Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach. (4th January 2021)

• Record Type: Journal Article
• Authors: Fernandez de Canete, J.; del Saz-Orozco, P.; Gómez-de-Gabriel, J.; Baratti, R.; Ruano, A.; Rivas-Blanco, I.
• Is Part Of: Computers & chemical engineering. Volume 144(2021)
• Publication Date: 2021-01-04
• Subjects: Neural networks -- Genetic algorithms -- Soft-sensing -- Optimized control -- Activated sludge process; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.107146
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: We develop an adaptive neural-network-based soft sensor to predict effluent characteristics. We estimate primary variables from secondary variables in activated sludge process. It is implemented a genetic algorithm to determine the control actions. This strategy presents better results complying with legal constraints and reduced expenses. Abstract: 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. Graphical abstract: