The prevalent use of organic materials in manufacturing is a fire safety concern, and motivates the need for predictive thermal decomposition models. A critical component of predictive modeling is numerical inference of kinetic parameters from bench scale data. Currently, an active area of computational pyrolysis research focuses on identifying efficient, robust methods for optimization. This paper demonstrates that kinetic parameter calibration problems can successfully be solved using classical gradient-based optimization. We explore calibration examples that exhibit characteristics of concern: high nonlinearity, high dimensionality, complicated schemes, overlapping reactions, noisy data, and poor initial guesses. The examples demonstrate that a simple, non-invasive change to the problem formulation can simultaneously avoid local minima, avoid computation of derivative matrices, achieve a computational efficiency speedup of 10x, and make optimization robust to perturbations of parameter components. Techniques from the mathematical optimization and inverse problem communities are employed. By re-examining gradient-based algorithms, we highlight opportunities to develop kinetic parameter calibration methods that should outperform current methods.

在制造中普遍使用有机材料是一项消防安全问题，并激发了对预测性热分解模型的需求。预测建模的重要组成部分是根据工作台规模数据对动力学参数进行数值推断。当前，计算热解研究的活跃领域集中于确定有效，鲁棒的优化方法。本文证明，使用经典的基于梯度的优化可以成功解决动力学参数校准问题。我们将探索表现出令人关注的特征的校准示例：高非线性，高维，复杂方案，重叠反应，嘈杂数据以及不良的初步猜测。这些示例表明，对问题的构成进行简单，无创的更改可以同时避免局部最小值，避免计算导数矩阵，实现10倍的计算效率加速，并使优化对参数分量的扰动具有鲁棒性。采用了来自数学优化和反问题社区的技术。通过重新检查基于梯度的算法，我们重点介绍了开发动力学参数校准方法的机会，该方法应优于当前方法。