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Optimization and Monitoring of Biogas Power Plants for Energy Management Using Genetic Algorithms and the Internet of Things (Case Study: Kendari City Slaughterhouse)
Abstract
This project seeks to build and optimize a Biogas Power Plant system connected with Internet of Things (IoT) technology and Genetic Algorithms (GA) for energy management purposes. The system is executed as a simulation at the Kendari City Slaughterhouse (RPH), employing organic waste as biogas feedstock. IoT-based monitoring is utilized to track essential data, such as methane concentration, temperature, pressure, voltage, and current, in real time. Genetic Algorithms are utilized to optimize load distribution, guaranteeing efficient energy consumption. The findings demonstrate that the monitoring system operates dependably, exhibiting great sensor precision, with voltage readings attaining an average accuracy of 98.91% and pressure sensors achieving 99.1% accuracy. Additionally, GA optimization effectively identifies an appropriate load supply combination that aligns closely with the generator capacity, avoiding overload problems. The integration of IoT and GA augments the performance, efficiency, and reliability of the PLTBG system, exhibiting its capacity for sustainable energy management in biogas-powered generation.
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DOI: https://doi.org/10.17509/coelite.v5i1.98498
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