Expert knowledge-guided feature selection for data-based industrial process monitoring

Indrustial processes are characterized to be in open environments with uncertainty, unpredictability and nonlinear behavior. Rigorous measuring and monitoring is required to strive for product quality, safety and finance. Therefore, data-based monitoring systems have gain interest in academia and industry (e.g. clustering). However industrial processes have high volumes of complex and high dimensional data available, with poorly defined domains and sometimes redundant, noisy or inaccurate measures with unknow parameters. When a mechanistic or structural model is not available or suitable, selecting relevant and informative variables (reducing the high dimensionality) eases pattern recognition to identify functional states of the process. In this paper, we address the feature selection problem in data-based industrial processes monitoring where a mathematical or structural model is not available or suitable. Expert knowledge-quidance is used inside a wrapper feature selction based on clustering. The reduced set of features is capable of represent intrinsic historical-data structure integrating the expert knowledge abput the process. A monitoring system is proposed and tested on an intesification reactor (OPR)', over the thiosulfate and the esterifictation  reaction. Results show fewer variables are needed to correctly identify the process functional states.