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.info:eu-repo/semantics/publishedVersio

Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network

Recent studies into the estimation and control of an activated sludge process (ASP) at a wastewater treatment plant suggest that artificial-intelligence methods, such as neural networks, fuzzy systems and genetic algorithms, can improve the plant performance in terms of reduced operation costs and improved effluent quality. In this paper, a neural-network-based soft sensor is developed for the on-line prediction of effluent concentrations in an ASP in terms of primary hard-to-measure variables, such as chemical oxygen demand, total nitrogen content and total suspended solids, starting from secondary on-line easy to-measure variables, such as oxygen and nitrogen compound concentrations in biological tanks, input flow rate and alkalinity, among others. An algorithm based on principal component analysis is applied to select the optimal net input vectors for the soft sensor, using an appropriated number of samples of the secondary variables set. The proposed soft sensor is tested on the ASP of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data. Satisfactory low values for mean and maximum absolute prediction errors are obtained, even when high values of sampling time of primary variables are set, as it is frequently done during monitoring operation. In this way, data-driven soft-sensors based on neural networks can become valuable tools for plant operators for the recognition of operational states in terms of low cost and efficient prediction of primary process variables such as chemical oxygen demand, total nitrogen content and total suspended solids, therefore avoiding the acquisition of expensive and sometimes unreliable instruments for measuring nutrient concentrations in plant

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