Abstract
To resolve the conflict between multiple performance indicators in the complicated wastewater treatment process (WWTP), an effective optimization control scheme based on a dynamic multi-objective immune system (DMOIA-OC) is designed. A dynamic optimization control scheme is first developed in which the control process is divided into a dynamic layer and a tracking control layer. Based on the analysis of the WWTP performance, the energy consumption and effluent quality models are next established adaptively in response to the environment by an optimization layer. An adaptive dynamic immune optimization algorithm is then proposed to optimize the complex and conflicting performance indicators. In addition, a suitable preferred solution is selected from the numerous Pareto solutions to obtain the best set of values for the dissolved oxygen and nitrate nitrogen. Finally, the solution is evaluated on the benchmark simulation platform (BSM1). The results show that the DMOIA-OC method can solve the complex optimization problem for multiple performance indicators in WWTPs and has a competitive advantage in its control effect.
Highlights
With the ongoing improvement of the economy and living standards, water consumption and wastewater discharge increase dramatically
The results show that the control system has better stability and lower energy consumption (EC) than traditional control methods
The results show that when the flow and concentration change, the proposed dynamic immune optimization control method can significantly reduce EC while improving the outflow water quality, thereby effectively improving the control performance of wastewater treatment process (WWTPs)
Summary
With the ongoing improvement of the economy and living standards, water consumption and wastewater discharge increase dramatically. Due to the typical nonlinear, multi-variable, unstable, and time-varying characteristics of the wastewater treatment process (WWTPs), higher requirements are put forward for the operation and management. Under the premise of more strict water quality standards, energy-saving and emission reduction are major challenges (Büyüközkan et al 2021; Li et al 2021; Shiek et al 2021; Busch et al 2013; Liu et al 2018). Guerrero et al proposed a model-based set-point optimization method to improve the performance of control systems (Guerrero et al 2011). A single cost function is used to evaluate the optimal performance of fixed and time-varying settings
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