Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil

Çanakçı, Mustafa; Özsezen, Ahmet Necati; Arcaklıoğlu, Erol; Erdil, Ahmet

doi:10.1016/j.eswa.2008.12.005

Cited by 234 publications

(116 citation statements)

References 38 publications

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“…With the increased use of biodiesel fuel all over the world, many studies have been conducted on the performance of diesel engines using pure biodiesel or blended with diesel fuel. Most of these studies reported that engine effective power and brake thermal efficiency are lower and brake specific fuel consumption is higher for used frying oil methyl ester and its blends than diesel fuel, especially with increase of biodiesel in the blends (Canakci et al, 2009;Rao et al, 2008;Utlu and Kocak, 2008;Murillo et al, 2007;Sudhir et al, 2007;Dorado et al, 2003;Canakci and Van Gerpen, 2003;Prabhu et al, 2013). However, some studies reported that biodiesel could cause a slightly higher engine effective power than diesel fuel (Deepanraj et al, 2011;Usta et al, 2005;Najafi et al, 2007).…”

Section: Introductionmentioning

confidence: 97%

Energy Balance for a Diesel Engine Operates on a Pure Biodiesel, Diesel Fuel and Biodiesel-Diesel Blends

Al-Hasan¹,

AlGhamdi²

2016

American Journal of Engineering and Applied Sciences

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Abstract:In general, small part of fuel chemical energy transforms to effective power in diesel engines and other parts are going as energy losses, there for many researchers, as well as engine designers, are searching to increase this part by using various methods. In this study, the effect of pure biodiesel and biodiesel-diesel fuel blends on engine energy balance, compared with diesel fuel, has been performed. For this purpose, diesel engine coupled with a dynamometer, as well as, the thermocouples (to measure the temperatures in different locations) and the flow meter (to measure the water flow rate) have been used. In addition, biodiesel mixed with diesel fuel to obtain various proportions of the fuel blends. The engine heat losses through: Cooling water system, exhaust gases and by radiation have been calculated, on the base of experimental results. The results show that an increase of the biodiesel in the fuel blends lead to increase of heat losses. Moreover, the energy distribution from the fuel used for the experiments show a decrease in useful work, increase of heat lost with cooling water, heat lost with exhaust gases and by radiation by about of (average values) 7, 3.8, 4.8 and 4.5% respectively when compared with diesel fuel. The energy distributions were as 26, 27, 22 and 0.46% for useful work, heat losses with cooling water, with exhaust gases and by radiation, respectively.

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Section: Introductionmentioning

confidence: 97%

Energy Balance for a Diesel Engine Operates on a Pure Biodiesel, Diesel Fuel and Biodiesel-Diesel Blends

Al-Hasan¹,

AlGhamdi²

2016

American Journal of Engineering and Applied Sciences

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“…MAPE, RMSE, and R 2 performances are calculated as (Canakci et al, 2009) To choose the size of ANNs, MAPE, RMSE and R 2 are calculated for the testing data for different numbers of hidden neurons as shown in Fig. 6 and Table 5.…”

Section: Ignition Advance Timingmentioning

confidence: 99%

“…Ghobadian et al (2009) used a multilayer perceptron network (MLP) to model the brake power, output torque, specific fuel consumption, and exhaust emissions of a diesel engine. Canakci et al (2009) used an ANN model to predict the performance and exhaust emissions of a diesel engine including CO, NO x and unburned hydrocarbons. However, Saerens et al (2009) used a mean value model of an internal combustion engine instead of ANN to minimize the fuel consumption by acting on the throttle valve.…”

Section: Introductionmentioning

confidence: 99%

Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

Martínez-Morales

Palacios-Hernández

Velázquez-Carrillo

2013

J. Zhejiang Univ. Sci. A

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Abstract:In this paper, a multi-objective particle swarm optimization (MOPSO) algorithm and a nondominated sorting genetic algorithm II (NSGA-II) are used to optimize the operating parameters of a 1.6 L, spark ignition (SI) gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NO x ), which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks (ANNs) were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms: NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NO x , respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NO x , respectively.

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“…Canakci et al [22] studied a diesel engine fuelled with biodiesel produced from waste frying palm oil to predict the performance and exhaust emissions. Ghobadian et al [23] analysed the diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an ANN and were able to predict the engine performance and exhaust emissions successfully.…”

Section: Introductionmentioning

confidence: 99%

Prediction of marine diesel engine performance by using artificial neural network model

Noor¹,

Mamat²,

Najafi³

et al. 2016

J. Mech, Eng, Sci.

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This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the output torque, brake power, brake specific fuel consumption and exhaust gas temperature. The input data for network training was gathered from engine laboratory testing running at various engine speeds and loads. An ANN prediction model was developed based on a standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data and output from the mathematical model. The results showed that the ANN model provided good agreement with the experimental data with a coefficient of determination (R 2 ) of 0.99. The prediction error of the ANN model is lower than the mathematical model. The present study reveals that the artificial neural network approach can be used to predict the performance of a marine diesel engine with high accuracy.

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Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil

Cited by 234 publications

References 38 publications

Energy Balance for a Diesel Engine Operates on a Pure Biodiesel, Diesel Fuel and Biodiesel-Diesel Blends

Energy Balance for a Diesel Engine Operates on a Pure Biodiesel, Diesel Fuel and Biodiesel-Diesel Blends

Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

Prediction of marine diesel engine performance by using artificial neural network model

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