Probability-Based Design of Experiments for Batch Process Optimization with End-Point Specifications

Abstract

Consistently complying with end-point specifications, mainly end-use product properties, is a key issue to the competitiveness of batch processes. To maximize in some way the probability of observing successful runs, a series of experiments is specifically designed to pinpoint the smallest operating region that guarantees that end-point conditions can meet their desired targets. On the basis of data-driven modeling of the underlying binomial probability of success, the proposed methodology seeks to trade off improving parameter precision with experimenting in a reduced region where there is a high probability of satisfying end-point specifications. Two case studies are used to demonstrate the efficacy of the probability-based optimal design of experiments to find optimal policies for runs involving stochastic binary outcomes. Run-to-run improvement of the success rate for the operating policy in the acetoacetylation of pyrrole with diketene is first discussed. Results obtained for emulsion polymerization ...