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PARALLEL MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS FOR WASTE SOLVENT RECYCLING
Citation:
Kim**, K. J. AND R L. Smith*. PARALLEL MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS FOR WASTE SOLVENT RECYCLING. Paul, D.R. (ed.), INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH. American Chemical Society, Washington, DC, 43(11):2669-2579, (2004).
Description:
Waste solvents are of great concern to the chemical process industries and to the public, and many technologies have been suggested and implemented in the chemical process industries to reduce waste and associated environmental impacts. In this article we have developed a novel parallel multiobjective steady-static genetic algorithm (pMSGA) for designing environmentally benign and economically viable processes for waste solvent recycling. pMSGA can efficiently solve this complex multiobjective design problem and provide accurate and uniform Pareto optimal (i.e., trade-off) solutions. In addition, it can approximate a wider range of the Pareto front than other multiobjective genetic algorithms. As a case study, acetic acid recovery from aqueous waste mixtures is investigated by maximizing total profit and minimizing potential environmental impacts (PEIs) simultaneously. At low acetic acid feed composition (xF=0.25) many of the Pareto optimal solutions are economically infeasible, and in addition PEI reduction is small. But at medium and high feed compositons (xF=0.30 and 0.35) the total profit is very large and PEI reduction is significant as well.