Applicability of genetic algorithm in biochar combustion kinetics with double distributed activation energy model

In this study, the genetic algorithm, a stochastic global optimization method, was used to investigate complex reaction kinetics. The genetic algorithm’s effectiveness and efficiency were validated through investigating a conventional optimization problem and a theoretically simulated chemical reaction process. The combustion kinetics of biochar derived pinewood sawdust pyrolysis was experimentally investigated, and a distributed activation energy model (DAEM) with a double distribution was utilized to analyze the kinetic behaviors of biochar combustion, and the genetic algorithm was employed to optimize the model parameters. For biochar combustion, two overlapping sub-processes with different activation energy distributions were revealed by the double DAEM: 160–200 kJ mol⁻¹ (peaked at 182.47 kJ mol⁻¹) for the first sub-process and 165–235 kJ mol⁻¹ (peaked at 199.96 kJ mol⁻¹) for the second sub-process. The DAEM with the genetic algorithm for the estimation of model parameters provides a powerful tool for analyzing the thermal decomposition kinetics of complex solid materials.