@inproceedings{01d98e75a8b34fb9ad8631f04abf92d4,
title = "Artificial intelligence-based IoT-enabled biogas production",
abstract = "This study explored the use of artificial intelligence (AI) and the Internet of Things (IoT) to boost biogas production from organic waste using anaerobic digesters. An artificial neural network (ANN) genetic algorithm (GA) model predicted biogas yields and optimized production. Sensors and IoT technologies monitored factors affecting biogas production, achieving an efficiency of 78.2%. The ANN GA model showed strong accuracy, with a correlation coefficient of 0.85. AI and IoT can significantly increase biogas yield and efficiency, though challenges and limitations exist. This research highlights the potential of these technologies in biogas production.",
keywords = "Artificial intelligence of things, artificial neural network, biogas production, internet of things",
author = "Peter Onu and Charles Mbohwa and Anup Pradhan",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023 ; Conference date: 10-05-2023 Through 12-05-2023",
year = "2023",
doi = "10.1109/ICCAD57653.2023.10152349",
language = "English",
series = "2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023",
address = "United States",
}