Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network (original) (raw)
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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.
Journal of Energy in Southern Africa, 2017
The changes in the performance, emission and combustion characteristics of bioethanol-safflower biodiesel and diesel fuel blends used in a common rail diesel engine were investigated in this experimental study. E20B20D60 (20% bioethanol, 20% biodiesel, 60% diesel fuel by volume), E30B20D50, E50B20D30 and diesel fuel (D) were used as fuel. Engine power, torque, brake specific fuel consumption, NOx and cylinder inner pressure values were measured during the experiment. With the help of the obtained experimental data, an artificial neural network was created in MATLAB 2013a software by using back-propagation algorithm. Using the experimental data, predictions were made in the created artificial neural network. As a result of the study, the correlation coefficient was found as 0.98. In conclusion, it was seen that artificial neural networks approach could be used for predicting performance and emission values in internal combustion engines.
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.
Due to the increasing demand for fossil fuels and environmental threat due to pollution a number renewable sources of energy have been studied worldwide. In the present investigation influence of injection timing, injection pressure, compression ratio and load on the performance of a single cylinder diesel engine are studied using pyrolysis oil as the biodiesel blended with diesel. To train the network, injection timing, injection pressure, compression ratio, load, are used as the input parameters where as engine performance parameter like brake specific Fuel consumption (BSFC), and is used as the output parameter for the performance model. The tests are performed at five different injection timings (20º, 22º, 23º, 24º, 25º CA BTDC), five injection pressure (140, 160, 180, 200, 220 bar), five compression ratio (18, 17, 16, 15, 14), five load . This study investigates the use of artificial neural network (ANN) modeling to predict break specific fuel consumption(BSFC).The experimental results reveal that the mixtures of Pyrolysis oil and diesel fuel provided better engine performance and reduce break specific fuel consumption (BSFC) compared with the pure diesel fuel. For the ANN modeling, the standard back-propagation algorithm is found to be the optimum choice for training the model. A multi-
Applied Energy, 2009
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.
2016
Biofuels or biodiesels are fuels that are in biodegradable and non-toxic. They are manufactured from waste cooking oils, vegetable oils and animal fats or tall oil (a by-product of the pulp and paper industry). These oils undergo a procedure called transesterification whereby they are subjected to a reaction with an alcohol usually ethanol or methanol by means of a catalyst such as sodium hydroxide. This results formation of ethyl or methyl ester called biodiesel and a by-product called glycerin. Pure biodiesel fuel is considerably not as much of flammable than petroleum diesel which burns at 50 degrees Celsius. Biodiesels are frequently used in blend with petroleum diesel and are named as biodiesel blends. These blends will contain a flashpoint and a gel point wherever between the two pure fuels depending on the mixture. Artificial neural networks (ANNs) are recently developed techniques which are in variably used in obtaining exact correlations which involves non-linear data. An A...
Performance and exhaust emissions of a biodiesel engine
Applied Energy, 2006
In this study, the applicabilities of Artificial Neural Networks (ANNs) have been investigated for the performance and exhaust-emission values of a diesel engine fueled with biodiesels from different feedstocks and petroleum diesel fuels. The engine performance and emissions characteristics of two different petroleum diesel-fuels (No. 1 and No. 2), biodiesels (from soybean oil and yellow grease), and their 20% blends with No. 2 diesel fuel were used as experimental results. The fuels were tested at full load (100%) at 1400-rpm engine speed, where the engine torque was 257.6 Nm. To train the network, the average molecular weight, net heat of combustion, specific gravity, kinematic viscosity, C/H ratio and cetane number of each fuel are used as the input layer, while outputs are the brake specific fuel-consumption, exhaust temperature, and exhaust emissions. The back-propagation learning algorithm with three different variants, single layer, and logistic sigmoid transfer function were used in the network. By using weights in the network, formulations have been given for each output. The network has yielded R 2 values of 0.99 and the mean % errors are smaller than 4.2 for the training data, while the R 2 values are about 0.99 and the mean % errors are smaller than 5.5 for the test data. The performance and exhaust emissions from a diesel engine, using biodiesel blends with No. 2 diesel fuel up to 20%, have been predicted using the ANN model.
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.