Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/3307
Title: Metaheuristics Optimized Machine Learning Modelling of Environmental Exergo-Emissions for an Aero-Engine [2022]
Authors: Baklacıoğlu, Tolga
Turan, Önder
Aydın, Hakan
Keywords: turboprop
exergy emission
artificial neural networks
genetic algorithms
Artificial Neural-Network
Sustainability Indicators
Genetic Algorithm
Energy Efficiency
Fuel Consumption
Turboprop Engine
Turbofan Engine
Aircraft
Prediction
Generation
Issue Date: 2019
Publisher: Walter De Gruyter Gmbh
Abstract: The purpose of this study is to develop a metaheuristic design for primary parameters and architectures of two models of artificial neural network (ANN) in predicting a cargo aircraft's exergo-emissions (exergy destruction ratio, r(ex,dest) , and waste exergy ratio, r(wex) ) at different flight stages. Hybrid genetic algorithm (GA)-ANN models have been accomplished utilizing real databases of r(ex,dest) and r(wex) at various powers. Implementing a metaheuristics-based optimization on multilayer perceptron (MLP)-ANNs has produced the most favourable initial weights, step-size, biases, and training algorithm's back-propagation (BP) momentum rate in addition to optimum number of neurons in the hidden layer(s). In accordance with an error assessment, a close fit linking real data and r(wex) (linear correlation ratio, R, value of 0.999851) as well as r(ex,dest) (R value of 0.999985) predicted values is found. In the r(ex, dest) estimation model, the accuracy among single-hidden-layer networks has been confirmed to be higher; whereas, highly accurate testing outcomes have been obtained in two-hidden-layer networks as far as modeling of r(wex) is concerned. ANN models' optimization by GAs has increased the accuracy of the resulting models (R value of 0.999987 and 0.999869 for r(ex,dest) and r(wex) , in that order ascertaining a drop-off in the testing stage errors).
URI: https://doi.org/10.1515/tjj-2019-0037
https://hdl.handle.net/20.500.13087/3307
ISSN: 0334-0082
2191-0332
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu
Uçak Gövde ve Motor Bakımı Bölümü Kolekiyonu
WoS İndeksli Yayınlar Koleksiyonu

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