The main cause of performance degradation in entrained-bed slagging gasification systems is attributed to evolution of structural damage in the refractory walls. Early detection of such damage is necessary to avert unscheduled shutdown of a gasification plant. This paper develops an integrated computer simulation model of a generic entrained-bed slagging gasifier for formulation of a damage prediction algorithm with the objective of real-time degradation monitoring and condition-based maintenance of refractory walls. The integrated simulation model yields: (a) quasi-steady-state spatial temperature profiles at any cross-section of the gasification system, and (b) dynamic response of the refractory wall temperature that is measured by an array of sensors installed at specified locations on the external surface of the gasifier wall. The key idea for early detection of refractory-wall damage is built upon the fact that a local anomaly (i.e. deviation from the nominal condition) is likely to influence the temperature gradient in the refractory wall due to changes in the thermal impedance. The information from dynamic response of refractory temperature is extracted in a compressed form as statistical patterns of evolving anomaly through usage of a recently reported data-driven pattern identification tool called symbolic dynamic filtering (SDF). The results of this model-based investigation show that the proposed anomaly detection and damage prediction method is potentially capable of characterizing the health status of refractory walls in particular and the entire gasification system in general. The SDF algorithms in this paper are implemented on the MATLAB platform and are interfaced with the gasification plant simulation model for emulation of real-time degradation monitoring. [PUBLICATION ABSTRACT]
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