Risk mitigation in model-based experiment design: a continuous-effort approach to optimal campaigns

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

Probabilistic framework for optimal experimental campaigns in the presence of operational constraints

The predictive capability of any mathematical model is intertwined with the quality of experimental data collected for its calibration. Model-based design of experiments helps compute maximally informative campaigns for model calibration. But in early stages of model development it is crucial to account for model uncertainties to mitigate the risk of uninformative or infeasible experiments. This article presents a new method to design optimal experimental campaigns subject to hard constraints under uncertainty, alongside a tractable computational framework. This computational framework involves two stages, whereby the feasible experimental space is sampled using a probabilistic approach in the first stage, and a continuous-effort optimal experiment design is determined by searching over the sampled feasible space in the second stage. The tractability of this methodology is demonstrated on a case study involving the exothermic esterification of priopionic anhydride with significant risk of thermal runaway during experimentation. An implementation is made freely available based on the Python packages DEUS and Pydex

An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty

To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study