Improved calibration investigation using phase-wise local and cumulative quality interpretation and prediction

Abstract

In the present work, a multiphase calibration modeling and statistical analysis strategy is developed for the improvement of process understanding and quality prediction. Having realized the phase-wise local and cumulative effects on quality interpretation and prediction, the major task lies in how to qualify and quantify them among multiple phases. The proposed scheme is presented on two different levels: On the first level, phase-specific variable selection and O2-PLS are designed focusing on revealing the local effects of individual phases on quality variations, e.g. within the current phase, which part of process variations are responsible for quality variations and which quality variations are dominated. Moreover, bootstrapping technique is employed during the procedure of variable selection and O2-PLS, which can enhance the reliability and robustness of calibration analysis. On the second level, conventional PLS is used to model the quantitative relationship between multiple phases and the final qualities so that the cumulative effects on quality variations are apprehended by additively stacking the local contributions of various phases. The proposed strategy highlights such an idea that in real multiphase processes, each phase may only explain one part of quality variations and the final qualities can only be additively and jointly defined by multiple phases. A benchmark simulation of fed-batch penicillin fermentation production is considered and put into illustration, which demonstrates the efficiency of the proposed algorithm for better process understanding and quality interpretation in multiphase processes.