This Research Topic will explore the application of AI-based techniques in the field of sustainable energy and chemicals. Our Topic uncovers the power of harnessing datasets to revolutionize the way we understand and optimize feedstock properties and processing, and fuel/product correlations in renewable applications.

Artificial Intelligence allows scientists to unlock the potential of experimental and theoretical datasets rich with information on diverse feedstocks, such as biomass or waste plastics, and processing conditions. These researchers also leverage algorithms to discern non-intuitive correlations, allowing the prediction and optimization of feedstock properties or processes for sustainable energy and chemicals production. Analyzing these correlations can result in the development of advanced models that enable precise fuel property predictions necessary for optimizing renewable energy systems.

The application of machine learning and deep learning techniques to improve the speed, breadth, and depth of insights that researchers can generate from biomass feedstock, processing, and products data is still nascent. As of December 2023, a Web of Science search using the title of this Research Topic resulted in only 21 publications. Dropping “deep learning” as a key word resulted in 36 titles, with 5 highly cited papers and 17 reviews, and recent publication years, ranging from 2021 to 2023. This research area has much promise, attracting interest from both domain science and data science experts. It is the aim of this Research Topic to solicit high quality scientific publications to further seed and contribute to the growth of this promising and exciting field.

Our goal is to develop a collection highlighting the span of the nascent and current research in sustainable energy and chemicals production from biomass using the power of machine learning and deep learning.

Types of accepted manuscripts include Reviews and Original Research. Specific themes that potential contributors can address may include, but are not limited to:
● Data considerations in sustainability, bioenergy, and biochemical machine learning/deep learning applications
● Use of machine learning techniques, such as classification, regression, and optimization to predict relevant process conditions and parameters (e.g. yield and purity)
● Identification of catalysts and process parameters from datasets to efficiently convert diverse feedstocks, such as biomass or waste plastics
● Explainability of ML/DL techniques applied to processing

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Artificial Intelligence/Machine Learning Applications for Sustainable Energy, Biofuels, and Chemicals