Coupling dynamic simulation and interactive multiobjective optimization for complex problems: An APROS-NIMBUS case study |
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| Affiliation: | 1. Department of Mathematical Information Technology, PO Box 35 (Agora), FI-40014 University of Jyväskylä, Finland;2. VTT Technical Research Centre of Finland, PO Box 1000, FI-02044 VTT, Finland;1. School of Science and Technology, Georgia Gwinnett College, Lawrenceville, GA 30045, United States;2. Dept of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL, United States;1. Institute for Infocomm Research, Singapore;2. University of Hong Kong, Hong Kong;3. Centre for Computer and Information Security Research, School of Computer Science and Software Engineering, University of Wollongong, Australia;1. Research Institute of Computer Science, Technical University of Loja, San Cayetano alto, Loja, Ecuador;2. Department of Computing, Polytechnic University of Madrid, Boadilla del Monte, Madrid, Spain;1. Universidade Federal de Ouro Preto, Computing Department, Ouro Preto, MG, Brazil;2. Universidade Federal de Minas Gerais, Computer Science Department, 31.270-010 Belo Horizonte, MG, Brazil |
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| Abstract: | Dynamic process simulators for plant-wide process simulation and multiobjective optimization tools can be used by industries as a means to cut costs and enhance profitability. Specifically, dynamic process simulators are useful in the process plant design phase, as they provide several benefits such as savings in time and costs. On the other hand, multiobjective optimization tools are useful in obtaining the best possible process designs when multiple conflicting objectives are to be optimized simultaneously. Here we concentrate on interactive multiobjective optimization. When multiobjective optimization methods are used in process design, they need an access to dynamic process simulators, hence it is desirable for them to coexist on the same software platform. However, such a co-existence is not common. Hence, users need to couple multiobjective optimization software and simulators, which may not be trivial. In this paper, we consider APROS, a dynamic process simulator and couple it with IND-NIMBUS, an interactive multiobjective optimization software. Specifically, we: (a) study the coupling of interactive multiobjective optimization with a dynamic process simulator; (b) bring out the importance of utilizing interactive multiobjective optimization; (c) propose an augmented interactive multiobjective optimization algorithm; and (d) apply an APROS-NIMBUS coupling for solving a dynamic optimization problem in a two-stage separation process. |
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| Keywords: | Pareto optimal solutions Dynamic process simulation IND-NIMBUS Simulation based optimization Augmented interactive multiobjective optimization algorithm Interactive method Apache Thrift Implementation challenges |
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