Coupling dynamic simulation and interactive multiobjective optimization for complex problems: An APROS-NIMBUS case study

K. Sindhya (Corresponding Author), V. Ojalehto, Jouni Savolainen, Hannu Niemistö, J. Hakanen, K. Miettinen

    Research output: Contribution to journalArticleScientificpeer-review

    17 Citations (Scopus)


    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.
    Original languageEnglish
    Pages (from-to)2546-2558
    JournalExpert Systems with Applications
    Issue number5
    Publication statusPublished - 2014
    MoE publication typeA1 Journal article-refereed


    • Apache Thrift
    • augmented interactive multiobjective optimization algorithm
    • dynamic process simulation
    • implementation challenges
    • interactive method
    • Pareto optimal solutions
    • simulation based optimization


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