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

• Published in: Computers & chemical engineering 2022-03, Vol.159, p.107680, Article 107680
• Main Authors: Kusumo, Kennedy Putra, Kuriyan, Kamal, Vaidyaraman, Shankarraman, García-Muñoz, Salvador, Shah, Nilay, Chachuat, Benoît
• Format: Article
• Language: English
• Subjects: Conditional-value-at-risk; Continuous effort; Model-based design of experiments; Optimal experiment design; Risk measure; Uncertainty
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2022.107680

•Novel bi-objective formulation to mitigate non-informative experiments in model-based experimental design under uncertainty.•Conditional-value-at-risk is considered alongside the average information in a bi-objective optimization problem.•Discretization of experimental set combined with continuous-effort approximation yield convex optimization regardless of model structure.•Characterization of a Pareto-efficient frontier from which experimenters can choose depending on their attitude to risk.•Industrially relevant experiment design problems proven computationally tractable with the proposed methodology. 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.