Enhancing Wastewater Treatment Plant Efficiency: Constraint Optimization and Regression Analysis Approach

This research work presents a comprehensive approach to optimizing wastewater treatment processes, emphasizing the improvement of both the energy efficacy of aeration and effluent quality. A multi-step methodology that combines the Genetic Algorithm (GA) optimization technique, data collection, linear regression analysis, constraint formulation, and the application of constrained optimization was adopted. In the initial phase, unconstrained GA optimization was used to find the values of the decision variables that optimized the objectives: aeration energy and effluent quality index. The dataset acquired during this phase indicated the values of the decision variables, objective functions, and other important variables such as effluent ammonia and nitrogen concentration. Linear regression analysis was performed using this dataset to establish linear correlations between the decision variables and the constraint variables. Constraint equations were then formulated using the coefficient values. A new GA optimization was executed with added constraints. The optimal values for the decision variables, which adhered to the constraints, were identified. The methodology presented here provides a robust, data-driven framework for optimizing wastewater treatment processes, with the potential for substantial energy savings and improved environmental performance in water treatment facilities.