<p>Thermogravimetric analysis was employed to investigate the pyrolysis kinetics of black walnut (<i>Juglans nigra</i>) under the nitrogen atmosphere across multiple heating rates ranging from 5 to 40&#xa0;K/min. This study comparatively evaluated five kinetic reaction models with pseudo-components (1 to 5 components) and innovatively introduced a genetic algorithm to globally optimize core kinetic parameters—including the pre-exponential factor&#xa0;<i>Z</i>, activation energy&#xa0;<i>E</i>, reaction order&#xa0;<i>n</i>, and initial density&#xa0;<i>ρ</i><sub><i>0</i></sub>. Parameter calibration was performed based on experimental data at 5, 10, 15, and 20&#xa0;K/min, while the predictive capability and generalizability of the models were independently validated using experimental data at 40&#xa0;K/min. Comprehensive benchmarking results demonstrated that the four-component model exhibited significant advantages in both predictive accuracy and computational efficiency among all tested models. This study provides a highly accurate and efficient parameter optimization strategy for kinetic modeling of biomass pyrolysis.</p>

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Decoupling reaction mechanisms: robust inverse modeling of black walnut pyrolysis kinetics

  • Baoping Wang,
  • Quanwei Li,
  • Ruiyu Chen

摘要

Thermogravimetric analysis was employed to investigate the pyrolysis kinetics of black walnut (Juglans nigra) under the nitrogen atmosphere across multiple heating rates ranging from 5 to 40 K/min. This study comparatively evaluated five kinetic reaction models with pseudo-components (1 to 5 components) and innovatively introduced a genetic algorithm to globally optimize core kinetic parameters—including the pre-exponential factor Z, activation energy E, reaction order n, and initial density ρ0. Parameter calibration was performed based on experimental data at 5, 10, 15, and 20 K/min, while the predictive capability and generalizability of the models were independently validated using experimental data at 40 K/min. Comprehensive benchmarking results demonstrated that the four-component model exhibited significant advantages in both predictive accuracy and computational efficiency among all tested models. This study provides a highly accurate and efficient parameter optimization strategy for kinetic modeling of biomass pyrolysis.