Gas turbine performance enhancement through fault diagnostics and compressor cleaning

Due to harsh environmental conditions, industrial gas turbines are subjected to fouling during operation, and the total output is thereby affected as a result of this fouled component parts. The fouling considered in this research is at the compressor as a result of ingested particles from the operating environment. It has become imperative to develop effective models for optimisation of power plant in this environment, especially operational strategies for off-design conditions specifically for gas turbine engines. Optimisation of the engine profit is necessary for adequate and integrated evaluation of many important constraints such as; engine performance and efficiency, present and future revenue generated, availability and fuel price, selling price of electricity, engine life cycle cost, and future dynamics of the market. The state-of-the-art contribution of this research is the application of the method to both maximise total profit and usage availability of a particular gas turbine engine to generate power. In this method, the user is allowed the opportunity to select compressor washing duration and intervals considering the ambient operating condition of the engine. The optimiser employed in this research is a linear programming model that is powerful and capable of determining the maximum and minimum (optimal) values of the net profit of the project in question. Performance simulation was carried out with the help of Pythia and Turbomatch software of Cranfield University, and the output result appears almost very accurate since the deviations from the values gotten from public domain are very close. As such, the engine was validated and further used for the rest of the research. Because the number of variables involved in carrying out the optimisation process is very large, it becomes very difficult to carry out the optimal configuration. For this reason, the research is divided into phases to ascertain that all necessary factors are critically considered before the optimal value is attained. The engine performance simulation was first achieved, so as to determine the major engine parameters mostly affecting off-design performance. The second phase was the presentation of a mathematical correlation known as the Larson-Millar Parameter (LMP), which was used to determine the creep life of the turbine blade as a result of the effect of very hot air hitting the blades from combustion chamber. In this regards, the LMP was used to determine the life of the blade along the section. Sub-models known as the stress and thermal models were also incorporated to determine the stress along the different sections of the blade. The remaining creep life investigated was later converted to creep cost based on the equivalent hour of the lost life of the high pressure turbine (HPT) blade. Next was the emission model, which was applied to determine the amount of emission index exerted to the environment during the engine operation. The emission investigated was converted into cost based on emission tax rate. Hence, the three models mentioned above were incorporated into the economic model for gas turbine off-design performance analysis. The model includes life cycle cost assessment such as; maintenance and operating cost, fuel cost, and creep cost, emission and other taxes. Including total revenue which makes it possible to develop a model which will enable maximisation of total profit under variable operating conditions. Afterwards, the optimiser was introduced, and the optimiser was linked with Turbomatch library output result for the particular engine in use. The tool uses an evaluation of the fitness value of the objective function and takes into account the optimisation constraints.
A sensitivity analysis was introduced lastly to investigate the gravity of each of the effect of the necessary factors mitigating the successful output and net profit of the engine performance. It was observed that the total profit is affected by; rate of dirt deposit on the turbine blades, price of fuel, price of electricity, and number of hours spend during shut-down for off-line compressor water washing.