Prediction of burning performance and emissions indexes of a turboprop motor with artificial neural network

Abstract

Purpose The purpose of this paper is to estimate different air–fuel ratio motor shaft speed and fuel flow rates under the performance parameters depending on the indices of combustion efficiency and exhaust emission of the engine, a turboprop multilayer feed forward artificial neural network model. For this purpose, emissions data obtained experimentally from a T56-A-15 turboprop engine under various loads were used. Design/methodology/approach The designed multilayer feed forward neural network models consist of two hidden layers. 75% of the experimental data used was allocated as training, 25% as test data and cross-referenced by the k-fold four value. Fuel flow, rotate per minute and air–fuel ratio data were used for the training of emission index input values on the designed models and EICO, EICO2, EINO2 and EIUHC data were used on the output. In the system trained for combustion efficiency, EICO and EIUHC data were used at the input and fuel combustion efficiency data at the output. Findings Mean square error, normalized mean square error, absolute mean error functions were used to evaluate the error obtained from the system as a result of the test. As a result of modeling the system, absolute mean error values were 0.1473 for CO, 0.0442 for CO2, 0.0369 for UHC, 0.0028 for NO2, success for all exhaust emission data was 0.0266 and 7.6165e-10 for combustion efficiency, respectively. Originality/value This study has been added to the literature T56-A-15 turboprop engine for the current machine learning methods to multilayer feed forward neural network methods, exhaust emission and combustion efficiency index value calculation.