Data-Driven Economic Predictive Control of Wastewater Treatment Process with Input-Output Koopman Operator

In this work, we address the problem of the economic operation of wastewater treatment plants by proposing a data-driven economic predictive control approach. First, we propose a deep input-output Koopman modeling framework, which is able to predict the overall economic operational cost for the water treatment process based on input data and partial state measurements. Subsequently, based on the learned model, a convex economic model predictive control (EMPC) strategy is developed. This control strategy improves the overall operational performance in a computationally efficient manner. The simulation results validate the effectiveness of our proposed approach and demonstrate its superiority over a benchmark EMPC method.