An integrated IoT and fuzzy logic controller system for biogas digester to predict methane generation

Ilangovan Pandian (), Sharmila Begum () and Srividhya Poosari Kumaravel ()
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Ilangovan Pandian: Periyar Maniammai Institute of Science and Technology
Sharmila Begum: Periyar Maniammai Institute of Science and Technology
Srividhya Poosari Kumaravel: Periyar Maniammai Institute of Science and Technology

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 9, No 82, 22517-22529

Abstract: Abstract In recent years the importance and demand of renewable energy has been increased drastically. The energy producers are facing ongoing challenges in executing biogas production due to lack of automation facilities and intelligent mechanisms to optimize the gas production. Our research work aims to design and develop Internet-of-things (IoT) controlled system coupled with a logic controller to monitor, predict and enhance biogas production thereby to overcome the current barriers and shortcomings of biogas production. The proposed architectural framework comprises three layers firstly the sensing layer followed by the predicting lawyer then the resolving layer. The sensing layer senses the data from the microcontroller coupled with temperature, pH, pressure and the methane sensor. In the predicting layer the sensed data are transported into the fuzzy domain by the fuzzification process. A rule-based inference engine predicts the appropriate consequences based on the antecedents. In the resolving layer, the predicted results are visualized to find the factors affecting the gas production and appropriate measures to be taken to improve the gas production. The experimental result of the proposed system accurately predicts the composition of methane to be generated for the loaded input feed which results to the prediction accuracy of 91% and error prediction percentage −9.51% which increases the stability of the digesters lifespan. Graphical abstract

Keywords: Optimization; Renewable energy; Framework; Fuzzification; Antecedents; Biogas prediction; Microcontroller (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10668-021-01943-7

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