Optimal Management of Multistakeholder Macroscopic Water Networks with Social, Economic, and Environmental Considerations

Abstract

This work presents an optimization model for macroscopic water networks, considering wastewater treatment, rainwater harvesting, allocation, and recycle/reuse. Given the pressing problem of water scarcity the world faces, it is important to address it while considering all of the stakeholders from various sectors. Furthermore, several intertwining objectives must be considered, including water resource conservation, social progress, and economic benefits. A systematic optimization approach is proposed to solve this problem. Trade-offs are established among the unsatisfied demand for domestic users, system profit, and the dynamic level of water wells. A superstructure is developed to embed configurations, considering domestic and industrial users. The users' water demands are seasonally dependent. Treatment options are considered to render the water in acceptable quality for its end use. The system has different storage devices that can be introduced at different locations in the targeted region. The solution of the optimization formulation provides a characterization of the design and operation of the macroscopic water system while providing the decision-makers with rigorous trade-offs and synergistic opportunities.

Optimal Location of Wastewater Treatment Plants Considering Multiple Factors: A Case Study of Phnom Penh

“…Because geographic information system (GIS) have the ability to manage multiple sources and large amounts of spatial data (Kao and Lin, 1996), WWTPs location selection can be accomplished using GIS by analyzing various indicators such as topography, prevailing wind direction, hydrology, land use type and spatial distribution of surface water, etc (Hongbo, 2019;Nigusse et al, 2020). For the location selection methods of WWTPs, various models have been investigated thoroughly, such as the multi-objective optimization model (Rezaei et al, 2019) for twofold optimization of price and energy consumption; the method amalgamated GIS, analytic hierarchy process (AHP), and remote sensing (RS) techniques (Liu et al, 2022); the model based on frizzy logic and multi criteria decision creation (Makropoulos et al, 2007); the model combined artificial neural networks (Wang and Jamieson, 2002) with genetic algorithms, and the manner by using GIS software integrated with the AHP (Hama et al, 2019) to optimization model (Jose Olivarez-Areyan et al, 2022;Alfaisal, 2022). However, the current location selection models have mostly focused on algorithm principle design, and there are few algorithms that integrate location selection models with local social-cultural factors.…”