Performance and exhaust emissions of a gasoline engine using artificial neural network

Sayın, Cenk; Ertunç, H. Metin; Hoşöz, Murat; Kılıçaslan, İbrahim; Çanakçı, Mustafa

doi:10.1016/j.applthermaleng.2006.05.016

Cited by 236 publications

(92 citation statements)

References 16 publications

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“…Similarly, while predicting engine performance emissions with ANN, Sayin et al [167] found prediction performance with correlation coefficients in the range of 0.983-0.996, mean relative errors in the range of 1.41%-6.66%, and very small root mean square (RMS) values, and concluded that ANN was an alternative to classical modelling techniques. Arcaklioğlu and Çelıkten [168] conducted experiments with petroleum diesel in a turbo-charged four-cylinder diesel engine.…”

Section: Ann In Predicting Engine Emission and Performancementioning

confidence: 99%

The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

et al. 2013

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Abstract:Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN) modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.

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Section: Ann In Predicting Engine Emission and Performancementioning

confidence: 99%

The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

et al. 2013

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“…where a is the actual output, PO the predicted output, and N s the number of points in the data set [31,32]. In this study, an ANN model was employed to establish a correlation between the input parameters and the engine performance and emissions as the output parameters.…”

Section: Arti Cial Neural Networkmentioning

confidence: 99%

“…The performance of the ANN-based predictions is evaluated by the regression analysis of the network outputs (predicted parameters) and the experimental values [31,32]. The criterion for performance evaluation of the ANN is the absolute fraction of variance, which is:…”

Section: Arti Cial Neural Networkmentioning

confidence: 99%

Performance and emissions optimization of an ethanol-gasoline fueled SI engine with oxygen enrichment using artificial bee colony algorithm

2017

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Abstract. The use of arti cial neural network in conjunction with arti cial bee colony algorithm is proposed as a method for performance and emissions optimization of an SI engine. The case study here involves the oxygen enriched combustion of an SI engine fueled with hydrous ethanol and gasoline. In this study, the engine was considered as a black box and its performance and emissions were extracted experimentally at di erent intake air oxygen concentrations, hydrous ethanol injection rates, and ethanol concentration in the hydrous ethanol mixture. Then, the simultaneous injection of hydrous ethanol and oxygen enriched combustion was investigated to maximize the fuel conversion e ciency and minimize the CO and NOx emissions. Therefore, an objective function consisting of both the emission and performance parameters was optimized using the Arti cial Bee Colony algorithm. The engine model used in this optimization process was obtained from an Arti cial Neural Network trained with experimental engine data. For operating speed of 3000 rpm, the optimization results indicated 1.21% improvement in fuel conversion e ciency and 31.11% and 13.94% reduction in CO and NOx emissions, respectively. At the speed of 2000 rpm, fuel conversion e ciency improved by 4.11% and CO emission decreased by 18.73%, while NOx concentration increased by 28.35%.

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“…Usually the basic shapes used in back-propagation models are feed-forward and Levenberg-Marquart (LM) with training function tanh sigmoid that is (Jha, 2004;Sayin et al, 2007) …”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Optimization and Prediction of Motorcycle Injection System Performance with Feed-Forward Back-Propagation Method Artificial Neural Network (ANN)

Syahrullah¹,

Sinaga²

2016

American Journal of Engineering and Applied Sciences

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This research studied the use of Artificial Neural Network (ANN) using feed-forward back-propagation model to optimize and predict the performance of a motorcycle fuel injection systems of gasoline. The parameters such as speed, throttle position, ignition timing and injection timing is used as the input parameters. While the parameters of fuel consumption and engine torque is used as the output layer. LavenbergMarquardt model type with train function tanh sigmoid and 25 neurons number is used to generate the target value and the desired output. Variation of ignition timing as optimization variable in a wide range of speed and throttle position is used in experimental tests. ANN is used to investigate the prediction of performance motorcycle engines and compared with the test results. Results showed that the operation of ANN in predicting engine performance is very good. From the test results obtained a smooth contour MAP compared to the initial state. The prediction result and performance test show a good correlation in small error value of training and test that is regression with range 0.98-0.99, mean relative error with range 0.1315-0.4281% and the root mean square error with range 0.2422-0.9754%. This study shows that the feed-forward back propagation on ANN model can be used to predict accurately the performance of a motorcycle engine injection system.

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Performance and exhaust emissions of a gasoline engine using artificial neural network

Cited by 236 publications

References 16 publications

The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

Performance and emissions optimization of an ethanol-gasoline fueled SI engine with oxygen enrichment using artificial bee colony algorithm

Optimization and Prediction of Motorcycle Injection System Performance with Feed-Forward Back-Propagation Method Artificial Neural Network (ANN)

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