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All Journal Mechatronics, Electrical Power, and Vehicular Technology
Muhammad Naufal An Nafi
Universitas Jember
Electrical & Electronics Engineering

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IoT-based monitoring system for biodigester production and purification Suprihadi Prasetyono; Catur Suko Sarwono; Digdo Listyadi Setyawan; Azmi Saleh; Bambang Sri Kaloko; Muhammad Naufal An Nafi
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 17, No 1 (2026): In Progress
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2026.1352

Abstract

Biogas is a promising renewable energy source, but its production and purification processes often lack real-time monitoring, leading to suboptimal yields and inconsistent gas quality. To address this gap, this study aimed to design a prototype for an integrated monitoring system based on the internet of things (IoT). The developed system facilitates the continuous observation of key parameters in both the anaerobic digestion and the subsequent purification stages. The prototype was constructed using an ESP32 microcontroller as the central processing unit, which collected data from a suite of sensors. These sensors measured critical process variables, including the digester's slurry temperature and pH, the volume of the produced gas in the gasholder, and the concentration of methane (CH₄) and hydrogen sulfide (H₂S) before and after the purification unit. Data were transmitted wirelessly via a Wi-Fi network to a cloud-based IoT platform, allowing for remote, real-time data visualization on a web dashboard. The results demonstrated that the prototype successfully captured and transmitted all parameter data with high reliability. The system provided a clear, real-time overview of the digester's operational stability and effectively quantified the increase in methane concentration and the reduction of impurities post-purification. Testing shows stable data transmission to Google Sheets and InfluxDB with minimal data loss. Delay times increase with distance in Google Sheets, from 3736.1 ms (2 m) to 3880.2 ms (8 m), while InfluxDB delay varies. RSSI values decrease with distance, with an accuracy range of 0.28 % to 5.11 %, peaking at 99.17 % accuracy at 6.05 meters.
Co-Authors Azmi Saleh Bambang Sri Kaloko Catur Suko Sarwono Digdo Listyadi, Digdo Suprihadi Prasetyono