Predictive Modelling of H2S Removal from Biogas Generated from Palm Oil Mill Effluent (POME) Using Biological Scrubber in an Industrial Biogas Plant: Integration of Artificial Neural Network (ANN) and Process Simulation

Clifford, Joanna Lisa; Chan, Yi Jing; Yusof, Mohd Amran Bin Mohd; Tiong, Timm Joyce; Lim, Siew Shee; Lee, Chai Siah; Tong, Woei-Yenn

Abstract

Research background. Biogas production from Palm Oil Mill Effluent (POME) is inherently unstable due to variations in feedstock composition and operating conditions. These fluctuations can result in inconsistent biogas quality, variable methane content, and fluctuating levels of hydrogen sulphide (H2S), posing significant challenges for bioscrubbers in removing H2S to meet the quality standards for gas engines used in electricity generation. This research aims to address these challenges by integrating simulation models using a computer programme and Artificial Neural Network (ANN) to predict the performance of a bioscrubber at a POME treatment plant in Johor, Malaysia.

Experimental approach. Initially, the process flowsheet model was simulated using a computer programme. The prediction of H2S removal was then conducted using a machine learning algorithm, specifically ANN, based on two years of historical data obtained from the biogas plant. Furthermore, a detailed techno-economic analysis was conducted to determine the economic feasibility of the process.

Results and conclusions. Simulation results revealed a biogas yield of 26.12 Nm3 of biogas per m3 of POME, aligning with industry data with less than 1 % deviation. The ANN model achieved a high coefficient of determination (R2) of 0.9 and a low mean squared error (MSE), with the bioscrubber reaching approximately 96 % H2S removal efficiency. The techno-economic analysis indicated that the process is feasible, with a net present value of $131,000 and a payback period of 7 years.

Novelty and scientific contribution. The integration of ANN and the computer programme provides a robust framework for predicting bioscrubber performance and ensuring stable bioscrubber operation due to their complementary strengths. ANN accurately predicts H2S removal using daily recorded data, while the computer programme estimates parameters not monitored daily, such as chemical oxygen demand (COD), biological oxygen demand (BOD), and total suspended solids (TSS). This research provides valuable insights into sustainable biogas production practices, offering opportunities to improve energy efficiency and environmental sustainability in the palm oil industry.

Citation

Clifford, J. L., Chan, Y. J., Yusof, M. A. B. M., Tiong, T. J., Lim, S. S., Lee, C. S., & Tong, W.-Y. (2025). Predictive Modelling of H2S Removal from Biogas Generated from Palm Oil Mill Effluent (POME) Using Biological Scrubber in an Industrial Biogas Plant: Integration of Artificial Neural Network (ANN) and Process Simulation. Food Technology and Biotechnology, 63(2), https://doi.org/10.17113/ftb.63.02.25.8792

• Journal Article Type: Article
• Acceptance Date: Mar 31, 2025
• Online Publication Date: Apr 20, 2025
• Publication Date: 2025-04
• Deposit Date: Apr 21, 2025
• Publicly Available Date: Apr 22, 2025
• Journal: Food Technology and Biotechnology
• Print ISSN: 1334-2606
• Publisher: Sveuciliste u Zagrebu, Prehrambeno-Biotehnoloski Fakultet
• Peer Reviewed: Peer Reviewed
• Volume: 63
• Issue: 2
• DOI: https://doi.org/10.17113/ftb.63.02.25.8792
• Keywords: palm oil mill effluent; biogas; simulation; Artificial Neural Network; bioscrubber
• Public URL: https://nottingham-repository.worktribe.com/output/48086574
• Publisher URL: https://www.ftb.com.hr/archives/1936-predictive-modelling-of-h2s-removal-from-biogas-generated-from-palm-oil-mill-effluent-pome-using-biological-scrubber-in-an-industrial-biogas-plant-integration-of-artificial-neural-network-ann-and-process-simulation