Optimisation and performance evaluation of response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the prediction of biogas production from palm oil mill effluent (POME). (1st March 2023)

• Record Type: Journal Article
• Authors: Chong, Daniel Jia Sheng; Chan, Yi Jing; Arumugasamy, Senthil Kumar; Yazdi, Sara Kazemi; Lim, Jun Wei
• Is Part Of: Energy. Volume 266(2023)
• Publication Date: 2023-03-01
• Subjects: Palm oil mill effluent -- Biogas -- Neural networks -- Adaptive neuro-fuzzy inference system (ANFIS) -- Response surface methodology (RSM); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126449
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In recent years, machine learning (ML) techniques have been developed to predict the performance of anaerobic digestion (AD) processes including methane potential and reactor stability. However, their practical applications to industrial-scale palm oil mill effluent (POME) treatment plant are limited. In this study, ML algorithms such as response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) are employed to model the biogas production and methane yield from the AD of POME in a local industry-scale anaerobic covered lagoon. Results demonstrated that these models were well aligned with two years of operational data with high coefficient of determination (R 2 ) of up to 0.98. ANFIS yields the highest prediction accuracy, with R 2 of 0.9791 along with the lowest mean absolute error (MAE) of 0.0730 and root mean squared error (RMSE) of 0.1438. Subsequently, ANFIS is used in the multi-objective optimisation to maximise the biogas production and methane yield. Optimal conditions for the temperature of the anaerobic digester, pH and recirculation ratio are 38.9 °C, 7.03 and 1.89 respectively which could enhance the biogas production and methane yield by 19.4% and 12.2% respectively. Confirmatory experiments were carried out in the biogas plant under this set of optimised variables for a period of two months. The predicted biogas production and methane yield are highly correlated to the actual data with small percentage difference of 1.25% and 5.09% respectively, indicating that ANFIS model was accurate and reliable. Sensitivity analysis shows that pH has the most dominant effect on the methane yield. Graphical abstract: Image 1 Highlights: Various machine learning models have been applied for prediction and optimisation. ANFIS yields the highest prediction accuracy. Biogas production and methane yield are enhanced by 19.4% and 12.2% respectively.