Keywords: multi-objective efficient genetic algorithm, MOEGA, Hammersley sequence sampling, HSS, uncertainty, pollution prevention, environmental pollution, optimal design, waste treatment, environmental impact, optimisation, genetic algorithms, solvent selection, solvent recycling
Multi-objective integrated solvent selection and solvent recycling under uncertainty using a new genetic algorithm
The optimal design of waste treatment processes always involves several objectives to be considered like cost and Environmental Impact (EI). Hence a multi-objective optimisation framework is required whose solution is not a single value but a Pareto set, which includes the alternatives representing potential compromise solutions among the objectives. Further, uncertainties are inherent in EI assessment, these uncertainties need to be propagated and analysed. In this paper, a multi-objective optimisation algorithm called MOEGA is developed. This new and efficient algorithm identifies more tradeoff solutions (with and without uncertainties in EI) than before.