A Robust Fractional Model for Improving Industrial Park Water Use Efficiency Under Dual Uncertainties

Abstract

As an existing form of high aggregation of industries, industrial parks, have problems with a large demand for water and sewage discharge. Therefore, developing an effective approach to improve the efficiency of industrial park water use is urgently needed. Originating from internal and external changes, complicated uncertainties may coexist in multiple system components and their correlations within industrial park water resource management. Previous studies ignored the uncertain probability related to water inflow, which is difficult to estimate for long-term decision-making problems. This research describes the exploitation of an inexact two-stage stochastic partial fractional programming (ITSPF) method for sustainable industrial park water supply, under dual uncertainties. ITSPF is developed via the synthesis of linear partial information, interval programming, two-stage stochastic programming, and fractional programming techniques. It could improve conventional industrial park water resource optimization by solving the uncertain probability of water inflow levels while optimizing the ratio issues. The results show that various water allocation plans within different inflow level probabilities can be generated by handling the trade-off between water consumption and system benefit. And the amount of reclaimed water would be increased under the higher risk of water shortage. The comparisons of ITSPF results against the least-cost model and model with deterministic probability of water inflow levels demonstrated that ITSPF could not only result in higher resource-use efficiency, but also avoid missing possible solution sets and offer a pragmatic way for obtaining satisfactory alternatives by providing wider adjustable ranges.