Eq.(6): TOC ( Q F OH y) B act cos t (6) where B s a boler, F cost s the fuel cost and OH s the overhead cost. The parameters extracted from Aspen smulaton model nclude the nlet mass flowrate and heat duty of each boler, whle the slack varables and multple choce varables have been defned n Eqs.(1) and (2). The nlet mass flowrates of Boler1 and Boler2 (F1 and F2) and bnary varables y are chosen as decson varables, then the ranges of varables are specfed n optmzaton platform. The mathematcal formulaton of the MINLP problem can be stated as Eq.(7): 52

5 Mn TOC s.t. 0 F Q act,1 0 Q 9500 act,2 0 Q act,3 6 1e SK 0 1 MS 3 y 0,1 (7) MISQP (Mxed Integer Sequental Quadratc Programmng) algorthm s used to solve aforementoned process synthess problem. The result shows that the mnmum operatng cost s $486,450 when Boler1 s operatng at 8, t/h and Boler3 at 6, t/h. 4.2 Flowsheet optmzaton wth cost and sze functons The proposed ntegrated framework can be also appled to the flowsheet optmzaton wth dscontnuous cost and sze functons. The case adapted by Caballero conssts of a small heat exchanger network, as shown n Fgure 3. Although the topology optmzaton s not addressed n ths case, the cost of each heat exchanger s gven by a dscontnuous cost functon assocated wth the heat transfer area (A). It s stll a typcal MINLP problem. All necessary data for the case s gven n Table 2. SteamOut HotStreamIn ColdS1 ColdStreamOut SteamIn E102 E101 HotS1 ColdStreamIn WaterOut E103 WaterIn HotStreamOut Table 2: Data for Case 2 Stream Composton Flow T n T n Cost (kmol/h) (K) (K) $/kwy Hot DPhenylC Cold Glycerol Coolng water H 2O Steam H 2O Heat exchanger Overall heat transfer coeffcent (W/m 2 K) E101 1,500 E E103 1,000 Fgure 3: Network structure for case 1 The objectve functon ncludes both the nvestment and utlty costs. The mathematcal formulaton of the MINLP problem can be stated as (Eq.(8)): Mn: TAC= IC Cost Q Cost Q 1 2 jhe j steam Steam water water S. T IC 2750A 3000 IC 1500A IC 600A A10 10 A25 25 A50 T T T 10 where HE s short for heat exchanger, IC s the annualzed nvestment cost of each heat exchanger and Q s the heat load suppled/removed by steam or water. T 1 and T 2 s the temperature of HotS1 and ColdS1 (n Fgure 2). There s only one degree of freedom n ths case, and the heat exchange area of E101 (A 1) s selected as decson varable. The areas of E102 and E103 are calculated accordng to Eq.(9): (8) Q A T U LMTD (9) 53

6 All necessary parameters extracted from smulaton platform nclude A 1, the log mean temperature dfference (T LMTD) of E102 and E103, Q steam and Q water. The case s solved by usng the adaptve smulated annealng algorthm (ASA). ASA algorthm greatly accelerates the search and mproves the qualty of the optmal soluton through adjustng the temperature and the Increment. The dscrete relatonshp between the annual total cost and decson varable can be observed n Fgure 4. Table 3 shows a summary of the optmal results. TAC (10 3 $/year) Optmum Pont A 1 Fgure 4: Impact of the heat exchange area A 1 on the annual total cost TAC Table 3: Result for Case 2 Area (m 2 ) Investment Cost ($/y) E ,5347 E ,4458 E ,923 Power (kw) Cost ($/y) Heat utlty ,648 Cold utlty ,732 TAC 16,4107 Temperature (K) T T Concluson In ths paper, a novel modular ntegrated framework s proposed to perform process synthess and optmzaton. In the modular framework, process smulator, nterface program, and optmzaton algorthms are ntegrated to a mult-platform envronment ntegrated system ncludng smulaton, parameter and optmzaton sub-platforms. The superstructure models of chemcal processes or unts are establshed n process smulator, rather than shortcut or aggregated models n form of equatons. The process synthess problems are smplfed and the accuracy of the soluton can also be mproved. Future research wll am at a larger number of academc and ndustral cases such as heat ntegrated complex dstllaton system synthess. References Caballero, J.A., Mlán-Yañez, D., Grossmann, I.E., 2005, Rgorous desgn of dstllaton columns: ntegraton of dsjunctve programmng and process smulators. Industral and Engneerng Chemstry Research, 44, Caballero, J.A., Odjo, A., Grossmann, I.E., 2007, Flowsheet optmzaton wth complex cost and sze functons usng process smulators. AIChE Journal, 53, Dwekar, U.M., Grossmann, I.E., Rubn, E.S., 1992b, An MINLP process syntheszer for a sequental modular smulator. Industral and Engneerng Chemstry Research, 31, Gross, B., Roosen, P., 1998, Total process optmzaton n chemcal engneerng wth evolutonary algorthms. Computers and Chemcal Engneerng, 22, Supplement 1, S229-S236. Kravanja, Z., 2010, Challenges n sustanable ntegrated process synthess and the capabltes of an MINLP process syntheszer MpSyn. Computers and Chemcal Engneerng 34, Kravanja, Z., Grossmann, I.E., 1990, Prosyn an MINLP process syntheszer. Computers & Chemcal Engneerng, 14, Lam, H.L., Klemeš, J.J., Fredler, F., Kravanja, Z., Varbanov, P.S., 2010, Software tools overvew: Process ntegraton, modellng and optmsaton for energy savng and polluton reducton, Chemcal Engneerng Transactons, 21, 2010, DOI: /CET Leborero, J., Acevedo, J., 2004, Processes synthess and desgn of dstllaton sequences usng modular smulators: a genetc algorthm framework. Computers AND Chemcal Engneerng, 28, Reneaume, J.-M.F., Koehret, B.M., Joula, X.L., 1995, Optmal process synthess n a modular smulator envronment: new formulaton of the mxed-nteger nonlnear programmng problem, Industral and Engneerng Chemstry Research, 34, Xu, W., Dwekar, U.M., 2005, Envronmentally Frendly Heterogeneous Azeotropc Dstllaton System Desgn: Integraton of EBS Selecton and IPS Recyclng, Industral & Engneerng Chemstry Research, 44, Yue, J. C., Zhe, S. Q., Han, F. Y., 2009, Strategy of mult-objectve process synthess based on modular smulator, CIESC Joural, 1,