Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network (original) (raw)
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Performance and exhaust emissions of a gasoline engine using artificial neural network
2007
This study deals with artificial neural network (ANN) modelling of a gasoline engine to predict the brake specific fuel consumption, brake thermal efficiency, exhaust gas temperature and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a four-cylinder, four-stroke test engine was fuelled with gasoline having various octane numbers (91, 93, 95 and 95.3), and operated at different engine speeds and torques. Using some of the experimental data for training, an ANN model based on standard back-propagation algorithm for the engine was developed. Then, the performance of the ANN predictions were measured by comparing the predictions with the experimental results which were not used in the training process. It was observed that the ANN model can predict the engine performance, exhaust emissions and exhaust gas temperature quite well with correlation coefficients in the range of 0.983-0.996, mean relative errors in the range of 1.41-6.66% and very low root mean square errors. This study shows that, as an alternative to classical modelling techniques, the ANN approach can be used to accurately predict the performance and emissions of internal combustion engines.
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.
Applied Energy, 2010
This study investigates the use of artificial neural network (ANN) modelling to predict brake power, torque, break specific fuel consumption (BSFC), and exhaust emissions of a diesel engine modified to operate with a combination of both compressed natural gas CNG and diesel fuels. A single cylinder, four-stroke diesel engine was modified for the present work and was operated at different engine loads and speeds. The experimental results reveal that the mixtures of CNG and diesel fuel provided better engine performance and improved the emission characteristics compared with the pure diesel fuel. For the ANN modelling, the standard back-propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception network was used for non-linear mapping between the input and output parameters. It was found that the ANN model is able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.9884, 0.9838, 0.95707, and 0.9934 for the engine torque, BSFC, NO x and exhaust temperature, respectively.
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...
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.
Artificial Neural Network based Modelling of Internal Combustion Engine Performance
International Journal of Engineering Research and, 2016
The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create the model are load, engine speed (RPM), fuel flow rate (FFR) & air flow rate (AFR); contrariwise the output parameters that have been used are brake power (BP), brake thermal efficiency (BTE), volumetric efficiency (VE), brake mean effective pressure (BMEP) and brake specific fuel consumption (BSFC). To begin with, databank has been alienated into training sets and testing sets. At that juncture, an ANN based model has been developed using training dataset which is based on standard back-propagation algorithm. Subsequently, performance and validation of the ANN based models have been measured by relating the predictions with the experimental results. Correspondingly, four different statistical functions have been used to examine the performance and reliability of the ANN based models. Moreover, Garson equation has been used to estimate the relative importance of the four different input variables towards their specific output. The results of the model suggests that, ANN based model is impressively successful to forecast the performance parameters of diesel engines for different input variables with a greater degree of accurateness and to evaluate relative impact of input variables.
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.
Applied energy, 2010
This study presents an artificial neural network (ANN) model to predict the torque and brake specific fuel consumption of a gasoline engine. An explicit ANN based formulation is developed to predict torque and brake specific fuel consumption of a gasoline engine in terms of spark advance, throttle position and engine speed. The proposed ANN model is based on experimental results. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. An ANN model based on a back-propagation learning algorithm for the engine was developed. The performance and an accuracy of the proposed ANN model are found satisfactory. This study demonstrates that ANN is very efficient for predicting the engine torque and brake specific fuel consumption. Moreover, the proposed ANN model is presented in explicit form as a mathematical function.
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).
NATIONAL CONFERENCE ON PHYSICS AND CHEMISTRY OF MATERIALS: NCPCM2020
The objective of this work is to find the performance and emission parameter of different blends of Karanja biodiesel with diesel and compare these parameters with pure diesel. This study investigates the potential of Karanja oil as a source of biodiesel. The objective of this work is to find the performance and emission parameters of 10 %, 20 %, 30 %, 40 %, and 50 % of blends with biodiesel and compared various parameters with diesel. The results showed that Brake Thermal Efficiency (BTE) decreases with an increase in the percent of biodiesel and Brake Specific Fuel Consumption (BSFC) decreases with an increase in the percent of biodiesel. Hydrocarbon (HC) and carbon monoxide (CO) emission reduces with an increase in blend percent whereas Nitrous oxide (NOx) emission increases with an increase in blend percent. Neural networks obviate the need to use complex mathematically explicit formulas, computer models, and impractical and costly physical models. In this work we use Neurosolution software for prediction of performance and emission parameters, separate models were developed for performance parameters as well as emission parameters. To train network, load, blend percentage, calorific value, the viscosity of fuel & airfuel ratio was used as input value whereas engine performance parameters like brake thermal efficiency, brake specific fuel consumption & exhaust gas temperature were used as output value for performance model and engine exhaust emission such as NOx, CO, and HC values were used as the output parameters for emission model. Artificial Neural Network (ANN) results showed that there is a good correlation between the ANN predicted values and the experimental values for various engine performance and exhaust emission parameters. It is observed that the ANN model can predict the engine BTE, BSFC with a correlation coefficient of about 0.998435668, 0.990616392, and 0.993346689 respectively for performance model and emission model CO, HC and NOx predict with a correlation coefficient of 0.986098699, 0.991243454 & 0.9855593. NOMENCLATURE BTE Brake Thermal Efficiency Cp Specific heat at constant pressure HS Hybrid System BSFC Brake Specific Fuel Consumption Cv Specific heat at constant volume IP Indicated Power, kW HC Hydrocarbon D Diameter of cylinder Mf Mass of fuel in the cylinder CO Carbon monoxide DI Direct Injection MSE Mean Square Error NOx Oxides of Nitrogen, ppm Cv Specific heat at constant volume N Speed (RPM)