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.
Highlights
Water management is currently facing a major challenge
The neural identification network is responsible for learning the dynamic behaviour of the secondary variables, in a way, it can relate the level of the secondary variables with the control inputs and past states
An adaptive neural network was applied to estimating the effluent characteristics of a wastewater treatment process model from secondary variables, while an additional neural network was developed to estimate the dynamic behaviour of the referred secondary controllable variables
Summary
Water management is currently facing a major challenge. Wastewater generated by human activity is currently a serious problem and one of the most important sources of pollution for watercourses and aquifers. Advanced alternatives to solve the purification problems are available on the market, and with their implementation it would be possible to curb the appearance of pollutant discharges and their consequences, as well as facilitating their prevention. Santin et al (2017) described the application of effective treatments in costs of both operation and maintenance to improve water qualit. An optimized treatment at a global level would entail a huge reduction in operating costs, and a notable reduction in levels of effluent pollution (Hreiz et al, 2015a; Kim et al, 2105; Asadi et al, 2016; Zhu and Anderson, 2017)
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