Abstract

In batch processing, process control is typically carried out comparing trajectories of process variables with those in an in-control set of batches that yielded products within specifications. However, one strong assumption of these schemes is that all batches have equal duration and are synchronized, which is often not satisfied in practice. To overcome that, dynamic time warping (DTW) methods may be used to synchronize stages and align the duration of batches. In this paper, three DTW methods are compared using supervised classification through the k-nearest neighbor technique to determine the in-control set in a milk chocolate conching process. Four variables were monitored over time and a set of 62 batches with durations between 495 and 1170 min was considered; 53% of the batches were known to be conforming based on lab test results and experts' evaluations. All three DTW methods were able to promote the alignment and synchronization of batches; however, the KMT method (Kassidas et al. in AIChE J 44(4):864-875, 1998) outperformed the others, presenting 93.7% accuracy, 97.2% sensitivity, and 90.3% specificity in batch classification as conforming and non-conforming. The drive current of the main motor was the most consistent variable from batch to batch, being deemed the most important to promote alignment and synchronization of the chocolate conching dataset.