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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 identi-fying 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.
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. the process, property, market fluctuation, errors in model prediction and so on that would affect the performance of a process. Wherefore, 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 identi-fying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.