Acta Phys. -Chim. Sin. ›› 2016, Vol. 32 ›› Issue (9): 2223-2231.doi: 10.3866/PKU.WHXB201607152

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Method for Optimizing the Kinetic Parameters for the Thermal Degradation of Forest Fuels Based on a Hybrid Genetic Algorithm

Hui-Chang NIU1,2,*(),Dan JI2,Nai-An LIU1   

  1. 1 State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, P. R. China
    2 Research Center of Urban Public Safety Technology, Institute of Industrial Technology Guangzhou & Chinese Academy of Sciences, Guangzhou 511458, P. R. China
  • Received:2016-03-23 Published:2016-09-08
  • Contact: Hui-Chang NIU
  • Supported by:
    the National Natural Science Foundation of China(51476156);Project of Science and Technology Planning of Guangdong Province, China(B010118001);Project of Science and Technology Planning of Guangdong Province, China(2014B010125003);the Open Project of State Key Laboratory of Fire Science, University of Science and Technology of China(HZ2015-KF10)


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 survivalof-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.

Key words: Hybrid genetic algorithm, Nonlinear fitting, Forest fuel, Thermal degradation, Kinetics


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