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
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 H<sub>2</sub>S 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.
Authors
- Siew Shee Lim (ORCID: https://orcid.org/0000-0002-1798-2683)
- Woei Yenn Tong (ORCID: https://orcid.org/0000-0002-8697-6885)
- Yi Jing Chan (ORCID: https://orcid.org/0000-0003-2123-2356)
- Chai Siah Lee (ORCID: https://orcid.org/0000-0002-7688-4469)
- Timm Joyce Tiong (ORCID: https://orcid.org/0000-0003-1137-3606)
- Jantina Clifford (ORCID: https://orcid.org/0000-0003-2346-0758)
- Mohd Amran Bin Mohd Yusof
Institutions
- University of Nottingham (GB)
- University of Nottingham Malaysia Campus (MY)
- University of Kuala Lumpur (MY)
Publication Details
- Journal
- Food Technology and Biotechnology
- Published
- 2025-04-20
- DOI
- https://doi.org/10.17113/ftb.63.02.25.8792
- Primary Topic
- Industrial Gas Emission Control
- Type
- article
- Field-Weighted Citation Impact
- 0.00