Biogas production estimation using data-driven approaches for cold region municipal wastewater anaerobic digestion

The objective of this study was to estimate biogas (including methane, carbon dioxide and hydrogen sulphide) production rates from the anaerobic digesters at the Saskatoon Wastewater Treatment Plant (SWTP), Saskatchewan, Canada. Average daily ambient temperatures typically fluctuate between -40 degrees C and 30 degrees C over the year making the management of the SWTP processes challenging. Operating parameters were taken from 2014 to 2016 including volatile fatty acids (VFAs), total solids, fixed solids, volatile solids, pH, and inflow rate. The input parameters were processed using two methods including a correlation test and principal component analysis (PCA) to determine highly correlated variables prior to use in models. The two models used to estimate biogas production rates are a multi-layered perceptron feed forward artificial neural network (ANN) and an adaptive network-based fuzzy inference system (ANFIS) with grid partition (GP), subtractive clustering (SC) and fuzzy c-means clustering (FCMC). The models using PCA processed variables had reasonable performances with shorter model processing times, while reducing model input data. Among various structures of ANN and ANFIS models for estimation of biogas generation, the ANFIS-FCMC results had better agreement with the observed data. Its average approximation of emission rates of CH4, CO2 and H2S from the wastewater digesters were 3,086, 6,351, and 41.5 g/min, respectively. Our group is assessing similar estimation methodology for the remaining SWTP wastewater treatment processes that are more highly impacted by the seasonal temperature variations including primary and secondary treatment processes.