|Ph.D.,||Texas A&M University,||(2012)|
|B.S.,||Louisianna State University,||(2008)|
Process monitoring has been described as the number one priority for improvement of industrial process operations. Excellent process monitoring includes advanced process control, fault detection, and online state and parameter estimation. This field offers a diverse area for research that is both interesting and challenging. My focus in research is the development and implementation of new techniques in process monitoring that are specifically applicable for industrial practice.
My current work particularly involves research into new techniques for fault detection, specifically for systems involving soft sensors. The integration of these two areas of process monitoring promises to produce improved performance. In addition, this type of approach would allow process monitoring strategies to perform fault detection directly on specifically critical unmeasured variables. This is important in situations that arise frequently in chemical engineering practice as variables of interest, such as reactor concentration, are often difficult or expensive to measure online. Soft sensors, specifically model based state estimators, allow the live determination of these unmeasured variables from process variables that are measured, by use of the dynamic process model. Using fault detection on these inferred variables allows the detection of abnormal events directly on the critical process variables.