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Feed-forward ANN and traditional machine learning-based prediction of biogas generation rate from meteorological and organic waste parameters

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Abstract

This study presents a comprehensive investigation into the prediction of biogas production (BP) using meteorological parameters and organic waste (OW) data through feed-forward artificial neural network (ANN) and traditional machine learning (ML) techniques. BP is a crucial renewable energy source, derived from the anaerobic digestion of OW, and its efficient prediction holds immense importance for sustainable waste management and energy production. The dataset used in the study comprised approximately 728 sample points, encompassing records of meteorological parameters, OW, and biogas yields for the period 2015–2021. Additionally, autoregressive integrated moving average (ARIMA) models have been employed to enhance long-term forecasting of BP. The results of the model validation were highly promising. The MLP model demonstrated remarkable performance with a root mean-squared error (RMSE) of 2.76 and an R-squared (R2) value of 0.94, indicating its accuracy in predicting BP rates. Moreover, for forecasting future data, the ARIMA-MLP model outperformed others, yielding a standard error of 5.1152 and a biogas yield of approximately 90 m3. These findings underscore the limitations of traditional empirical approaches and highlight the potential of ML algorithms as effective reconstruction techniques. By develo** robust and accurate region-specific BP rate models, these ML methods offer valuable support in fostering integrated circular agricultural systems for a sustainable global future. The integration of ML in waste-to-energy processes can play a significant role in addressing environmental challenges, advancements in sustainable waste management and clean energy production.

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Data availability

The datasets used and analysed in this study are mentioned in the references section, and the rest of the predicted datasets will be made available on reasonable request.

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Acknowledgements

The authors thankfully acknowledge the Centre for the Environment, Indian Institute of Technology Guwahati, India and Central Pollution Control Board (CPCB) for providing the data.

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  1. Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    Tinka Singh

  2. Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

    Ramagopal V. S. Uppaluri

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TS contributed to data extraction, data preprocessing, investigation, modelling, writing–original draft preparation, writing–review and editing, and validation. RVSU contributed to conceptualization, supervision, validation, visualization, and writing–review and editing.

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Correspondence to Ramagopal V. S. Uppaluri.

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Singh, T., Uppaluri, R.V.S. Feed-forward ANN and traditional machine learning-based prediction of biogas generation rate from meteorological and organic waste parameters. J Supercomput 80, 2538–2571 (2024). https://doi.org/10.1007/s11227-023-05569-6

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