Kinetic parameters and heat of reaction for forest fuels based on genetic algorithm optimization

被引:4
|
作者
Filho, G. C. Krieger [1 ]
Costa, Franklin [1 ]
Maria, G. F. Torraga [1 ]
Bufacchi, Paulo [1 ]
Trubachev, Stanislav [2 ,3 ]
Shundrina, Inna [4 ]
Korobeinichev, Oleg [2 ]
机构
[1] Univ Sao Paulo, Sao Paulo, Brazil
[2] Russian Acad Sci, Siberian Branch, Voevodsky Inst Chem Kinet & Combust, Novosibirsk, Russia
[3] Novosibirsk State Univ, Novosibirsk, Russia
[4] Russian Acad Sci, Siberian Branch, NN Vorozhtsov Novosibirsk Inst Organ Chem, Novosibirsk, Russia
基金
俄罗斯基础研究基金会;
关键词
Forest fuels; Kinetics parameters; Heat of reaction; TGA; DSC; Genetic Algorithm Optimization; SMOLDERING COMBUSTION; THERMAL-DEGRADATION; NUMERICAL-SIMULATION; FIRES; PEAT; THERMOGRAVIMETRY; PYROLYSIS; MOISTURE; BIOMASS; DENSITY;
D O I
10.1016/j.tca.2022.179228
中图分类号
O414.1 [热力学];
学科分类号
摘要
Environmental issues and climate change are playing a central role in our society. Forest and peatland fires can prejudice the environment and population health. Therefore, it is vital to study this phenomenon. The main goal of this work is to determine the kinetic parameters and heat of reaction for different forest fuels. Pine needles and sphagnum peat are typical fuels from the Northern Hemisphere, while Amazonian leaves and high-density peat are from the Southern Hemisphere. The DTG curves for pine needles and Amazonian leaves clearly show two peaks of mass loss, while for sphagnum peat a third mass-loss peak occurs for higher temperatures. For high density peat degradation, there is only one significant peak of mass loss, which happens at a higher temperature. Also, the DSC curves for pine needles, Amazonian leaves, and high-density peat show two exothermic peaks along with the mass-loss peaks. However, the overall pattern is not the same. The maximum heat released for pine needles and high-density peat occurs at the second degradation stage. There are two exothermic peaks of almost the same magnitude for Amazonian leaves. Finally, three exothermic peaks match the peaks of mass loss for sphagnum peat. DTG and DSC curves patterns for all forest fuels are independent of the heating rate and atmospheric composition. In this work, the proposed pseudo reaction mechanism for pyrolysis and oxidation of the forest fuels contains five (for Amazonian leaves, high-density peat, and sphagnum peat) and seven (for pine needle) steps. The genetic algorithm optimization process compares the instantaneous recorded data of TGA, DTG, and DSC with the calculated ones. The optimized kinetic reactions parameters for the forest fuels are the activation energy, the pre-exponential factor, the global order of reaction, the stoichiometric coefficients, and the heat of reaction. The overall performance of the proposed mechanism is evaluated taking the error or the experimental data into account. This set of reaction kinetics parameters allows for a suitable numerical model for forest fires. To the authors' knowledge, a comprehensive assessment of the above parameters, including the heat of reaction, for forest fuels is lacking.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Optimization Design and Calculation of Mooring System Parameters based on Genetic Algorithm
    Xia, Liang-yu
    Zhi, Li
    Chen, Ling
    Cheng, Yue
    4TH INTERNATIONAL CONFERENCE ON MECHATRONICS AND MECHANICAL ENGINEERING (ICMME 2017), 2018, 153
  • [32] Parameters optimization during filter design based on improved genetic algorithm
    Wang, XD
    PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION SCIENCE AND TECHNOLOGY, VOL 3, 2002, : 771 - 776
  • [33] Optimization of fracturing parameters for tight oil production based on genetic algorithm
    Dali Guo
    Yunwei Kang
    Zhiyong Wang
    Yunxiang Zhao
    Shuguang Li
    Petroleum, 2022, (02) : 252 - 263
  • [34] Optimization design based on genetic algorithm for designing parameters of PID controller
    Zhou, Kaitin
    Zheng, Lixin
    Huaqiao Daxue Xuebao/Journal of Huaqiao University, 2000, 21 (02): : 200 - 204
  • [35] Optimization of bridges' parameters based on bp neural network and genetic algorithm
    Xi, Hui-Feng
    Tang, Li-Qun
    He, Ting-Hui
    Huang, Xiao-Qing
    Zhongshan Daxue Xuebao/Acta Scientiarum Natralium Universitatis Sunyatseni, 2008, 47 (SUPPL. 2): : 46 - 49
  • [36] Genetic algorithm-based optimization of cutting parameters in turning processes
    D'Addona, Doriana M.
    Teti, Roberto
    FORTY SIXTH CIRP CONFERENCE ON MANUFACTURING SYSTEMS 2013, 2013, 7 : 323 - 328
  • [37] Optimization of control parameters for hybrid electric bus based on genetic algorithm
    Shi, Dapai
    Chu, Liang
    Guo, Jianhua
    Ou, Yang
    Liu, Chuqun
    Liu, Zuofei
    Ha, Linqi
    ADVANCES IN MECHANICAL ENGINEERING, 2017, 9 (11)
  • [38] SEARCHING SAFE LANDING SITE WITH PARAMETERS OPTIMIZATION BASED ON GENETIC ALGORITHM
    Liu, Xianggen
    Zhong, Huasong
    Chang, Sheng
    Meng, Yi
    2014 11TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP), 2014, : 102 - 105
  • [39] Genetic algorithm for parameters optimization of ANN-based speed controller
    Grzesiak, L. M.
    Meganck, V.
    Sobolewski, J.
    Ufnalski, B.
    EUROCON 2007: THE INTERNATIONAL CONFERENCE ON COMPUTER AS A TOOL, VOLS 1-6, 2007, : 2759 - 2764
  • [40] Genetic Algorithm Based Optimization of Controller Parameters for an Electromagnetic Levitation System
    Bhaduri, Rupam
    Banerjee, Subrata
    Sarkar, Mrinal Kanti
    MEMS, NANO AND SMART SYSTEMS, PTS 1-6, 2012, 403-408 : 3900 - +