Industrial-scale anaerobic Co-digestion (ACoD) of palm oil mill effluent (POME) and decanter cake (DC) for maximizing methane yield: An integrated machine learning and simulation-based economic analysis approach

Yee Theng Jessy Hoon, Yi Jing Chan, Yoke Kin Wan, Yong Kheng Goh and Sara Kazemi Yazdi

Energy, 2024, vol. 289, issue C

Abstract: This study employs machine learning (ML) algorithms, including multiple linear regression (MLR), decision tree (DT) and support vector regression (SVR) to predict the methane yield from the anaerobic co-digestion (ACoD) of palm oil mill effluent (POME) and decanter cake (DC) in an industry-scale anaerobic covered lagoon. Results showed that the DT model outperformed the other models with a high R2 value of 0.9763 and the lowest MAE, MSE, and RMSE values. It exhibited over 95 % similarity to the actual results, validating its effectiveness in capturing real-world scenarios. Optimisation was conducted using response surface methodology (RSM) to achieve maximum biogas production (14,245 m3/day) and methane yield (0.285 Nm3 CH4/kg CODremoved), where the optimal range of pH, organic loading rate (OLR) and dilution ratio of DC were found to be 6.83–6.94, 0.82–0.83 kg COD/m3. day and 0.09–0.11 respectively. The ACoD process was simulated, and an economic analysis was performed using SuperPro Designer v10.6. ACoD of POME and DC was more economically viable than mono-digestion of POME, with a 43.16 % improvement in return on investment (ROI), considering the trade-off between the additional cost of pre-treating DC and the additional revenue from improved biogas production using ACoD.

Keywords: Biogas production; Methane yield; Multiple linear regression (MLR); Decision tree (DT); Support vector regression (SVR) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:289:y:2024:i:c:s0360544223033339

DOI: 10.1016/j.energy.2023.129939

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