Korean and Chinese researchers analyse biomass chemical looping
In South Korea, a research team led Korea University and Southeast University, China, conducted a comprehensive review and analyzed biomass chemical looping (BCL) as a sustainable pathway for energy and chemical production. Their study, titled “Biomass chemical looping: A sustainable pathway for energy and chemicals,” was published in the Journal of Energy Chemistry on June 1, 2026.
BCL employs solid oxygen carriers to transfer oxygen between reactors, enabling controlled reduction–oxidation reactions without direct contact between air and fuel. This distinctive process configuration improves energy efficiency, facilitates carbon management, and reduces the need for energy-intensive gas separation. The researchers examined a broad range of BCL routes, including chemical looping gasification, chemical looping combustion, chemical looping reforming, chemical looping hydrogen production, and syngas tailoring for downstream chemical synthesis. Together, these approaches demonstrate the versatility of BCL as a platform for generating renewable energy and value-added products.
A major focus of the review is the potential of BCL for producing hydrogen and methanol. BCL hydrogen production offers a promising pathway for generating renewable hydrogen while improving carbon conversion and process integration. When coupled with methanol synthesis, this route could provide low-carbon feedstocks for the chemical industry and support the transition toward more sustainable energy systems. The review highlights BCL-to-hydrogen pathways for green methanol production as particularly attractive because of their environmental and economic advantages.
The researchers also identify oxygen carrier design as a critical factor determining the success of BCL technologies. Effective oxygen carriers must possess high oxygen transfer capacity, strong redox stability, resistance to carbon deposition, mechanical durability, and cost-effectiveness. Traditional development approaches often rely on time-consuming trial-and-error experimentation. To overcome these limitations, the review highlights machine learning as a powerful tool for accelerating oxygen carrier discovery and optimization.
Category: Research
