A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

›› 2008, Vol. 16 ›› Issue (1): 39-42.

• SELECTED PAPERS FROM THE 4^th INTERNATIONAL SYMPOSIUM ON DESIGN, OPERATION AND CONTROL OF CHEMICAL PROCESSES • Previous Articles Next Articles

A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

SUN Li¹, Helen H. Lou²

1. Department of Chemical Engineering, Dalian University of Technology, Dalian 116023, China;
2. Department of Chemical Engineering, Lamar University, Beaumont, Texas 77710, USA

Received:2007-05-10 Revised:2007-10-27 Online:2008-02-28 Published:2008-02-28
Supported by:
Dalian University of Technology, the US National Science Foundation (No.CTS-0407494);the Texas Ad-vanced Technology Program (No.003581-0044-2003)

A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

孙力¹, Helen H. Lou²

1. Department of Chemical Engineering, Dalian University of Technology, Dalian 116023, China;
2. Department of Chemical Engineering, Lamar University, Beaumont, Texas 77710, USA

通讯作者: SUN Li, E-mail: bonnia@dlut.edu.cn
基金资助:
Dalian University of Technology, the US National Science Foundation (No.CTS-0407494);the Texas Ad-vanced Technology Program (No.003581-0044-2003)

Abstract

Abstract: In many circumstances, chemical process design can be formulated as a multi-objective optimization(MOO) problem. Examples include bi-objective optimization problems, where the economic objective is maxi-mized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process.Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose ageneric and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a casestudy based on the design of a condensate treatment unit in an ammonia plant.

Key words: multi-objective optimization, uncertainty, chemical process design

摘要： In many circumstances, chemical process design can be formulated as a multi-objective optimization(MOO) problem. Examples include bi-objective optimization problems, where the economic objective is maxi-mized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process.Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose ageneric and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a casestudy based on the design of a condensate treatment unit in an ammonia plant.

关键词: multi-objective optimization, uncertainty, chemical process design

SUN Li, Helen H. Lou. A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design[J]. , 2008, 16(1): 39-42.

孙力, Helen H. Lou. A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design[J]. , 2008, 16(1): 39-42.

References

1 Sikdar, S., “Sustainable development and sustainability metrics”, AIChE J., 49, 1928-1932 (2003).
2 The Institution of Chemical Engineers (IChemE), “The sustainable development progress metrics-recommended for use in the process industries”, http://www.getf.org/file/toolmanager/O16F26202 (2002).
3 AIChE Center for Waste Reduction and Technology (CWRT), Col- laborative Projects Focus Area: Sustainable Development, AIChE, New York, NY (2000).
4 Wendt, M., Li, P., Wozny, G., “Nonlinear-constraint process optimi- zation under uncertainty”, Ind. Eng. Chem. Res., 41, 3621-3629 (2002).
5 Cheng, L., Subrahmanian, E., Westerberg, A.W., “Design and plan- ning under uncertainty: Issues on problem formulation and solution”, Comp. Chem. Eng., 27, 781-801 (2003).
6 Sahinidis, N.V., “Optimization under uncertainty: State-of-the-art and opportunities”, Comp. Chem. Eng., 28, 971-983 (2004).
7 Mattson, C.A., Messac, A., “Pareto frontier based concept selection under uncertainty with visualization. Special issue on multidiscipli- nary design optimization”, OPTE: Optimizat. Eng., (6), 85-115 (2005).
8 Liu, M.L., Sahinidis, N.V., “Optimization in process planning under uncertainty”, Ind. Eng. Chem. Res., 35, 4154-4165 (1996).
9 Prekopa, A., Stochastic Programming, Kluwer Academic Publishers, Dordrecht (1995).
10 Pistikopoulos, E.N., Ierapetritou, M.G., “Novel approach for optimal process design under uncertainty”, Comp. Chem. Eng., 19 (10), 1089-1110 (1995).
11 Singh, A., Lou, H.H., “Hierarchical Pareto optimization for the sus- tainable development of industrial ecosystems”, Ind. Eng. Chem. Res., 45, 3265-3279 (2006).
12 Sun, L., “Study on chemical process integration for multi-objective optimization based on fuzzy set theory”, Ph. D. Thesis, Dalian Uni- versity of Technology, China (2004). (in Chinese)
13 Sun, L., He, G.H., Fan, X.S., Yao, P.J., “Calculation and application of compound environmental impact index based on fuzzy optimal selection theory”, J. Chem. Ind. Eng. (China), 55 (9): 1550-1554 (2004). (in Chinese)
14 Bare, J.C., Norris, G.A., Pennington, D.W., McKone, T., “TRACI, the tool for the reduction and assessment of chemical and other en- vironmental impacts”, J. Ind. Ecol., 6 (3/4), 49-78 (2002).
15 USEPA, The Benefits and Costs of the Clean Air Act: 1990 to 2010 (EPA-410-R99-001), USEPA, Washington, D.C., USA (1999).
16 USEPA, Exposure Factors Handbook EPA/600/8-89/043, Office of Health and Environmental Assessment, Washington, D.C., USA (1989).
17 Sun, L., Singh, A., Lou, H.H., “A case study of multi-objective op- timization under uncertainty in process design for sustainability”, In: AIChE Annual National Meeting, San Francisco, CA (2006).
18 Davis, L., Handbook of Genetic Algorithms, International Thomson Computer Press, London (1996).
19 Gen, M., Cheng, R., Genetic Algorithms and Engineering Design, John Wiley and Sons, New York (1997).
20 Sun, L., Fan, X.S., Yao, P.J., “Multi-objective fuzzy optimization algorithm for separation-recycle system”, Chin. J. Chem. Eng., 12 (2), 221-226(2004).

[1]	Wenhui Yang, Wuxi Qian, Zhihong Yuan, Bingzhen Chen. Perspectives on the flexibility analysis for continuous pharmaceutical manufacturing processes [J]. Chinese Journal of Chemical Engineering, 2022, 41(1): 29-41.
[2]	Mohammad Sadegh Sharafi, Mehdi Ghasemi, Mohammad Ahmadi, Alireza Kazemi. An experimental approach for measuring carbon dioxide diffusion coefficient in water and oil under supercritical conditions [J]. Chinese Journal of Chemical Engineering, 2021, 34(6): 160-170.
[3]	Markus Enekvist, Xiaodong Liang, Xiangping Zhang, Kim Dam-Johansen, Georgios M. Kontogeorgis. Estimating Hansen solubility parameters of organic pigments by group contribution methods [J]. Chinese Journal of Chemical Engineering, 2021, 29(3): 186-197.
[4]	Bo Liu, Yufei Wang, Xiao Feng. Optimization of circulating cooling water systems based on chance constrained programming [J]. Chinese Journal of Chemical Engineering, 2021, 40(12): 167-178.
[5]	Lukuan Yang, Wenqi Zhong, Li Sun, Xi Chen, Yingjuan Shao. Dynamic optimization oriented modeling and nonlinear model predictive control of the wet limestone FGD system [J]. Chinese Journal of Chemical Engineering, 2020, 28(3): 832-845.
[6]	Ying Chen, Zhihong Yuan, Bingzhen Chen. Process optimization with consideration of uncertainties-An overview [J]. Chin.J.Chem.Eng., 2018, 26(8): 1700-1706.
[7]	Weijun Li, Jakob Kjøbsted Huusom, Zhimao Zhou, Yi Nie, Yajing Xu, Xiangping Zhang. Multi-objective optimization of methane production system from biomass through anaerobic digestion [J]. Chin.J.Chem.Eng., 2018, 26(10): 2084-2092.
[8]	Ke Yang, Yijun He, Zifeng Ma. Multi-objective steady-state optimization of two-chamber microbial fuel cells [J]. , 2017, 25(8): 1000-1012.
[9]	Lili Tao, Bin Xu, Zhihua Hu, Weimin Zhong. Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm [J]. , 2017, 25(8): 983-991.
[10]	Baocang Ding, Hongguang Pan. Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance [J]. , 2016, 24(10): 1431-1441.
[11]	Lingjun Tan, Chen Yang, Nana Zhou. Synthesis/design optimization of SOFC-PEM hybrid system under uncertainty [J]. Chin.J.Chem.Eng., 2015, 23(1): 128-137.
[12]	Qinghua Chi, Weijie Zhang, Zhengshun Fei, Jun Liang. A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process [J]. Chin.J.Chem.Eng., 2015, 23(1): 112-120.
[13]	Juwari Purwo sutikno, Badhrulhisham Abdul aziz, Chin Sim yee, Rosbi Mamat. A New Tuning Method for Two-Degree-of-Freedom Internal Model Control under Parametric Uncertainty [J]. Chin.J.Chem.Eng., 2013, 21(9): 1030-1037.
[14]	JIAO Yunqiang, SU Hongye, LIAO Zuwei, HOU Weifeng. Modeling and Multi-objective Optimization of Refinery Hydrogen Network [J]. , 2011, 19(6): 990-998.
[15]	LI Chufu, HE Xiaorong, CHEN Bingzhen, XU Qiang, LIU Chaowei. A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery [J]. , 2008, 16(2): 241-246.

A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments