Role of optimisation method on kinetic inverse modelling of biomass pyrolysis at the microscale
Role of optimisation method on kinetic inverse modelling of biomass pyrolysis at the microscale
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DOI：
10.1016/j.fuel.2019.116251
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发表时间：
2020-02
期刊：
影响因子：
7.4
通讯作者：
Dwi M. J. Purnomo;F. Richter;M. Bonner;R. Vaidyanathan;G. Rein
中科院分区：
文献类型：
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作者：
Dwi M. J. Purnomo;F. Richter;M. Bonner;R. Vaidyanathan;G. Rein
了解生物质热解对生物燃料生产和火灾安全具有重要意义。逆模拟是一种越来越常用的技术，用来寻找控制热解的动力学参数的值。这种逆模型的质量取决于实验数据的质量、动力学模型和所使用的优化方法。与前两个部分不同的是，所选择的优化方法，即算法和目标函数的结合，在文献中很少讨论。本工作比较了五种常用的高级算法(遗传算法、汞齐、混洗复杂进化、布谷鸟搜索和多启动非线性规划)和一种简单算法(随机搜索)的精确度和效率，以通过文献中的热重测量在微尺度上寻找纤维素和木材热解的动力学参数。这些算法结合了七个目标函数，包括集中函数和离散函数。结果表明，对于纤维素(简单的化学)，没有必要使用先进的优化算法，因为简单的算法实现了类似的高精度和更高的效率(速度快40%到350%)。然而，对于木材(复杂化学)，高级算法和集中函数的组合极大地提高了精度。在我们研究的25种可能的木材组合中，均方误差目标函数的混洗复合进化的效果最好，质量损失率误差为0.91%， × 1013CPU时间为0.88。这些结果可以指导选择最优的优化方法用于动力学参数的反模拟，提高了精度和效率。
Understanding biomass pyrolysis is important for biofuel production and fire safety. Inverse modelling is an increasingly used technique to find values for the kinetic parameters that control pyrolysis. The quality of this inverse modelling depends on, in order of importance, the quality of the experimental data, the kinetic model, and the optimisation method used. Unlike the two former components, the optimisation method chosen, i.e. the combination of algorithm and objective function, is rarely discussed in the literature. This work compares the accuracy and efficiency of five commonly used advanced algorithms (Genetic Algorithm, AMALGAM, Shuffled Complex Evolution, Cuckoo Search, and Multi-Start Nonlinear Program) and a simple algorithm (Random Search) to find the kinetic parameters for cellulose and wood pyrolysis at the microscale via thermogravimetric measurements in the literature. These algorithms are combined with seven objective functions comprising concentrated and dispersed functions. The results show that for cellulose (simple chemistry) the use of an advanced optimisation algorithm is unnecessary, since a simple algorithm achieves similarly high accuracy with higher efficiency (40% to 350% faster). However, for wood (complex chemistry) a combination of an advanced algorithm and a concentrated function greatly improves accuracy. Among the 25 possible combinations we investigated for wood, Shuffled Complex Evolution with mean square error objective function performed best with 0.91% error in mass loss rate and 0.88 × 1013CPU time. These findings can guide the selection of the optimal optimisation method to use in inverse modelling of kinetic parameters, improving accuracy and efficiency.