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Correlation Analysis Model of Environment Parameters Using IoT Framework in a Biogas Energy Generation Context

Author

Listed:
  • Angelique Mukasine

    (African Center of Excellence in the Internet of Things, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

  • Louis Sibomana

    (National Council for Science and Technology, Kigali P.O. Box 2285, Rwanda)

  • Kayalvizhi Jayavel

    (Department of Information Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India
    Current address: Creative Computing Institute, University of Arts London, London SE5 8UF, UK.)

  • Kizito Nkurikiyeyezu

    (Department of Electrical and Electronics Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

  • Eric Hitimana

    (African Center of Excellence in the Internet of Things, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

Abstract

Recently, the significance and demand for biogas energy has dramatically increased. However, biogas operators lack automated and intelligent mechanisms to produce optimization. The Internet of Things (IoT) and Machine Learning (ML) have become key enablers for the real-time monitoring of biogas production environments. This paper aimed to implement an IoT framework to gather environmental parameters for biogas generation. In addition, data analysis was performed to assess the effect of environmental parameters on biogas production. The edge-based computing architecture was designed comprising sensors, microcontrollers, actuators, and data acquired for the cloud Mongo database via MQTT protocol. Data were captured at a home digester on a time-series basis for 30 days. Further, Pearson distribution and multiple linear regression models were explored to evaluate environmental parameter effects on biogas production. The constructed regression model was evaluated using R 2 metrics, and this was found to be 73.4% of the variability. From a correlation perspective, the experimental result shows a strong correlation of biogas production with an indoor temperature of 0.78 and a pH of 0.6. On the other hand, outdoor temperature presented a moderated correlation of 0.4. This implies that the model had a relatively good fit and could effectively predict the biogas production process.

Suggested Citation

  • Angelique Mukasine & Louis Sibomana & Kayalvizhi Jayavel & Kizito Nkurikiyeyezu & Eric Hitimana, 2023. "Correlation Analysis Model of Environment Parameters Using IoT Framework in a Biogas Energy Generation Context," Future Internet, MDPI, vol. 15(8), pages 1-14, August.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:265-:d:1213585
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    References listed on IDEAS

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    1. Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    2. Panigrahi, Sagarika & Dubey, Brajesh K., 2019. "A critical review on operating parameters and strategies to improve the biogas yield from anaerobic digestion of organic fraction of municipal solid waste," Renewable Energy, Elsevier, vol. 143(C), pages 779-797.
    3. Hamid, R.G. & Blanchard, R.E., 2018. "An assessment of biogas as a domestic energy source in rural Kenya: Developing a sustainable business model," Renewable Energy, Elsevier, vol. 121(C), pages 368-376.
    4. Namahoro, Jean Pierre & Wu, Qiaosheng & Xiao, Haijun & Zhou, Na, 2021. "The asymmetric nexus of renewable energy consumption and economic growth: New evidence from Rwanda," Renewable Energy, Elsevier, vol. 174(C), pages 336-346.
    5. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    6. Sarker, Swati Anindita & Wang, Shouyang & Adnan, K.M. Mehedi & Sattar, M. Nahid, 2020. "Economic feasibility and determinants of biogas technology adoption: Evidence from Bangladesh," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
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    Cited by:

    1. Angelique Mukasine & Louis Sibomana & Kayalvizhi Jayavel & Kizito Nkurikiyeyezu & Eric Hitimana, 2024. "Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach," Energies, MDPI, vol. 17(2), pages 1-13, January.

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