Model-free control based on reinforcement learning for a wastewater treatment problem

Abstract

This article presents a proposal, based on the model-free learning control (MFLC) approach, for the control of the advanced oxidation process in wastewater plants. This is prompted by the fact that many organic pollutants in industrial wastewaters are resistant to conventional biological treatments, and the fact that advanced oxidation processes, controlled with learning controllers measuring the oxidation-reduction potential (ORP), give a cost-effective solution. The proposed automation strategy denoted MFLC-MSA is based on the integration of reinforcement learning with multiple step actions. This enables the most adequate control strategy to be learned directly from the process response to selected control inputs. Thus, the proposed methodology is satisfactory for oxidation processes of wastewater treatment plants, where the development of an adequate model for control design is usually too costly. The algorithm proposed has been tested in a lab pilot plant, where phenolic wastewater is oxidized to carboxylic acids and carbon dioxide. The obtained experimental results show that the proposed MFLC-MSA strategy can achieve good performance to guarantee on-specification discharge at maximum degradation rate using readily available measurements such as pH and ORP, inferential measurements of oxidation kinetics and peroxide consumption, respectively.