Biomass valorisations in efficient and sustainable ways are widely considered as practical approaches for simultaneously mitigating climate change and strengthening energy security. Compared to conventional thermochemical and biochemical biomass valorisation approaches, biomass chemical looping (BCL) has attracted attention, owing to its advantages of high energy efficiency, controllable main products, and high-purity CO 2 separation by a simple condensation unit. In this review, we discuss BCL technical routes in terms of targeted products and practical applications, providing underlying mechanisms and optimization methodologies to reduce energy consumption and improve conversion efficiency with high-purity desired products. As one typical data-driven approach, machine learning (ML) is an emerging approach for accurate design of oxygen carriers and efficient optimisation of reduction-oxidation cycles, significantly enhancing large-scale applications of BCL technologies. Moreover, we address environmental impacts and economic feasibility of typical BCL approaches for providing comprehensive guidelines to researchers, industrial practitioners, and policymakers. Owing to the excellent environmental and economic benefits of BCL-to-H 2 for green methanol production, we highlight its potential as a multifunctional alternative to conventional power generation and biowaste management technologies. With concerted efforts, sustainable BCL for value-added products such as green electricity, hydrogen, and methanol is beneficial to achieving UN SDGs 7, 12, and 13.
Zhao et al. (Mon,) studied this question.