Qiao Huang

Date of Graduation

2017

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Chemical and Biomedical Engineering

Committee Chair

Debangsu Bhattacharyya

Committee Member

Richard Turton

Committee Member

Charter D. Stinespring

Committee Member

Stephen E. Zitney

Committee Member

Edward M. Sabolsky

Abstract

The operating temperature is one of the most important variables for gasifier operation. A higher temperature can shorten the service life of the refractory lining. A lower temperature can reduce the extent of carbon conversion and disrupt the flow of molten slag in a slagging gasifier. Therefore temperature is one of the most important variable in a gasifier. In slagging gasifiers, molten slag can penetrate into the refractory lining leading to refractory spallation, undesired downtime, and costly replacement. Even though the temperature and extent of slag penetration are extremely important for the gasifier operation, they cannot be measured reliably by current measurement techniques. With this motivation, a novel ‘smart’ refractory brick is developed where various types of sensors are embedded in it. Although the ‘smart’ brick technology is promising, several issues needed to be addressed before it can be commercialized. First, a significant temperature drop is expected along the sensor length. Therefore, the traditional correlation-based approaches used for converting the raw measurements from a sensor to the variable of interest cannot be used. Second, thermal and electrical properties of the refractory do change in course of gasifier operation as the flowing molten slag penetrates into the refractory lining. The response of the embedded sensors can change due to changes in the temperature, and/or extent of slag penetration. Therefore, it becomes difficult to estimate the variables of interest by using the raw measurements. Third, if a model-based approach is used to estimate the temperature and extent of slag penetration, then model inaccuracy and measurement noise must be accounted for. Fourth, it is desired that the measurements collected from the embedded sensors are sent by using a wireless transmitter. Measurements from wireless sensor networks can be out-of-sequence leading to poor estimation. In order to address above issues, first-principles, dynamic model of the ‘smart’ brick has been developed. Sensor models are developed with consideration of installation direction. These models are further used in Kalman filter-based estimation framework to estimate the temperature and extent of slag penetration. Out-of-sequence measurement problem is also addressed. Finally, the optimal sensor placement for this ‘smart’ brick system is obtained by maximizing the state estimation accuracy

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