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

Angelique Mukasine (), Louis Sibomana, Kayalvizhi Jayavel, Kizito Nkurikiyeyezu and Eric Hitimana
Additional contact information
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
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

Future Internet, 2023, vol. 15, issue 8, 1-14

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.

Keywords: biogas energy; Internet of things; regression modeling; correlation analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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