Multiobjective Optimization of Plastic Waste Sorting and Recycling Processes Considering Economic Profit and CO2 Emissions Using Nondominated Sorting Genetic Algorithm II

Abstract

Plastic waste has become a severe threat to the environment as increasing amounts of plastic waste are generated every year. To solve this problem, it is crucial to increase the recycling rate of plastic waste with proper sorting and recycling processes. However, sorting and recycling costs vary depending on the specific process, and CO2 is inevitably generated during recycling. Therefore, this study developed a novel multiobjective optimization model based on mixed-integer nonlinear programming to optimize plastic waste sorting and recycling processes according to target polymer types while maximizing net profit and minimizing CO2 emissions. Recycling solutions were proposed using a nondominated sorting genetic algorithm, which enabled the selection of a portfolio of plastic recycling methods for each plastic type depending on the importance of the two objectives: maximum net profit and minimum total CO2 emissions. As a result, Pareto-optimal solutions with a net profit distribution of 35–1936 million USD/year and CO2 emissions of 9.7–17.0 kt/year were obtained. Furthermore, the Pareto-optimal front was analyzed to provide representative optimal solutions, which can provide decision makers with a wide range of choices when determining process specifications.