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; Suprihadi Prasetyono; Catur Suko Sarwono; Digdo Listyadi Setyawan; Azmi Saleh; Bambang Sri Kaloko; Muhammad Naufal An Nafi

doi:10.55981/j.mev.2026.1352

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

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.

10.55981/j.mev.2026.1352

Keywords

biogas monitoring; internet of things; gas purification; methane detection; CO2 and H2S detection; real-time monitoring

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