Thermally efficient gas turbine with pressure drop-based automated filter cleaning and optimized fuel control system

Abstract

Optimizing elements like fuel specifications and inlet–outlet conditions is essential for enhancing gas turbine performance. Prior research optimized filter house pressure drop using statistical methods, but these methods had drawbacks related to measurement errors. Additionally, with changing air conditions, periodic-based cleaning for self-cleaning pulse filters at the turbine inlet proved unsuccessful, making the procedure difficult and time-consuming. A novel “Thermally Efficient Gas Turbine with Pressure Drop-Based Automated Filter Cleaning and Optimized Fuel Control System” has been suggested to improve gas turbine performance. To handle pressure drop effectively, this cutting-edge system adds a predictive Apriori-based parallel auto-cleaning pulse filter, with automatic cleaning activated at a predetermined pressure difference. An adaptive neuro-fuzzy inference system-multi-layer perceptron integrated simulated annealing-Levenberg Marquardt algorithm has been created to handle mistakes in prediction and improve turbine parameters for greater energy efficiency. This complete strategy considerably increases energy efficiency while also improving air pressure drop prediction. A 200 MW power output, 70 % energy efficiency, 1.3 kg/kW-h fuel consumption, a small residual error (−0.4 mbar), and a pressure difference of only 3 mbar are just a few of the model's excellent performance indicators.