Simulation and Control of Anaerobic Digestion Process for Biogas Energy Production: A Methodology Comparison

Abstract

State-of-the-art mechanically driven model of the anaerobic digestion process was compared to data driven methodology of System Identification and Machine Learning. Through collaboration with OsloMet, Veas WWTP and DHI was data collected for analysis and to compose a dataset that DHIs West software could use to run their mechanically driven simulation based on the ADM1 model. Live data from the same plant was also collected to create a dataset where two data driven models were created; one using System Identification methodology and the other using Machine Learning. Both data driven models were attempted for closed-loop integration with controllers. DHIs model output tracked the actual output, showing similar behaviour to the actual plant. The data driven models showed less stable behaviour, although at times closer tracking to actual output than in West. Closed-loop integration with PI-controller and MPC on the SI-model showed close setpoint tracking with predictive inputs to the MV. Controller implementation on the ML-model showed less stable behaviour and further development of controller tuning is needed. Keywords: Renewable Energy, Wastewater Treatment, Biogas Production, Anaerobic Digestion, Veas, DHI, West, Simulation, Control, Machine Learning, Recurrent Neural Network, Long-Short Term Memory, System Identificatio