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

• Autor(a) principal: Fernandez de Canete, J.
• Data de Publicação: 2021
• Outros Autores: del Saz-Orozco, P., Gómez-de-Gabriel, J., Baratti, R., Ruano, Antonio, Rivas-Blanco, I.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/14968
• topic: Neural networks; Activated sludge process; Genetic algorithms; Soft-sensing; Optimized control

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.