https://doi.org/10.17113/ftb.63.02.25.8792

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

Joanna Lisa Clifford¹, Yi Jing Chan^1*, Mohd Amran Bin Mohd Yusof¹, Timm Joyce Tiong¹, Siew Shee Lim¹, Chai Siah Lee² and Woei-Yenn Tong³

¹Department of Chemical and Environmental Engineering, University of Nottingham Malaysia, Broga Road, 43500, Semenyih, Selangor Darul Ehsan, Malaysia

²Advanced Materials Research Group, Faculty of Engineering, University of Nottingham, NG7 2RD, UK

³Universiti Kuala Lumpur, Institute of Medical Science Technology, A1-1, Jalan TKS 1, Taman Kajang Sentral, 43000 Kajang, Selangor, Malaysia

Copyright © 2025 This is a Diamond Open Access article published under CC-BY licence. Copyright remains with the authors, who grant third parties the unrestricted right to use, copy, distribute and reproduce the article as long as the original author(s) and source are acknowledged.

Article history:

Received: 24 July 2024

Accepted: 31 March 2025

Keywords:

palm oil mill effluent; biogas; simulation; artificial neural network; bioscrubber

The content of this publication has not been approved by the United Nations and does not reflect the views of the United Nations or its officials or Member States.

Summary:

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 lead to inconsistent biogas quality, variable methane content and fluctuating hydrogen sulphide (H2S) concentration. This poses significant challenges for bioscrubbers in removing H₂S to meet quality standards for gas engines used for electricity generation. This research aims to address these challenges by integrating simulation models with 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. First, the process flowsheet model was simulated using a computer programme. The H₂S removal was then predicted using a machine learning algorithm, specifically ANN, based on two years of historical data obtained from the biogas plant. A detailed techno-economic analysis was also carried out to determine the economic feasibility of the process.

Results and conclusions. The simulation results showed a biogas yield of 26.12 Nm3 pervm3 POME, which is in line 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 an H₂S removal efficiency of approx. 96 %. The techno-economic analysis showed that the process is feasible with a net present value (NPV) 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 based on daily recorded data, while the computer programme estimates parameters that are 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 and offers opportunities to improve energy efficiency and environmental sustainability in the palm oil industry.