Model-based experimental design is attracting increasing attention in chemical process engineering. Typically, an iterative procedure is pursued: an approximate model is devised, prescribed experiments are then performed and the resulting data is exploited to refine the model. To help to reduce the cost of trial-and-error approaches, strategies for model-based design of experiments suggest experimental points where the expected gain in information for the model is the largest. It requires the resolution of a large nonlinear, generally nonconvex, optimization problem, whose solution may greatly depend on the starting point. We present two discretization strategies that can assist the experimenter in setting the number of relevant experiments and performing an optimal selection, and we compare them against two pattern-based strategies that are independent of the problem. The validity of the approaches is demonstrated on an academic example and two test problems from chemical engineering including a vapor liquid equilibrium and reaction kinetics.

基于模型的实验设计在化学过程工程中正受到越来越多的关注。通常，采用迭代程序：设计一个近似模型，然后执行指定的实验，并利用所得数据来完善模型。为了帮助减少试错法的成本，基于模型的实验设计策略建议了实验点，其中模型信息的预期收益最大。它需要解决一个大的非线性（通常是非凸的）优化问题，其解决方案可能在很大程度上取决于起点。我们提供了两种离散化策略，可以帮助实验者设置相关实验的数量并进行最佳选择，然后将它们与两种独立于问题的基于模式的策略进行比较。方法的有效性在一个学术实例和来自化学工程的两个测试问题上得到了证明，包括汽液平衡和反应动力学。