Decision support tools for environmentally benign process design under uncertainty

This paper presents a new systematic framework for the synthesis of an environmentally benign process under uncertainty. The uncertainty is classified depending on its sources and mathematical model structure as deterministic or stochastic. The proposed methodology is a two-layer algorithm. In the outer layer, the synthesis problem is represented by a multi-objective optimization problem considering the performances associated with design parameters. In the inner layer, the problem is expressed as a single-objective optimization problem taking in to account the operating performances in the presence of uncertainty. The proposed hybrid approach consisting of multi-period and stochastic optimization formulations is then employed. Additionally, the effect of variability on decisions related to process performance and quality is also discussed. Applicability of the developed methodology is illustrated in a case study. Four configurations of membrane-based toluene recovery process are investigated to quantitatively compare economic, environmental and robustness performance of each configuration. The developed methodology can select solutions with minimal environmental impacts and adequate robustness at a desired economic performance.

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