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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.