Abstract
Palm oil mill effluent (POME) contributes to 23.7% of the methane emissions in Malaysia. Development of a methane emission prediction tool by using machine learning (ML) enables the estimated volume of methane released to be determined. In this study, Gaussian Process Regression (GPR) along with its respective kernels was explored for the development of the prediction tool. Synthetic minority oversampling technique (SMOTE) was also implemented to study the effect of the training sample size used on the model validation. The GPR model was trained using synthetic data created from SMOTE, while the measure data from the plant was used to test the reliability of the trained model. The application of SMOTE was capable in producing high model validation performance (R2 = 0.98, RMSE = 0.133, MSE = 0.018 and MAE = 0.08) using the common squared exponential kernel GPR model. However, the Matern 5/2 and rational quadratic kernel GPR model had the best model validation performance (R2 = 0.98, RMSE = 0.131, MSE = 0.017 and MAE = 0.083). In terms of model testing performance, rational quadratic kernel had the best performance with R2 = 0.99, RMSE = 0.061, MSE = 0.0037 and MAE = 0.044. The results of this study indicate the prediction tool developed using SMOTE-based rational quadratic kernel GPR model can predict methane emissions with high accuracy. The methane emissions prediction tool developed is an alternative cost friendly and reliable option to existing methods.
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Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- POME:
-
Palm oil mill effluent
- ML:
-
Machine learning
- MCS:
-
Methane capture system
- GRP:
-
Gaussian Process Regression
- SMOTE:
-
Synthetic minority oversampling technique
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Hia, H.Y., Selvanathan, K., Ragu, K. et al. Development of a Methane Emission Prediction Tool (POMEP178) for Palm Oil Mill Effluent Using Gaussian Process Regression. Process Integr Optim Sustain 7, 921–930 (2023). https://doi.org/10.1007/s41660-023-00331-0
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DOI: https://doi.org/10.1007/s41660-023-00331-0