Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation

Abstract

This paper discusses the design of an inferential sensor for the online prediction of the end-quality of an industrial batch polymerization process. Owing to unequal batch speeds, measurement profiles must be synchronized before modeling. This makes profile alignment an integral part of any inferential sensor. In this work, a novel online hybrid derivative dynamic time warping data alignment technique is presented. The proposed technique allows for automatic adjustment of the warping resolution to achieve optimal alignment results for both slowly and rapidly varying parts of the measurement profiles. The proposed online data alignment technique is combined with a multiway partial least-squares black box model to yield online predictions of the final quality of a running batch process. It is demonstrated that this inferential sensor is capable of accurately predicting the quality online for an industrial polymerization process, even when the production process is only halfway, that is, well before lab measurements become available. As a result of this early warning, batches violating the quality specifications can be corrected or even stopped. This leads to fewer off-spec batches, saves production time, lowers operational costs, and reduces waste material and energy.