Greenhouse gas (GHG) emissions are one of the leading environmental concerns faced nowadays. The chemical looping combustion (CLC) process is one of the main processes that aim for carbon capture, utilization, and storage (CCUS), allowing the generation of a high-purity CO2 stream that can be easily captured. Brazil has a wide variety of biomasses that could be applied to CLC, and the behavior of these biomasses can be predicted using machine learning algorithms. An artificial neural network (ANN) was created considering the biomass characteristics (proximate and ultimate analysis) and fuel reactor temperature as input data to assess their influence on CLC performance parameters (carbon capture efficiency, ηCC, and total oxygen demand, ΩT) and gas compositions. The characteristics of five Brazilian biomasses were considered in the constructed ANN to predict their behavior on CLC performance. The ANN presented a good data fit, with R2 achieving values higher than 0.973. Volatile matter played a crucial role in predicting the CLC performance parameters. Rice husks presented the smoothest results for ηCC and ΩT, while the CO2 composition was most affected by the eucalyptus characteristics. Experimental tests with all the biomasses should be carried out to provide a higher prediction capability of the algorithm.
Oliveira et al. (2026) studied this question.