Optimizing biogas production from palm oil mill effluent utilizing integrated machine learning and response surface methodology framework

This study presents a novel approach to optimize the anaerobic digestion of palm oil mill effluent (POME) for maximum biogas production on an industrial scale. Unlike most optimization studies which are limited to laboratory scale, this study utilized data from a real-world industrial setting to simulate the anaerobic digestion (AD) process. The processes include feedstock pre-treatment, biogas scrubbing, wastewater treatment, and sludge handling. The impact of process conditions such as hydraulic retention time (HRT), organic loading time (OLR), anaerobic sludge recycling ratio (RRAS), treatment effluent recycling ratio (RRTE), and reaction temperature on chemical oxygen demand (COD) removal and methane yield was analysed. An integration of response surface methodology (RSM) and artificial neural network (ANN) is applied to predict the optimal values for the process conditions. The results showed that the optimal values for HRT, temperature, OLR, RRAS, and RRTE are 38.58 days, 45 °C, 1.033 g COD/(L�day), 0.052, and 0.95, respectively. The optimization improves the COD removal efficiency, SS removal efficiency, and biogas production by 7.38, 8.37 and 16.18, respectively when processing 24,134 L/h of POME feed. The optimized AD process is economically viable with a payback period of 5.34 years and a net present value (NPV) of 5,079,000. © 2023