(246q) Dynamic Model and Estimator Development for a Smart Refractory Brick with Embedded Sensors for Gasifier Applications Conference: AIChE Annual MeetingYear: 2015Proceeding: 2015 AIChE Annual MeetingGroup: Computing and Systems Technology DivisionSession: Interactive Session: Systems and Process Operations Time: Monday, November 9, 2015 - 6:00pm-8:00pm Authors: Huang, Q., West Virginia University Paul, P., West Virginia University Bhattacharyya, D., West Virginia University Sabolsky, E., West Virginia University Dynamic Model and Estimator Development for a Smart Refractory Brick with Embedded Sensors for Gasifier Applications Qiao Huang, Prokash Paul, Debangsu Bhattacharyya Department of Chemical Engineering, West Virginia University, Morgantown, WV 26506, USA Edward M. Sabolsky Department of Mechanical & Aerospace Engineering, West Virginia University, Morgantown, WV 26506, USA Operating temperature of a gasifier is extremely important. A lower temperature can lead to lower carbon conversion, but a higher temperature can reduce the life of the refractory resulting in a significant capital cost. Therefore, the operating temperature must be strictly monitored. Another important issue in the operation of the entrained-flow gasifiers is penetration of the molten slag into the entrained flow gasifiers causing physical and chemical changes. Thus a measure of the extent of slag penetration can be very useful not only for process monitoring, but also for developing effective control strategies. However due to the harsh environment inside the gasifier, it is practically impossible to measure any variable using current state of the art in measurement technology. If the traditional measuring devices, such as thermocouples, are inserted into gasifier through open ports within the refractory, they survive for only several weeks. Furthermore, the defects introduced by the open access ports provide paths for slag penetration reducing the refractory life further. In order to address these issues, a novel smart refractory with embedded sensors is being developed. Since the sensors are embedded within the brick and the materials for embedded sensors are carefully selected, these smart bricks results in minimal defects. While these smart bricks can measure a number of desired variables, the penetrating slag changes the thermal and electrical properties of the refractory and the layout of the sensor. Therefore, typical correlations developed by only considering the data from the pristine bricks would not result in satisfactory estimation of the variables of interest. A model-based approach is employed to circumvent this challenge. This approach can be useful to estimate not only the temperature profile but also other characteristics including stress profile, change of the material properties, and extent of slag penetration. It could be noted that due to the inaccuracy of the process model, only the model itself is not able to provide a good estimate of the variables. However, using the noisy measurement data and the model together, the accuracy of the estimated variables can be significantly improved. Therefore, an algorithm is needed in this application to get the best estimates of the variables. To be used in the estimator algorithm, rigorous, first-principles, dynamic models of the refractory brick and the embedded sensors are developed. Both the pristine smart brick and the slag-infiltrated brick are considered in building the thermal model. The properties models are based on the data from the in-house experiment and the open literature. Models for the embedded sensors, such as thermocouple, interdigital capacitor, strain gauge, thermistor, are also developed with the consideration of the location as well as the geometries. Finally, the nonlinear unknown input filter (UIF) using the unscented Kalman filter (UKF) approach is developed. The raw measurements from the embedded sensors are used in this algorithm to obtain the desired estimates of the variables.