The quantitative description of the dynamic behaviour of reacting systems requires the identification of an appropriate set of kinetic model equations. The selection of the correct model may pose substantial challenges as there may be a large number of candidate kinetic model structures. In this work, a model selection approach is presented where an Artificial Neural Network classifier is trained for recognising appropriate kinetic model structures given the available experimental evidence. The method does not require the fitting of kinetic parameters and it is well suited when there is a high number of candidate kinetic mechanisms. The approach is demonstrated on a simulated case study on the selection of a kinetic model for describing the dynamics of a three-component reacting system in a batch reactor. The sensitivity of the approach to a change in the experimental design and to a change in the system noise is assessed.

反应系统动力学行为的定量描述需要确定一组合适的动力学模型方程。由于可能存在大量候选动力学模型结构，因此正确模型的选择可能会带来重大挑战。在这项工作中，提出了一种模型选择方法，其中训练了人工神经网络分类器以在给定可用实验证据的情况下识别适当的动力学模型结构。该方法不需要动力学参数的拟合，并且在存在大量候选动力学机制时非常适合。在选择动力学模型的模拟案例研究中证明了该方法，该动力学模型用于描述间歇反应器中三组分反应系统的动力学。