Disjunctive programming model for the synthesis of property-based water supply network with multiple resources

Abstract

Industrial water supply networks can use multiple water resources (i.e., municipal water, surface water, municipal treated wastewater, groundwater, and seawater) with their respective pre-treatment technologies to meet the industrial water demand. The water quality of each water resource varies, and the associated pre-treatment technologies provide different technical and economic efficiencies that directly impact the overall cost of the water supply network. This paper proposed a superstructure-based mathematical modeling optimization to design the multi-resources industrial water supply network with optimal pre-treatment technologies. The established model includes the relative equations among different pre-treatment units and water sinks, flow rate constraints, property constraints, logic constraints, and cost constraints. These constraints are used to describe water treatment technologies selection through general disjunctive programming (GDP) and transform them into a mixed-integer nonlinear programming (MINLP) model for the problem solution. The minimum annualized cost of the water supply network is taken as an objective function. The optimization and comparison analysis of the coastal oil refinery water supply system is illustrated. The results show that an integrated water supply network of surface water and municipal treated wastewater has the lowest annualized cost of 12.3 million CNY/y. The cost is reduced by 7.44 million CNY/y compared to municipal water as the single water resource. This integrated water supply network saves freshwater resources, provides substantial economic gains, and encourages municipal treated wastewater reuse.