Biomass to energy: A machine learning model for optimum gasification pathways

Abstract

Biomass is a highly versatile renewable resource for decarbonizing energy systems. Gasification is a promising conversion technology that can transform biomass into multiple energy carriers to produce heat, electricity, biofuels, or chemicals. At present, identifying the best gasification route for a given biomass relies on trial and error, time-consuming experimentation that, given the wide range of biomass feedstocks available, slows down the deployment of the technology. Here we develop a supervised machine-learning model to find the optimal application of a particular biomass in gasification processes. Our model can select the suitable gasification pathway from the characteristics of the biomass, and also identify the optimal operating conditions for a selected application of the produced gas. In addition, with this model, we can obtain insights into the relationships between biomass properties and gasification results, leading to a better understanding of the process. A relevant aspect of this work is that these results rely on a relatively small dataset, representative of those typically collected by research groups using different types of gasifiers worldwide. This study opens the path for future integration of such data, which would allow addressing the complexity of biomass and conversion process simultaneously. With this work, we aim to increase the flexibility of biomass gasification processes and promote the development of bioenergy technologies, considered crucial in the energy transition context.