Optimal water resource management for sustainable development of the chemical industrial park under multi-uncertainty and multi-pollutant control

Abstract

Two major concerns over the chemical industrial park (CIP) operations are high consumption of water resources and large amount of pollutant emissions. This study develops an interval chance-constrained programming model for industrial water resources management (ICCP-IWM) with consideration of multi-uncertainty and multi-environmental constraints. Uncertainties expressed as intervals and probability distributions are merged in the ICCP-IWM framework. The developed model is used to solve a real-world water resource management problem in the Shenyang Chemical Industrial Park to demonstrate its capacity and effectiveness, where the objective is to minimize the system cost of water pathways and pollutant-emission control under a series of constraints. Interval solutions with respect to water resources allocation, wastewater management, and pollutant emissions could be generated. Results indicate that a lower violation risk leads to an increased strictness of the constraints, then to a higher system cost; conversely, a higher violation risk results in a lower system cost, at the expense of an increase in the risk. These findings would be recommended by the decision-makers because of their applicability for practical decision process providing the optimal strategy for sustainable water resource management under multiple uncertainties.