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Feature Importance Study for Biogas Production from POME Treatment Plants Using Out-of-Bag Permutation

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Cutting Edge Applications of Computational Intelligence Tools and Techniques

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1118))

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Abstract

Methane capturing systems (MCS) for electricity generation in the palm oil mill effluent (POME) treatment process are emphasized to reduce methane emissions. However, the presence of carbon dioxide and hydrogen sulfide in POME biogas affect the heating quality of the biogas. Therefore, proper understanding on the parameters which could affect the emission of these impurities is necessary to curb their production. Due to limited available data, Synthetic Minority Oversampling Technique (SMOTE) was applied to expand the dataset for training purposes. In this study, a random forest based out-of-bag permutation feature importance study was conducted by assessing the influence of temperature, pH and organic loading rate (OLR), chemical oxygen demand (COD), total solids (TS), biological oxygen demand (BOD), suspended solids (SS), and hydraulic retention time (HRT) on methane, carbon dioxide and hydrogen sulfide emission. Temperature, pH and organic loading rate were found to be the most influential parameters for methane and carbon dioxide production, while pH was replaced by suspended solids the case of hydrogen sulfide. The final random forest machine learning model generated performance metrics for R2 and RMSE with values of 0.98 and 0.131 and 0.99 and 0.061, respectively.

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Acknowledgements

We are grateful to the University of Nottingham Malaysia for providing us with the software support required to conduct this research. We would also like to show appreciation for the engineers and managers of Lepar Hilir Palm Oil Mill, Adela Palm Oil Mill, Keratong Estate Oil Palm Mill and Felda Lok Heng Palm Oil Mill for providing the dataset required for this study.

Funding

This research received no external funding.

Data Availability Statement

Data will be made available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Authors and Affiliations

  1. Department of Chemical and Environmental Engineering, University of Nottingham, Semenyih, Malaysia

    Kishaan Ragu, Ong Qian Yee, Kiew Xin Yun, Hia Hung Yi, Kashwin Selvanathan, Sara Kazemi Yazdi, Chan Yi Jing & Reza Godary

  2. Department of Computer Science, University of Nottingham, Semenyih, Malaysia

    Chen Zhi Yuan

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    Kevin Daimi

  2. Asia Pacific International College (APIC), Sydney, NSW, Australia

    Abeer Alsadoon

  3. Polytechnic Institute of Porto, Porto, Portugal

    Luis Coelho

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Ragu, K. et al. (2023). Feature Importance Study for Biogas Production from POME Treatment Plants Using Out-of-Bag Permutation. In: Daimi, K., Alsadoon, A., Coelho, L. (eds) Cutting Edge Applications of Computational Intelligence Tools and Techniques. Studies in Computational Intelligence, vol 1118. Springer, Cham. https://doi.org/10.1007/978-3-031-44127-1_7

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