Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process

This paper considers the development of multivariate statistical soft sensors for the online estimation of product quality in a real-world industrial batch polymerization process. The batches are characterized by uneven length, non-reproducible sequence of processing steps, and scarce number of measurements for the quality indicators with uneven sampling of (and lag on) these variables. It is shown that, for the purpose of quality estimation, the complex series of operating steps characterizing a batch can be simplified to a sequence of three estimation phases. The switching from one phase to the other one can be triggered by easily detectable events occurring in the batch. For each estimation phase, PLS software sensors are designed, and their performance is evaluated against plant data. The estimation accuracy can be substantially improved if some form of dynamic information is included into the models, either by augmenting the process data matrix with lagged measurements, or by averaging the process measurements values on a moving window of fixed length. In particular, the moving average three-phase PLS estimator shows the best overall performance, providing accurate estimations also during estimation Phase 2, which is characterized by a very large variability between batches.