Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. We discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.
This article was originally published in Reaction Chemistry and Engineering. The version of record is available at: This article will be embargoed until 01/25/2023.
Batchu, Sai Praneet, Borja Hernandez, Abhinav Malhotra, Hui Fang, Marianthi Ierapetritou, and Dionisios G. Vlachos. “Accelerating Manufacturing for Biomass Conversion via Integrated Process and Bench Digitalization: A Perspective.” Reaction Chemistry & Engineering, January 25, 2022.