Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach

During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study presents a machine learning-based control strategy for wastewater treatment plants, optimizing costs and regulation violations using neural networks and a neuro-genetic optimum model-based control approach. Testing on a simulation model and validation with operational data showed optimal control performance, meeting effluent requirements while reducing investment in expensive sensor devices.