Extracting Technology and Detecting Outliers from Process Time Series Data Reflecting Expert Operator Skills

This paper proposes a novel method to develop a process response model from continuous time-series data. The method consists of the following phases: (1) reciprocal correlation analysis; (2) process response model; (3) extraction of control rules; (4) extraction of a workflow; (5) detecting outliers. The main contribution of the research is to establish a method to mine a set of meaningful control rules from learning classifier system using the minimum description length criteria and tabu search method. The proposed method has been applied to an actual process of a biochemical plant and has shown the validity and the effectiveness