Adaptive critic design with weight allocation for intelligent learning control of wastewater treatment plants

Abstract

With the deepening of modernization and industrialization, the issues of water pollution and scarcity have become more pressing. To address these issues, many wastewater treatment factories have been built to improve the reuse of water resources. However, the control of the wastewater treatment process (WWTP) is a complex task due to the highly nonlinear and strongly coupled nature. It is challenging to develop the accurate mechanism models of the wastewater treatment system. The improvement of the efficiency for the WWTP is crucial to safeguard the urban ecological environment. In this paper, adaptive critic with weight allocation (ACWA) is developed to address the optimal control problem in the WWTP. Different from the previous methods of the WWTP, system modeling is not adopted in this paper, which meets the actual physical background of the wastewater treatment system to a great extent. In addition, the actor-critic algorithm in reinforcement learning is used as the basic structure in the ACWA. It is worth noting that a novel weighted action-value function and the advantage function are introduced in the weight updating process of the action network and the critic network. The experimental results show that the control accuracy of the ACWA is greatly improved compared with the previous control methods.