Robust batch-to-batch optimization with global sensitivity analysis for microbial fermentation processes under model-plant mismatch

Abstract

This paper studies the optimization of product yield for the microbial fermentation process in the presence of model-plant mismatch. The typical “two-step” optimization method involves modifying the model by adjusting its parameters and subsequently utilizing the updated model to optimize product output. However, due to estimability and overfitting problems, it is often impractical to update all available parameters. Therefore, we propose a robust batch-to-batch optimization method with global sensitivity analysis. First, the parameters to be identified are determined through global sensitivity analysis. Then, the robust batch-batch optimization method is employed to optimize the yield of fermentation products. Compared with previous parameter selection methods, global sensitivity analysis considers the complex correlation between parameters, allowing for a more comprehensive evaluation of the impact of multiple parameters and their interactions on the model. Furthermore, compared with previous studies, the robust batch-to-batch optimization method assigns weights to each variable in the identification objective function based on experience and model output variance, significantly reducing the uncertainty of the next optimal batch run. Simultaneously, the method is robust to the uncertainty in initial conditions, ensuring a more stable process when running in large batches. A penicillin fermentation case study verifies the convergence and robustness of the method.