Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine

Parlak, Adnan; İslamoğlu, Yaşar; Yaşar, Halit; Egrisogut, Aysun

doi:10.1016/j.applthermaleng.2005.10.006

Cited by 202 publications

(94 citation statements)

References 10 publications

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“…Still in other studies, the performance and emission of engines were predicted by the application of Artificial Neural Networks (ANNs) [33,34]. Parlak, Islamoglu [35] used ANNs to predict specific fuel consumption and exhaust temperature for a diesel engine. Ghobadian, Rahimi [36]analysed the diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an ANN.…”

Section: Introductionmentioning

confidence: 99%

Effect of the Injection Timing on the Performance of a Diesel Engine Using Diesel-Biodiesel Blends

Rostami¹,

Ghobadian²,

Kiani³

2014

Int. J. Automot. Mech. Eng.

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In this study, the effect of fuel injection timing on the performance of a diesel engine was investigated experimentally and analytically using diesel-biodiesel blends. Different experiments were carried out on the diesel engine at engine speeds of 1200, 1600, 2000 and 2400 rpm. The injection timing was regulated for 10, 15 and 20 degree crank angle before top dead centre. The experimental results of engine torque, brakespecific fuel consumption (BSFC), cylinder pressure, and exhaust gas temperature for fuel blends of B20, B40, and B100 at different engine speeds and injection timings were recorded. The results showed that advancing the fuel injection timing for fuel blends of B20, B40, and B100 increased engine torque by approximately 2.1%, 2.9% and 6.3%, respectively, and decreased maximum BSFC by approximately 2.7%, 3.3% and 6.6%, respectively. Then, an artificial neural network (ANN) was used to predict engine performance. The injection timing, engine speed, and fuel blends were used as input parameters whereas engine performance parameters such as engine torque, BSFC, peak cylinder pressure, and exhaust gas temperature were used as the output parameters. The results showed that an ANN is a good tool to predict engine performance.

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Section: Introductionmentioning

confidence: 99%

Effect of the Injection Timing on the Performance of a Diesel Engine Using Diesel-Biodiesel Blends

Rostami¹,

Ghobadian²,

Kiani³

2014

Int. J. Automot. Mech. Eng.

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“…They have developed an ANN model for the prediction of engine power, CO, CO 2 , HC and O 2 emissions. Parlak et al [170] also successfully used ANN to predict diesel engine brake specific fuel consumption and NO x emission for a Ricardo E6 type, single-cylinder diesel engine considering engine speed, break mean effective pressure and fuel injection timing as input variables.…”

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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“…Yang et al [12] and Ramadhas et al [13] used ANNs for predicting the cetane number for the mixtures of fuels, while Lee et al [14] used ANNs for modelling the range of fuel injection to the engine cylinder chamber. ANNs were also used in the combustion process models to reduce the cost of the algorithm calculation [15] - [21], and for determining specific fuel consumption [22,23], combustion process temperature [24], air/fuel equivalence ratio [25], the emission of carbon oxide and hydrocarbons [26] - [28], and even failures of piston engines [29].…”

Section: Introductionmentioning

confidence: 99%

ANN based evaluation of the NOx concentration in the exhaust gas of a marine two-stroke diesel engine

Kowalski¹

2009

Polish Maritime Research

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Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine

Cited by 202 publications

References 10 publications

Effect of the Injection Timing on the Performance of a Diesel Engine Using Diesel-Biodiesel Blends

Effect of the Injection Timing on the Performance of a Diesel Engine Using Diesel-Biodiesel Blends

The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks

ANN based evaluation of the NOx concentration in the exhaust gas of a marine two-stroke diesel engine

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