Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups (original) (raw)
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alexandria engineering journal, 2016
This study deals with usage of linear regression (LR) and artificial neural network (ANN) modeling to predict engine performance; torque and exhaust emissions; and carbon monoxide, oxides of nitrogen (CO, NOx) of a naturally aspirated diesel engine fueled with standard diesel, peanut biodiesel (PME) and biodiesel-alcohol (EME, MME, PME) mixtures. Experimental work was conducted to obtain data to train and test the models. Backpropagation algorithm was used as a learning algorithm of ANN in the multilayered feedforward networks. Engine speed (rpm) and fuel properties, cetane number (CN), lower heating value (LHV) and density (q) were used as input parameters in order to predict performance and emission parameters. It was shown that while linear regression modeling approach was deficient to predict desired parameters, more accurate results were obtained with the usage of ANN.
World Journal of Engineering, 2021
Artificial neural network model was constructed to analyse and evaluate the engine performance. The experiments were conducted on a diesel engine with the blend of plastic pyrolysis oil with diesel and ethanol. Three input layer with two hidden layers and five output layers were used in artificial neural network modelling. The learning algorithm called feed-forward back-propagation was applied for the hidden layer. To train the neural network, 70% of the complete data from the experimentation was selected and 30% in predicting from the neural network. The model developed for prediction has excellent agreement as observed from the correlation coefficient (R) within the range of 0.964-0.9816. Statistical analysis shows that the ANN predicted and experimental results are in close agreement with each other. Overall, it could be concluded that it is a mean to predict the virtual sensing in studying the real time with established artificial neural network architecture. In addition, common rail direct injection engine operation could give complete freedom from diesel and thereby provides energy security and sustainable of a nation.
The ability of an artificial neural network model, using a back propagation learning algorithm, to predict specific fuel consumption and exhaust temperature of a Diesel engine for various injection timings is studied. The proposed new model is compared with experimental results. The comparison showed that the consistence between experimental and the network results are achieved by a mean absolute relative error less than 2%. It is considered that a well-trained neural network model provides fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.
Energy, 2013
This study investigates the use of ANN (artificial neural networks) modelling to predict BSFC (break specific fuel consumption), exhaust emissions that are CO (carbon monoxide) and HC (unburned hydrocarbon), and AFR (airefuel ratio) of a spark ignition engine which operates with methanol and gasoline. To obtain training and testing data, a number of experiments were performed with a fourcylinder, four-stroke test engine operated at different engine speeds and torques. The experimental results reveal that the methanol improved the emission characteristics compared with the gasoline. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, four different learning algorithms were used such as BFGS (Quasi-Newton back propagation), LM (LevenbergeMarquardt learning algorithm). It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.998621, 0.977654, 0.998382 and 0.996075 for the BSFC, CO, HC and AFR for testing data, respectively. It was obvious that the developed ANN model is fairly powerful for predicting the brake specific fuel consumption and exhaust emissions of internal combustion engines.
Renewable Energy, 2009
This study deals with artificial neural network (ANN) modeling a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of engine. To acquire data for training and testing the proposed ANN, two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results reveal that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model based on standard Back-Propagation algorithm for the engine was developed. Multi layer perception network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) were 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values by the model was obtained as 0.0004. Talal (2009) U Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network.U Renewable Energy, 34 (4).
Application of Neural Network for Estimating Properties of Diesel–Biodieselblends
International Journal of Computers and Applications, 2010
Soybean oil was transesterified with methanol in the presence of alkaline catalyst to produce methyl esters commonly known as biodiesel. Biodiesel and diesel blends were prepared and tested in laboratory for flash point, fire point, viscosity and density. Seven neural network architectures, three training algorithms along with ten different sets of weight and biases were examined to predict the above-mentioned properties of diesel and biodiesel blends. The best suited neural network and training algorithm were selected and further generalized to improve its performance by using early stopping technique. The results showed that the neural network having an architecture 2-7-4 with Levernberg-Marquardt algorithm gave the best estimate for the properties of diesel-biodiesel blends.
This study deals with predicting various performance parameters and exhaust emissions of a four-stroke, four-cylinder, direct injection diesel engine fuelled with soybean oil methyl ester (SME) and its 5%, 20% and 50% blends with jet fuel, marine fuel and No.2 diesel fuel using artificial neural networks (ANNs). In order to acquire data for training and testing the proposed ANN, the test engine was operated at steady-state conditions while varying the engine speed and torque for each fuel case. Using some of the experimental data for training, an ANN model based on standard back propagation algorithm for the engine was developed. This model was used to predict various performance parameters and exhaust emissions of the engine, namely the brake specific fuel consumption, break thermal efficiency, mechanical efficiency, exhaust gas temperature, and emissions of CO, NO x , and CO 2. Then, the performance of the ANN predictions were measured by comparing the predictions with the experim...
Prediction of Cetane Number and Ignition Delay of Biodiesel Using Artificial Neural Networks
Energy Procedia, 2014
This work deals with obtaining models for predicting the cetane number and ignition delay using artificial neural networks. Models for the estimation of the cetane number of biodiesel from their methyl ester composition and ignition delay of palm oil and rapeseed biodiesel using artificial neural networks were obtained. For the prediction of the cetane number model, 38 biodiesel fuels and 10 pure fatty acid methyl esters from the available literature were given as inputs. The best neural network for predicting the cetane number was a conjugate gradient descend (11:4:1) showing 96.3 % of correlation for the validation data and a mean absolute error of 1.6. The proposed network is useful for prediction of the cetane number of biodiesel in a wide range of composition but keeping the percent of total unsaturations lower than 80 %. The model for prediction of the ignition delay was developed from 5 inputs: cetane number, engine speed, equivalence ratio, mean pressure and temperature. The results showed that the neural network corresponding to a topology (5:2:1) with a back propagation algorithm gave the best prediction of the ignition delay. The correlation coefficient and the mean absolute error were 97.2% and 0.03 respectively. The models developed to predict cetane number and ignition delay using artificial neural networks showed higher accuracy than 95 %. Hence, the ANN models developed can be used for the prediction of cetane number and ignition delay of biodiesel.
Artificial neural networks used for the prediction of the cetane number of biodiesel
Renewable Energy, 2006
Cetane number (CN) is one of the most significant properties to specify the ignition quality of any fuel for internal combustion engines. The CN of biodiesel varies widely in the range of 48-67 depending upon various parameters including the oil processing technology and climatic conditions where the feedstock (vegetable oil) is collected. Determination of the CN of a fuel by an experimental procedure is a tedious job for the upcoming biodiesel production industry. The fatty acid composition of base oil predominantly affects the CN of the biodiesel produced from it. This paper discusses the currently available CN estimation techniques and the necessity of accurate prediction of CN of biodiesel. Artificial Neural Network (ANN) models are developed to predict the CN of any biodiesel. The present paper deals with the application of multi-layer feed forward, radial base, generalized regression and recurrent network models for the prediction of CN. The fatty acid compositions of biodiesel and the experimental CNs are used to train the networks. The parameters that affect the development of the model are also discussed. ANN predicted CNs are found to be in agreement with the experimental CNs. Hence, the ANN models developed can be used reliably for the prediction of CN of biodiesel.
Engineering Science and Technology, an International Journal, 2018
In the present study, the performance and exhaust emissions of a single-cylinder, direct-injection and air-cooled diesel engine using diethyl ether (DEE)-diesel fuel mixtures were estimated by artificial neural networks (ANN). The test engine was run with pure diesel and diesel-DEE blends at different engine speeds and loads to obtain the test and training data required to build the ANN model. In the designed ANN model, brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), brake thermal efficiency (BTE), nitrogen oxides (NO x), hydrocarbons (HC), carbon monoxides (CO) and smoke were selected as the output layer while engine load, engine speed and fuel blending ratio were selected as input layer. An ANN model was developed using 75% of the experimental results for training. The performance of the ANN model was measured by comparing the test data generated from the unused part of the training. According to the obtained data, ANN model predicts exhaust emissions and engine performance with a regression coefficient (R2) at 0.964-0.9878 interval. At the same time, mean relative error (MRE) values ranged from 0.51% to 4.8%. These results show that the ANN model is able to use for estimating low-power diesel engine emissions and performance.