Sustainable Process Intensification for Biomass Valorization

Jianping Li ()
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Jianping Li: University of Wisconsin-Madison

A chapter in Handbook of Smart Energy Systems, 2023, pp 3355-3373 from Springer

Abstract: Abstract Lignocellulosic biomass is a promising renewable energy source due to its abundant feedstock availability and the significant reduction of net greenhouse gas emissions compared with fossil fuels. Microbial conversion of biomass often leads to bioproducts that can serve as platform chemicals and fuels. Effective production and utilization of these bioproducts require new chemical processes with high product yields from biomass and effective separation of bioproducts to achieve the desired purity and recoveries. Significant efforts have been made in (1) exploiting enzymes and reaction solvents that promote conversions and (2) leveraging various separation agents including heat, work, and separation solvent to break the separation bottleneck. Process intensification is a promising technique to further reduce energy and material consumption, economic investment, and carbon emissions. Process intensification can be achieved by combining multiple reaction and separation phenomena, e.g., reaction/reaction, separation/separation, or reaction/separation. Challenges exist in the systematic exploration of intensification pathways that achieve optimal process targets. In this aspect, machine learning is a potential technique for the systematic exploration of process design space by connecting input as process flowsheet graph descriptors and output as design targets. In this chapter, we summarize the background and technical development for biomass valorization. Then, we introduce key concepts and methodologies of process intensification and applications of intensification methods for biomass valorization. We illustrate concepts and techniques using practical examples, including production of sustainable plastics and biofuels.

Keywords: Process intensification; Biomass valorization; Separation; Reaction; Machine learning; Cost-environment trade-off (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_170

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DOI: 10.1007/978-3-030-97940-9_170

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