Sustainable Predictive Maintenance for Biomass Energy Plants Using IoT and Decision Trees

Predictive maintenance in biomass energy facilities is revolutionized by combining modern data analytics and Internet of Things (IoT) technologies. This paper's innovative approach for sustainable predictive maintenance uses Internet of Things sensors to gather operational data from biomass systems in real-time. The system evaluates this data using decision tree algorithms to forecast equipment failures and optimize maintenance plans, increasing operational effectiveness and decreasing downtime. The research illustrates how, by providing equipment life and optimum performance, this strategy reduces maintenance costs and advances the sustainability objectives of biomass energy generation. Case studies show how the proposed method was successfully implemented, highlighting notable gains in resource use, maintenance procedures, and overall plant performance. According to the research, using IoT and machine learning methods in predictive maintenance may result in more sustainable biomass energy operations, eventually aiding in shifting to more environmentally friendly energy sources.