431561 Batch Process Monitoring By Dynamic Time Warping and k-Means Clustering

Monday, November 9, 2015
Exhibit Hall 1 (Salt Palace Convention Center)
Adel Basli1, Ajit Gopalakrishnan1, Sudhir Kulkarni1, Tim Poludniak2 and Brian Besancon1, (1)Air Liquide, Newark, DE, (2)Air Liquide, Newport, DE

In the production of membranes for gas separation, the raw material goes through a series of batch processes before it is converted into a finished product. Each of these batch processes is governed by a recipe, which often involves a combination of process control logic and operator interventions. This work presents a method for monitoring the quality of a batch process by detecting deviations from the recipe, through a combination of Dynamic Time Warping (DTW) and k-Means clustering.

Historical data (e.g., flows, temperatures, pressures) are gathered from a membrane manufacturing process, which consists of several steps of variable duration. Through a combination of interactions between researchers and subject matter experts, a set of “ideal” and “bad” recipes are identified which is used as a training set for the model. Historical operation is then compared with the ideal and bad recipes, and a unified distance metric is used to then cluster the operational data using k-Means clustering. Encouraging results are reported from practice.

Extended Abstract: File Not Uploaded
See more of this Session: Interactive Session: Systems and Process Control
See more of this Group/Topical: Computing and Systems Technology Division