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 optimization programming) concept to the practical field of chemical engineering to take into account the trade-off between economics and pollution with appropriate 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 based on potential environmental impact indexes calculated by using the hazard value (HV). The other is the cost-benefit function. To analyze the biobjective 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 PSI (parameter space investigation) methods within a chemical process simulator. We can find the ideal compromise solution set based on the Pareto curve. The multiobjective problem is then interpreted by sensitivity and elasticity analyses of the Pareto curve that give the decision basis between the conflicting objectives.