Enhancing Biogas Production from Palm Oil Mill Effluent: Integrated Cooling Tower Optimisation, Artificial Neural Network Modelling, and Techno-Economic Feasibility Analysis

This study evaluated whether a cooling tower can stabilise the temperature of raw palm oil mill effluent before anaerobic digestion and improve biogas performance. Industrial operating data was trained on an artificial neural network, followed by optimisation, process simulation, and techno-economic evaluation. The optimised outlet temperature was 38. 9°C, yielding a simulated biogas production rate of 12, 683 m 3 /day and a methane yield of 0. 3135 Nm 3 /kg COD removed. Predictive accuracy was high with a coefficient of determination of 0. 9732 and a mean squared error of 0. 2096, and flowsheet simulation in SuperPro Designer V9 reproduced plant data with less than 15% deviation. Sensitivity analysis identified temperature as the dominant driver of methane yield, while high feed rates suppressed methanogenesis through substrate overloading. The techno-economic assessment estimated total annual revenues of 4, 142, 000, a payback time of 5. 89 years, and an internal rate of return of 13. 52%, corresponding to a 29% shorter payback and an 11. 7% higher internal rate of return than conventional palm oil mill effluent treatment with cooling ponds and mixing tanks. These results support adopting cooling tower-based temperature control to raise biogas output, sustain higher methane quality, and strengthen project economics at an industrial scale. • Cooling tower stabilised POME temperature and raised biogas and methane outputs. • ANN-optimised conditions achieved 38. 9 °C and 12, 683 m 3 /d biogas with high accuracy. • TEA showed a 5. 89-year payback and 13. 52% IRR for the CT system