Integrating Automation in Biomass Transformation: Opportunities, Challenges, and Future Directions

Abstract

The integration of automation, artificial intelligence (AI), and machine learning (ML) is revolutionizing the field of biomass transformation by enabling smarter, more efficient, and scalable processes. AI/ML have shown significant promise in enhancing processes such as biofuel production, anaerobic digestion, and waste-to-energy conversion by enabling predictive analytics, process control, and real-time monitoring. For instance, ML algorithms can predict optimal fermentation conditions for bioethanol production, while deep learning models can enhance enzyme selection for the breakdown of lignocellulosic biomass. Intelligent decision support systems (IDSS) are being applied to improve process efficiency in biogas plants by analyzing large datasets from sensor networks. Despite these advancements, critical challenges remain, including the need for laboratory automation, robust data infrastructure, a skilled workforce, and broader technology adoption. This review uniquely consolidates and analyzes the integration of AI/ML across a wide spectrum of biomass transformation processes, rather than focusing on isolated applications as seen in previous studies. This review presents a comprehensive overview of current developments, identifies existing limitations, and outlines future directions for researchers and practitioners aiming to drive innovation in this interdisciplinary field.