The construction of kinetic models has become an indispensable step in developing and scale-up of processes in the industry. Model-based design of experiments (MBDoE) has been widely used to improve parameter precision in nonlinear dynamic systems. Such a framework needs to account for both parametric and structural uncertainty, as the physical or safety constraints imposed on the system may well turn out to be violated, leading to unsafe experimental conditions when an optimally designed experiment is performed. In this work, Gaussian processes are utilized in a two-fold manner: 1) to quantify the uncertainty realization of the physical system and calculate the plant-model mismatch, 2) to compute the optimal experimental design while accounting for the parametric uncertainty. The proposed method, Gaussian process-based MBDoE (GP-MBDoE), guarantees the probabilistic satisfaction of the constraints in the context of model-based design of experiments. GP-MBDoE is assisted with the use of adaptive trust regions to facilitate a satisfactory local approximation. The proposed method can allow the design of optimal experiments starting from limited preliminary knowledge of the parameter set, leading to a safe exploration of the parameter space. This method’s performance is demonstrated through illustrative case studies regarding the parameter identification of kinetic models in flow reactors.

动力学模型的构建已成为工业流程开发和扩大中不可或缺的步骤。基于模型的实验设计（MBDoE）已被广泛用于提高非线性动态系统中的参数精度。这样的框架需要考虑参数和结构的不确定性，因为强加给系统的物理或安全约束可能会被违反，从而导致在执行最佳设计的实验时出现不安全的实验条件。在这项工作中，以两种方式利用高斯过程：1）量化物理系统的不确定性实现并计算工厂模型不匹配，2）在考虑参数不确定性的同时计算最佳实验设计。提出的方法，基于高斯过程的MBDoE（GP-MBDoE），在基于模型的实验设计中保证了约束的概率满足。GP-MBDoE在使用自适应信任区域方面得到了帮助，以促进令人满意的局部逼近。所提出的方法可以允许从有限的参数集初步知识开始设计最佳实验，从而可以安全地探索参数空间。通过有关流动反应器动力学模型参数识别的说明性案例研究，证明了该方法的性能。所提出的方法可以允许从有限的参数集初步知识开始设计最佳实验，从而可以安全地探索参数空间。通过有关流动反应器动力学模型参数识别的说明性案例研究，证明了该方法的性能。所提出的方法可以允许从有限的参数集初步知识开始设计最佳实验，从而可以安全地探索参数空间。通过有关流动反应器动力学模型参数识别的说明性案例研究，证明了该方法的性能。