Multi‐phase analysis framework for handling batch process data

Abstract

Principal component analysis (PCA) and partial least squares (PLS) are bilinear modelling tools which have been successfully applied to three-way batch process data for monitoring and quality prediction. Most modelling approaches in the literature are based on a fixed model structure. The approach proposed in this paper, named the Multi-phase (MP) analysis framework, provides the flexibility to adjust the model structure to the dynamic nature of the process under study. The existence of several phases, with dynamics of different order and changes in the correlation structure among variables, is effectively identified. This adjustment of the model structure to the features of the process yields performance improvements in several applications, such as the on-line monitoring and final quality prediction, as shown when comparing the MP models with various well-established modelling approaches. Also, the MP approach provides a set of valuable tools for process understanding and data handling. Data from two processes, a fermentation process and a waste-water treatment process, are used to illustrate the capabilities of the proposed modelling framework. † Henceforth, the terms 'unfolding methods' and 'number of LMVs' will be used indistinctly throughout the paper, since each different number of LMVs implies a different unfolding procedure following Figure 1(c). 22: 632-643 ¶ Sub-models S 1 and S 2 may represent a single phase of a process or else comprise several sub-models.

Bilinear modelling of batch processes. Part I: theoretical discussion

“…represent a different reality than the objects of another cluster. This is discussed in Reference [32].…”

“…When properly calibrated, multi-phase models following (15) outperform batch-wise models in terms of prediction error [25] and are specially suited for off-line batch process monitoring [31,32].…”

Bilinear modelling of batch processes. Part II: a comparison of PLS soft‐sensors

“…As concluded in the companion paper [8], the multi-phase batch dynamic structure presents many advantages but its calibration may be challenging. Here, the multi-phase (MP) model according to Equation (6) is obtained following the approach of Reference [14]: the MP framework. A brief explanation of the MP approach for PLS is included in Appendix C. As stated before, the MP model may also need the imputation of future values.…”

“…There are several factors, besides how dynamics are built in the models, which may affect the monitoring performance and which are not treated here. See Reference [14] for more details.…”

Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

“…Another method for detecting phase change uses singular points detection based only on online measurements (Maiti et al, 2009;Régis et al, 2008). The reader can find more multi-phase analysis methods in (Yao and Gao, 2009;Camacho et al, 2008;Doan et al, 2007;Luo et al, 2016), which give different kinds of phase identification methods. Knowledge based phase identification fails when the process prior knowledge is not enough to divide processes into phases legitimately and difficult to customise for diverse fermentation processes.…”