Artificial Neural Network (ANN) Modelling for Biogas Production in Pre-Commercialized Integrated Anaerobic-Aerobic Bioreactors (IAAB)

Abstract

The use of integrated anaerobic-aerobic bioreactor (IAAB) to treat the Palm Oil Mill Effluent (POME) showed promising results, which successfully overcome the limitation of a large space that is needed in the conventional method. The understanding of synergism between anaerobic digestion and aerobic process is required to achieve maximum biogas production and COD removal. Hence, this work presents the use of artificial neural network (ANN) to predict the COD removal (%), purity of methane (%), and methane yield (LCH4/gCODremoved) of anaerobic digestion and COD removal (%), biochemical oxygen demand (BOD) removal (%), and total suspended solid (TSS) removal (%) of aerobic process in a pre-commercialized IAAB located at Negeri Sembilan, Malaysia. MATLAB R2019b was used to develop the two ANN models. Bayesian regularization backpropagation (BR) showed the best performance among the 12 training algorithms. The trained ANN models showed high accuracy (R2 > 0.997) and demonstrated good alignment with the industrial data obtained from the pre-commercialized IAAB over a 6-month period. The developed ANN model is subsequently used to create the optimal operating conditions which maximize the output parameters. The COD removal (%) was improved by 33.9% (from 68.7% to 92%), while the methane yield was improved by 13.4% (from 0.23 LCH4/gCODremoved to 0.26 LCH4/gCODremoved). Sensitivity analysis shows that COD inlet is the most influential input parameters that affect the methane yield, anaerobic COD, BOD and TSS removals, while for aerobic process, COD removal is most affected by mixed liquor suspended solids (MLSS). The trained ANN model can be utilized as a decision support system (DSS) for operators to predict the behavior of the IAAB system and solve the problems of instability and inconsistent biogas production in the anaerobic digestion process. This is of utmost importance for the successful commercialization of this IAAB technology. Additional input parameters such as the mixing time, reaction time, nutrients (ammonium nitrogen and total phosphorus) and concentration of microorganisms could be considered for the improvement of the ANN model.