Machine Learning for Process Optimization in Waste Biomass Conversion Technologies
Biomass waste is a valuable renewable energy resource, but its efficient conversion is hindered by feedstock variability, complex non-linear reactions, and the limited adaptability of conventional control systems. This chapter examines how machine learning improves biomass conversion through intelligent process monitoring, prediction, and optimization. It discusses supervised learning, reinforcement learning, Long Short-Term Memory (LSTM) networks, graph neural networks, and physics-informed machine learning, emphasizing their role in enhancing reactor performance and operational efficiency. Industrial case studies demonstrate measurable improvements, including a 15% reduction in gasification by-products, an 18% decrease in energy consumption during hydrothermal processing, and a 9% increase in ethanol yield during fermentation. This chapter concludes that machine learning is transforming biomass conversion into a more efficient, sustainable, and economically viable process by enabling intelligent, software-driven biorefineries.