H. C. Niu, D. Ji and N. A. Liu (2016) Method for Optimizing the Kinetic Parameters for the Thermal Degradation of Forest Fuels Based on a Hybrid Genetic Algorithm. Journal/Acta Physico-Chimica Sinica 32 2223-2231. [In Chinese]
Web link: http://dx.doi.org/10.3866/pku.whxb201607152
Keywords: Hybrid genetic algorithm; Nonlinear fitting; Forest fuel; Thermal; degradation; Kinetics; SMOLDERING COMBUSTION; DECOMPOSITION; PYROLYSIS; MODEL; THERMOGRAVIMETRY; PEAT; WOOD

Abstract: For thermal degradation of forest fuels, the optimization of kinetic parameters is a crucial step for the construction of comprehensive pyrolysis model. Traditional gradient-based optimization methods are characterized by strong converging speed, but with weak global optimization capability. The Darwinian survival of-the-fittest theory based genetic algorithm (GA) is a good tool for global optimization, but with weak converging speed because of the general principles of this algorithm. In this study we evaluated the dependence of the pure GA on the setting of the initial values (IVs), and found that the use of the correct initial values accelerated the converging speed and stabilized the results of the GA. A hybrid genetic algorithm (HGA) was used when the IVs were unknown. This algorithm shares the merits of iterative algorithms and GA. Thermogravimetric experiments were performed using the branches of Pinus Sylvestris and the results were used to compare the converging performances of GA and HGA under the assumption of a three-step, first-order pyrolysis model. The results of these analyses verified the validity and reliability of the HGA.