Track: Waste

Abstract

The increasing global demand for renewable energy and the pressing need for environmental sustainability have led to a growing interest in bioenergy production as a viable solution. Concurrently, advancements in machine learning (ML) techniques have opened up new opportunities for optimizing processes and improving efficiency across various sectors. Recognizing the potential of integrating ML with bioenergy, this study aims to explore the synergistic applications of these technologies to enhance environmental sustainability. Through a comprehensive assessment, which includes a systematic review and case study analysis, this research investigates the potential of ML algorithms and models in optimizing bioenergy processes and bioproduct production. By leveraging ML techniques, bioenergy processes can be refined and fine-tuned, leading to increased efficiency, reduced waste, and enhanced environmental performance. The systematic review component of this study critically examines existing literature on the integration of ML and bioenergy, identifying key trends, challenges, and opportunities. The case study component of this study provides a detailed analysis of the integration of ML and bioenergy in one case: the production of biomethane from municipal solid waste (MSW) in Mukono District, Uganda. Ultimately, this study's findings will provide valuable insights and recommendations for policymakers, researchers, and industry stakeholders. The insights gained will help harness the full potential of ML in advancing the field of bioenergy, while simultaneously achieving sustainable energy production. By identifying critical factors influencing the successful integration of ML and bioenergy, this research contributes to the broader objective of addressing environmental concerns and ensuring a sustainable future.