(164g) Fault Detection in an Acid Gas Removal Unit of IGCC Plant Using Magnitude Ratio Algorithm

Authors: 
Mobed, P., Texas Tech University
Maddala, J., SYSENG, LLC
Bhattacharyya, D., West Virginia University
Rengaswamy, R., Texas Tech University

Tight carbon emission standards on power generation from fossil fuels have made researchers focus on plants with near-zero emissions. Integrated gasification combined cycle (IGCC) power plants with CO2 capture are promising technologies for safe and clean power generation that enforce the environmental emission standards by treating the fuel gas in the acid gas removal (AGR) unit. Satisfying the environmental constraints depends crucially on the performance of AGR unit. Any faults that occur in the AGR unit can drive the process away from its nominal condition and may lead to violation of environmental constraints and hazardous consequences. An early detection and identification of the faults facilitate preventive actions for safe and optimal operations. Any abnormalities affect several process variables throughout the process which requires measurements of variables for symptoms to be observed. In order to detect and diagnose faults, crucial variables must be identified based on their economical and practical feasibility. An algorithmic approach for identifying the optimal number, type and location of the sensors for fault detection and diagnosis is useful, particularly for the large-scale, emerging fossil power plants with CO2 capture.

The usefulness of magnitude ratio (MR) algorithm developed in our previous work[1], which enhances the diagnosis capability of signed directed graph (SDG) sensor placement algorithm, is demonstrated by implementing on three basic case studies. The algorithm uses all the variables from the process and assumes the magnitude ratio of each pair as a pseudo-sensor. Similar to SDG, pseudo-sensors have discrete states and are treated by symmetric difference operator in sensor network design framework. When used in SDG framework, the algorithm retains the properties of SDG and manages to add more information for improving diagnosis capability. The algorithm is implemented on the SELEXOL-based AGR unit to identify an optimal sensor network. The enhanced diagnosis capability of the MR algorithm observed in AGR unit is promising for further implementation of the algorithm in large and complex power plants.

Reference:

[1] P Mobed, J Maddala, D Bhattacharyya, R Rengaswamy, "On the Use of Magnitude Ratio in Sensor Placement Algorithms for Fault Detection and Diagnosis in Complex Energy Processes", 57th Annual ISA POWID Symposium, Scottsdale, Arizona, USA, 2014.