Analyzing the occurrence of foaming in batch fermentation processes using multiway partial least square approaches

This article presents an application of multiway partial least squares (MPLS) methods to develop interpretative correlation models to monitor the foaming occurrence and improve batch fermentation. We choose the exhaust differential pressure as a quality variable to quantify the foaming occurrence and consider three‐dimensional datasets of different batches, process variables, and measurements. We integrate batch‐wise unfolding (BWU) and observation‐wise unfolding (OWU) of plant datasets with standard, dynamic, and kernel PLS methods. We find that dynamic PLS (DPLS) with OWU and time‐lagged quality variables to be the most efficient, accurate, and easy to implement. The BWU approach is useful for analyzing the differences between batches and identifying abnormalities and outliers, while the OWU quantifies the variation within a given batch. With OWU, the DPLS method with one unit of time lag in the quality variable is the most effective, accurate, and easy to implement. With both BWU and OWU, we identify the quantitative effects of process variables on the quality variable and providence guidance to improve fermentation performance.