An effective dynamic immune optimization control for the wastewater treatment process

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.

Automated summary

The optimization control scheme based on dynamic multi-objective immune system has shown effectiveness in resolving conflicting performance indicators in wastewater treatment plants (WWTPs). By dividing the control process into dynamic and tracking control layers, adapting energy consumption and effluent quality models, and utilizing an adaptive dynamic immune optimization algorithm, the method successfully optimized complex and conflicting performance indicators. The competitive advantage of this method in control effectiveness was demonstrated through evaluation on a benchmark simulation platform.