Comparative Analysis of Gas-Turbine Engine Diagnostics through Compressor Wash Wastewater Parameter Monitoring for Geregu Gas-Turbine Power Plant

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Abubakar Kandi Mohammed
Idris Ibrahim Ozigis
Nasir Muhammad Lawal

Abstract

This paper focuses on finding a lasting solution to the reason why offline compressor washing of unit 13 at Geregu Power plc in   Ajaokuta does not restore power output lost due to fouling back to the rated output, as is the case for the other two (2) units in the plant and for several reported cases published in the literature. Monitoring of parameters closely related to the power output was carried out to find out the root causes for the low output recovery after offline compressor wash and came up with a successful diagnosis by interrelating GT13 wash effluent test results of parameters like conductivity, turbidity, and heavy metal concentration with parameters of two (2) other turbines in the plant. Using statistical tools like the mean and Pearson correlation coefficient for the four-year period, the mean comparative result indicates that the conductivity of GT13 was higher than that of GT11 and GT12 by 50% and 79%, respectively. The turbidity of GT13 was 570% and 700% higher than that of GT11 and GT12. The Pearson correlation coefficients of GT13 effluent conductivity, turbidity, and silica content relative to the power output recovery after wash were (-89.2%), (-64.4%), and (-77.3%), respectively, and the covariance of each of the three parameters relative to one another was high. Effluent Ph as a factor was discarded based on its 0% linear correlation coefficient with the output. The conclusion reached is that the co-linear relationship between conductivity, silica content, and effluent turbidity is responsible for GT13's low output recovery after the wash.

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How to Cite
[1]
A. K. Mohammed, I. I. Ozigis, and N. M. Lawal, “Comparative Analysis of Gas-Turbine Engine Diagnostics through Compressor Wash Wastewater Parameter Monitoring for Geregu Gas-Turbine Power Plant”, AJERD, vol. 6, no. 2, pp. 192–204, Dec. 2023.
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