Applications of artificial intelligence-based modeling for bioenergy systems: A review

Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large-scale applications of biomass-based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy-related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end-use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end-uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy-related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

Automated summary

AI has been increasingly applied in bioenergy systems to address challenges related to feedstock variability, conversion economics, and supply chain reliability. The review of 164 articles published between 2005 and 2019 shows that AI techniques have unique capabilities in predicting biomass properties, process performance of biomass conversion, biofuel properties, and supply chain modeling and optimization. The future research should focus on developing standardized procedures for selecting AI techniques, enhancing data sharing, and exploring the potential of AI to support sustainable development of bioenergy systems.