FUEL CONSUMPTION OPTIMIZATION OF GAS TURBINE GENERATOR BASED ON ARTIFICIAL INTELLIGENCE FOR MULTIVARIABLE CONTROL PERFORMANCE IMPROVEMENT

Risma Yudhanto, Risma (2020) FUEL CONSUMPTION OPTIMIZATION OF GAS TURBINE GENERATOR BASED ON ARTIFICIAL INTELLIGENCE FOR MULTIVARIABLE CONTROL PERFORMANCE IMPROVEMENT. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

A Gas Turbine is a rotary engine that extracts energy from a flow combustion gas. The reliability and efficiency of gas turbines is one of the top priorities at Saka Indonesia Pangkah Ltd (SIPL). In order to optimize the operating conditions of a gas turbine, three components are needed. First is the problem formulation which consists of objective functions, problem boundaries or contrains and determination of optimized variables. Second component is a valid model, which represents the characteristics of a gas turbine installed in SIPL. Third component is the optimization technique that is suitable with the optimization problem that will be solved. In this tesis, the objective function is maximizing gas turbine efficiency, some operational limitation as constrains by manipulating air to fuel ratio. The model was developed using Artificial Neural Network (ANN) and Genetic Algorithm (GA) was selected as the stochastic optimization technique to solve the problem. The neural network model created directly using the operational data from an actual parameter gas turbine generator. The data needed for ANN-based modeling is around 8150 data sets that will be used to train and validate the ANN model. Variable data sets were divided in two parts, for training purposes is 87.5% and for validation is 12.5%. Weight management for neural networks was carried out using Levenberg-Marquardt algorithm which could give good results with RMSE = 7.3 X 10-3. From the results of the stochastic optimization (GA) simulation, the potential reduction of fuel gas consumption is around 280.8 kg/hr if the air mass flow can be increased from 2.4 kg/s to 2.7 kg/s, efficiency increase up to 10.6%.

Item Type: Thesis (Masters)
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ266 Turbines. Turbomachines (General)
Divisions: Faculty of Industrial Technology > Physics Engineering > 30101-(S2) Master Thesis
Depositing User: Risma Yudhanto
Date Deposited: 06 Aug 2020 08:12
Last Modified: 06 Aug 2020 08:12
URI: https://repository.its.ac.id/id/eprint/77078

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