Multi-Objective Optimization of Synergic Perchlorate Pollution Reduction and Energy Conservation in China’s Perchlorate Manufacturing Industry

Abstract

Perchlorate is a highly mobile and persistent toxic contaminant, with the potassium perchlorate manufacturing industry being a significant anthropogenic source. This study addresses the Energy Conservation and Perchlorate Discharge Reduction (ECPDR) challenges in China’s potassium perchlorate manufacturing industry through a multi-objective optimization model under uncertainty. The objectives encompass energy conservation, perchlorate discharge reduction, and economic cost control, with uncertainty parameters simulated via Latin Hypercube Sampling (LHS). The optimization was performed using both the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Generalized Differential Evolution 3 (GDE3) algorithm, enabling a comparative analysis. Three types of decision-maker preferences were then evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to generate optimal decision strategies. Results revealed: (1) The comprehensive perchlorate discharge intensity in China’s potassium perchlorate industry is approximately 23.86 kg/t KClO4. (2) Compared to NSGA-II, GDE3 offers a more robust and efficient approach to finding optimal solutions within a limited number of iterations. (3) Implementing the optimal solution under PERP can reduce perchlorate discharge intensity to 0.0032 kg/t. (4) Processes lacking primary electrolysis should be phased out, while those with MVR technology should be promoted. This study provides critical policy recommendations for controlling perchlorate pollution and guiding the industry toward cleaner and more sustainable production practices.