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 CO2is 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 CO2emissions. 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 CO2emissions. As a result, Pareto-optimal solutions with a net profit distribution of 35-1936 million USD/year and CO2emissions 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.
|Number of pages||10|
|Journal||ACS Sustainable Chemistry and Engineering|
|Publication status||Published - 2022 Oct 10|
Bibliographical noteFunding Information:
This work was supported by the Korean Institute of Industrial Technology within the framework of the “Development of Global Optimization System for Energy Process” project [grant numbers IR-22-0040, IZ-22-0049 and UR-22-0031].
© 2022 American Chemical Society. All rights reserved.
All Science Journal Classification (ASJC) codes
- Environmental Chemistry
- Chemical Engineering(all)
- Renewable Energy, Sustainability and the Environment