(149b) Batch Process Monitoring By Dynamic Time Warping and k-Means Clustering
AIChE Spring Meeting and Global Congress on Process Safety
2016
2016 AIChE Spring Meeting and 12th Global Congress on Process Safety
2nd Big Data Analytics
Big Data Analytics and Statistics I
Wednesday, April 13, 2016 - 8:22am to 8:44am
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 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.