Multiobjective optimization in terms of economics and potential environment impact for process design and analysis in a chemical process simulator

This paper is devoted to an application of the MOOP (multiobjective optimiz ation programming) concept to the practical field of chemical engineering t o take into account the trade-off between economics and pollution with appr opriate analysis methods. Optimization of the process is performed along an infeasible path with the SQP (successive quadratic programming) algorithm. One of the objective functions, the global pollution index function, is ba sed on potential environmental impact indexes calculated by using the hazar d value (HV). The other is the cost-benefit function. To analyze the biobje ctive optimization system in terms of economics and potential environmental impact, the noninferior solution curve (Pareto curve) is formed using SWOF (summation of weighted objective functions), GP (goal programming), and PS I (parameter space investigation) methods within a chemical process simulat or. We can find the ideal compromise solution set based on the Pareto curve . The multiobjective problem is then interpreted by sensitivity and elastic ity analyses of the Pareto curve that give the decision basis between the c onflicting objectives.