Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/12959
Title: Optimization of recovery processes for multiple economic and environmental criteria
Authors: LEE SU-QIN ELAINE
Keywords: Multi-Objective Optimization; Sustainability; Environmental Objectives; Recovery Processes; Net Flow Method
Issue Date: 2-Sep-2009
Source: LEE SU-QIN ELAINE (2009-09-02). Optimization of recovery processes for multiple economic and environmental criteria. ScholarBank@NUS Repository.
Abstract: Of the three spheres of sustainabilityseconomic development, environmental stewardship, and societal equity,only the first two are quantifiable based on process variables. While economic criteria such as profit beforetaxes, payback period, and net present worth are well established, environmental objectives are recent andthere is no general consensus on aggregation methods for calculating environmental impact. Many contributingfactors have been identified for environmental impacts: impact on humans, ecosystemsterrestrial and aquatic,and local/global temperaturessglobal warming and ozone depletion, as well as photochemical oxidation,acid rain, and eutrophication. However, reported studies have used one aggregate environmental index as theobjective in process design besides one economic objective. Hence, feasibility and usefulness of processoptimization for more than two economic and environmental objectives is studied. Two case studies: a VOC(volatile organic component) recovery system and a solvent recovery system are chosen and optimized forboth economic and environmental objectives using the elitist nondominated sorting genetic algorithm. Thecontributing factors to the environmental impacts are optimized individually or grouped into a few indiceswhere appropriate. The objectives chosen for each round of optimization is two, several, or more, with atleast one economic and one environmental objective. As they are partially or totally conflicting, Paretooptimalsolutions are obtained. These elucidate the trade-offs present, and the decision maker would be betterequipped in choosing the best solution. The net flow method is then used to identify the best Pareto-optimalsolution, whereby the decision makerb s preference has to be declared. Pareto-optimal solutions and the bestPareto-optimal solution for the two case studies are presented and discussed. Insights gained from consideringa number of environmental objectives for process optimization are highlighted.
URI: http://scholarbank.nus.edu.sg/handle/10635/12959
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