<![CDATA[Waste management has become an urgent issue in many regions, prompting the need for sustainable solutions in its treatment. Waste-to-Energy (WtE) power plants have emerged as a promising option, but manual control issues within them have raised some serious challenges. Temperature variations in the boiler pose a threat to efficiency and productivity, resulting in significant economic losses and adverse environmental impacts. To address this problem, this research aims to apply Machine Learning to optimize boiler temperature control. Operational data is collected and used to train a Machine Learning model capable of accurately predicting temperature. This model is then implemented into the boiler control system. The research results indicate a significant improvement in temperature stability and a reduction in operational costs. The utilization of Machine Learning technology has paved the way for more efficient and sustainable operations. With the successful implementation of Machine Learning in boiler control, this research emphasizes the crucial role of advanced technology in the development of sustainable energy industries. In conclusion, the application of Machine Learning can provide an effective solution to temperature control issues, significantly optimizing efficiency and productivity. Therefore, this research makes a valuable contribution to more efficient waste management, has a positive impact on the environment, and supports the achievement of cleaner and more sustainable renewable energy goals.]]>
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