Optimizing Smart Methane Farms: Intelligent Waste Sorting for Maximum Biogas Yield through Naive Bayes and IoT Integration
E. Sivanantham, R. Vijayakumar, Parthiban Veda, A. Nithya, P Vedasundara Vinayagam, S. Renukadevi · 2024
This research presents a innovative approach to smart methane farm biogas production enhancement via the use of Internet of Things (IoT) technology and intelligent waste sorting methods. This study sorts trash into categories according to its biogas production potential using the Naive Bayes method. IoT devices are used to track the make-up of trash in real-time. After that, the information is sent to a main system where it will be thoroughly analyzed. For optimal feeding of organic materials into anaerobic digesters, the intelligent waste sorting system makes use of Naive Bayes insights and the constant data flow from IoT devices. This strategy is designed to maximize the production of biogas by ensuring a personalized and responsive process. One novel approach to improving methane farms’ efficiency is the complementary use of Naive Bayes and IoT technology. By making better use of resources and reducing environmental effects, the suggested solution does double duty: improving biogas production and adding to sustainable practices. There is a rising need for environmentally conscious and economically viable solutions in the energy and agricultural industries, and this study provides a futuristic framework that fits the bill.