Abstract
System Analysis and Optimization of Multi-Contaminant Water Reuse Network with and without Regeneration using a Hybrid Genetic Algorithm
Highlights
Water is a key element for the normal functioning of many industries
Despite all the above mentioned significant achievements in water network analysis research that utilizes optimization-based modelling techniques and computational strategies there are some challenges in the issues of non convexity, nonlinearity, simultaneous optimization of the interactions of rigorous design models for wastewater treatment technologies and multiple water-using units, enabling faster numerical solutions, development of more meaningful optimization-based formulations, uncertainty, alternative methods for optimization under uncertainty and extension to resource recovery systems
Inconsideration of the above, this paper developed a general framework for water allocation planning (WAP) that can be applied to different water allocation contexts e.g. oil refinery, food production, pharmaceutical etc, by considering multiple contaminants with and without regeneration
Summary
Water is a key element for the normal functioning of many industries. It is intensively utilised in food, pulp or paper, pharmaceutical, petrochemical and chemical industries, and so on. Despite all the above mentioned significant achievements in water network analysis research that utilizes optimization-based modelling techniques and computational strategies there are some challenges in the issues of non convexity (which leads to the presence of multiple local optimal solutions), nonlinearity (which is due to considering regeneration technologies as nonlinear mixed with linear objective function), simultaneous optimization of the interactions of rigorous design models for wastewater treatment technologies and multiple water-using units, enabling faster numerical solutions, development of more meaningful optimization-based formulations, uncertainty (posed by the data obtained from the industry, the assumed parameters in analysis of the problems), alternative methods for optimization under uncertainty and extension to resource recovery systems.
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