Brewery wastewater treatment plant key-component estimation using moving-window recurrent neural networks

Abstract

This work proposes an experimental validation of software sensors for advanced on-line anaerobic digester process monitoring. The considered strategy is based on cheap available measurements (conductivity, temperature, pH, redox potential, etc) to reconstruct key component trajectories such as volatile fatty acid, carbonate and alkalinity concentrations, as well as biogas composition (methane, carbon dioxide, etc). The proposed solution considers a radial basis function artificial neural network (RBF -ANN) structure, using data processing (principal component analysis) and an efficient and fast sequential learning algorithm. In order to better reproduce unknown and complex process dynamics, the combination of a moving-window technique with a simple Jordan recurrent ANN structure (MW - RBF - RNN) is proposed. Comparative results based on real industrial data illustrate the estimation improvements provided by the MW - RBF - RNN with respect to the classical RBF - ANN structure.