A key challenge in maximizing the effectiveness of model-based design of experiments for calibrating nonlinear process models is the inaccurate prediction of information that is afforded by each new experiment. We present a novel methodology to exploit prior probability distributions of model parameter estimates in a bi-objective optimization formulation, where a conditional-value-at-risk criterion is considered alongside an average information criterion. We implement a tractable numerical approach that discretizes the experimental design space and leverages the concept of continuous-effort experimental designs in a convex optimization formulation. We demonstrate effectiveness and tractability through three case studies, including the design of dynamic experiments. In one case, the Pareto frontier comprises experimental campaigns that significantly increase the information content in the worst-case scenarios. In another case, the same campaign is proven to be optimal irrespective of the risk attitude. An open-source implementation of the methodology is made available in the Python software Pydex.

最大化基于模型的实验设计的有效性以校准非线性过程模型的一个关键挑战是对每个新实验提供的信息的不准确预测。我们提出了一种新的方法来利用双目标优化公式中模型参数估计的先验概率分布，其中条件风险值标准与平均信息标准一起考虑。我们实施了一种易于处理的数值方法，该方法将实验设计空间离散化，并在凸优化公式中利用了连续努力实验设计的概念。我们通过三个案例研究展示了有效性和易处理性，包括动态实验的设计。在一种情况下，帕累托前沿包括在最坏情况下显着增加信息内容的实验活动。在另一种情况下，无论风险态度如何，同样的活动被证明是最优的。Python 软件中提供了该方法的开源实现派克斯。