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

Fernandez de Canete, J.; del Saz-Orozco, P.; Gómez-de-Gabriel, J.; Baratti, R.; Ruano, Antonio; Rivas-Blanco, I.

http://hdl.handle.net/10400.1/14968

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Fernandez de Canete, J.

Abstract(s)

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.