Accurate, predictive reaction models are critical for the design and optimization of chemical looping combustion (CLC) reactors. The formulation and estimation of kinetic parameters for these reaction models using a first-principles equation-oriented (EO) approach is particularly beneficial as large amounts of experimental data spanning process-relevant conditions can be used to estimate parameters in a computationally tractable way. This work demonstrates the application of a novel EO framework to develop reduction reaction kinetic models of an iron-based CLC oxygen carrier (OC). An optimization problem is formulated to estimate kinetic parameters that provide the best fit to the experimental data. The model predicts the state of the OC with mean square error values of 2.5%–4.4% across the full range of validation data, including multiple reduction cycles.

中文翻译：

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应用面向方程的框架来制定和估计化学循环反应模型的参数

准确的预测反应模型对于化学循环燃烧 (CLC) 反应器的设计和优化至关重要。使用第一性原理面向方程 (EO) 方法对这些反应模型的动力学参数进行制定和估计是特别有益的，因为跨越过程相关条件的大量实验数据可用于以计算上易于处理的方式估计参数。这项工作展示了一种新型 EO 框架在开发铁基 CLC 氧载体 (OC) 的还原反应动力学模型中的应用。制定优化问题以估计提供与实验数据最佳拟合的动力学参数。该模型在整个验证数据范围内以 2.5%–4.4% 的均方误差值预测 OC 的状态，