Artificial Neural Network Prediction of Diesel Engine Performance and Emission Fueled With Diesel–Kerosene–Ethanol Blends: A Fuzzy-Based Optimization

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Subrata Bhowmik ◽  
Rajsekhar Panua ◽  
Durbadal Debroy ◽  
Abhishek Paul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Specific Energy Consumption ◽  
Ann Model ◽  
Volume Percentage ◽  
Acceptable Error ◽  
Performance And Emission ◽  
Artificial Neural ◽  
The Impact

The present study explores the impact of ethanol on the performance and emission characteristics of a single cylinder indirect injection (IDI) Diesel engine fueled with Diesel–kerosene blends. Five percent ethanol is added to Diesel–kerosene blends in volumetric proportion. Ethanol addition to Diesel–kerosene blends significantly improved the brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), total hydrocarbon (THC), and carbon monoxide (CO) emission of the engine. Based on engine experimental data, an artificial neural network (ANN) model is formulated to accurately map the input (load, kerosene volume percentage, ethanol volume percentage) and output (BTE, BSEC, NOx, THC, CO) relationships. A (3-6-5) topology with Levenberg–Marquardt feed-forward back propagation (trainlm) is found to be optimal network than other training algorithms for predicting input and output relationship with acceptable error. The mean square error (MSE) of 0.000225, mean absolute percentage error (MAPE) of 2.88%, and regression coefficient (R) of 0.99893 are obtained from the developed model. The study also attempts to make clear the application of fuzzy-based analysis to optimize the network topology of ANN model.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

Artificial Neural Network-Based Prediction of Performances-Exhaust Emissions of Diesohol Piloted Dual Fuel Diesel Engine Under Varying Compressed Natural Gas Flowrates

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Abhishek Paul ◽  
Subrata Bhowmik ◽  
Rajsekhar Panua ◽  
Durbadal Debroy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Natural Gas ◽  
Goodness Of Fit ◽  
Specific Energy Consumption ◽  
Dual Fuel ◽  
Mean Square ◽  
Compressed Natural Gas ◽  
Artificial Neural

The present study surveys the effects on performance and emission parameters of a partially modified single cylinder direct injection (DI) diesel engine fueled with diesohol blends under varying compressed natural gas (CNG) flowrates in dual fuel mode. Based on experimental data, an artificial intelligence (AI) specialized artificial neural network (ANN) model have been developed for predicting the output parameters, viz. brake thermal efficiency (Bth), brake-specific energy consumption (BSEC) along with emission characteristics such as oxides of nitrogen (NOx), unburned hydrocarbon (UBHC), carbon dioxide (CO2), and carbon monoxide (CO) emissions. Engine load, Ethanol share, and CNG strategies have been used as input parameters for the model. Among the tested models, the Levenberg–Marquardt feed-forward back propagation with three input neurons or nodes, two hidden layers with ten neurons in each layer and six output neurons, and tansig-purelin activation function have been found to the optimal model topology for the diesohol–CNG platforms. The statistical results acquired from the optimal network topology such as correlation coefficient (0.992–0.999), mean square error (MSE) (0.0001–0.0009), and mean absolute percentage error (MAPE) (0.09–2.41%) along with Nash–Sutcliffe coefficient of efficiency (NSE), Kling–Gupta efficiency (KGE), mean square relative error, and model uncertainty established itself as a real-time robust type machine learning tool under diesohol–CNG paradigms. The study also incorporated a special type of measure, namely Pearson's Chi-square test or goodness of fit, which brings up the model validation to a higher level.

scholarly journals Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

2017 ◽  
Vol 26 (2) ◽  
pp. 74 ◽  
Author(s):  
Hasan Aydogan
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Diesel Fuel ◽  
Internal Combustion Engines ◽  
Back Propagation Algorithm ◽  
Performance And Emission ◽  
Fuel Blends ◽  
Artificial Neural

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.

scholarly journals Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 584
Author(s):  
Vinay Arora ◽  
Sunil Kumar Mahla ◽  
Rohan Singh Leekha ◽  
Amit Dhir ◽  
Kyungroul Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Activation Mechanism ◽  
Coefficient Of Determination ◽  
Dual Fuel ◽  
Biological Origin ◽  
Research Issues ◽  
Performance And Emission ◽  
Artificial Neural

Biogas is a significant renewable fuel derived by sources of biological origin. One of today’s research issues is the effect of biofuels on engine efficiency. The experiments on the engine are complicated, time consuming and expensive. Furthermore, the evaluation cannot be carried out beyond the permissible limit. The purpose of this research is to build an artificial neural network successfully for dual fuel diesel engine with a view to overcoming experimental difficulties. Authors used engine load, bio-gas flow rate and n-butanol concentration as input parameters to forecast target variables in this analysis, i.e., smoke, brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbon (HC), nitrous-oxide (NOx). Estimated values and results of experiments were compared. The error analysis showed that the built model has quite accurately predicted the experimental results. This has been described by the value of Coefficient of determination (R2), which varies between 0.8493 and 0.9863 with the value of normalized mean square error (NMSE) between 0.0071 and 0.1182. The potency of the Nash-Sutcliffe coefficient of efficiency (NSCE) ranges from 0.821 to 0.8898 for BTE, HC, NOx and Smoke. This research has effectively emulated the on-board efficiency, emission, and combustion features of a dual-fuel biogas diesel engine taking the Swish activation mechanism in artificial neural network (ANN) model.

Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization

2018 ◽  
Vol 29 (8) ◽  
pp. 1413-1437 ◽  
Author(s):  
Subrata Bhowmik ◽  
Rajsekhar Panua ◽  
Subrata K Ghosh ◽  
Abhishek Paul ◽  
Durbadal Debroy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Network Model ◽  
Neural Network Model ◽  
Diesel Fuel ◽  
Artificial Neural Network Model ◽  
Specific Energy Consumption ◽  
Oxides Of Nitrogen ◽  
Artificial Neural

This study evaluates the effects of diesel fuel adulteration on the performance and exhaust emission characteristics of an existing diesel engine. Kerosene is added to diesel fuel in volumetric proportions of 5, 10, 15, and 20%. Adulterated fuel significantly reduced the oxides of nitrogen emissions of the engine. In view of the engine experimentations, artificial intelligence-based artificial neural network model has been developed to accurately predict the input–output relationships of the diesel engine under adulterated fuel. The investigation also attempts to explore the applicability of fuzzy logic in the selection of the network topology of artificial neural network model under adulterated fuel. A (2–7–5) topology is found to be optimal for predicting input parameters, namely load, diesel–kerosene blend and output parameters, namely brake thermal efficiency, brake-specific energy consumption, oxides of nitrogen, total hydrocarbon, carbon monoxide of the network. The developed artificial neural network model is enabled for predicting engine output responses with high accuracy. The regression coefficient (R) of 0.99887, mean square error of 1.5e-04 and mean absolute percentage error of 2.39% have been obtained from the plausible artificial neural network model.

scholarly journals Simulation of Dye Synthesized Solar Cell using Artificial Neural Network

2019 ◽  
Vol 9 (2) ◽  
pp. 1316-1322
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Cell ◽  
Transfer Function ◽  
Short Circuit ◽  
Ann Model ◽  
Minimum Mean Squared Error ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
The Impact

The primary goal of present examination is to foresee every day worldwide solar cell efficiency in view of meteorological factors, utilizing distinctive counterfeit neural system (ANN) procedures. In the present examination we report the impact of Dye Synthesized solar cell. A three-layer artificial neural network (ANN) model was developed to predict the efficiency of Dye Synthesized solar cell based on 100 experimental sets. In the present examination we report the impact of Dye Synthesized solar cell. The effect of operational parameters such as short circuit current (Jsc),Open circuit voltage(Voc),Fill factor(FF) were studied to optimize the conditions to check the efficiency of Dye Synthesized solar cell. Experimental results showed that the ANN model was able to predict adsorption efficiency with a tangent sigmoid transfer function (tansig) at hidden layer with 20 neurons and a linear transfer function (purelin) at output layer. The Levenberg–Marquardt algorithm (LMA) was used with a minimum mean squared error (MSE) of 0.00350141. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient of about 0.9993 for six model variables used in this study

scholarly journals Modelling the Effect of Cu Content on the Microstructure and Vickers Microhardness of Sn-9Zn Binary Eutectic Alloy Using an Artificial Neural Network

Crystals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 481
Author(s):  
Heba Y. Zahran ◽  
Hany Nazmy Soliman ◽  
Alaa F. Abd El-Rehim ◽  
Doaa M. Habashy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Eutectic Alloy ◽  
Aging Temperature ◽  
Vickers Microhardness ◽  
Ann Model ◽  
Cu Content ◽  
Binary Eutectic ◽  
Artificial Neural ◽  
The Impact

The present study aims to clarify the impact of Cu addition and aging conditions on the microstructure development and mechanical properties of Sn-9Zn binary eutectic alloy. The Sn-9Zn alloys with varying Cu content (0, 1, 2, 3, and 4 wt.%) were fabricated by permanent mold casting. X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques were utilized to investigate the influence of Cu concentration on the microstructure of pre-aged Sn-9Zn-Cu alloys. The main phases are the primary β-Sn phase, eutectic α-Zn/β-Sn phases, and γ-Cu5Zn8/η-Cu6Sn5/ε-Cu3Sn intermetallic compounds. Vickers microhardness values of Sn-9Zn alloys increased with additions of 1 and 2 wt.% Cu. When the concentration of Cu exceeds 2 wt.%, the values of microhardness declined. Besides, the increase in the aging temperature caused a decrease in the microhardness values for all the investigated alloys. The variations in the microhardness values with Cu content and/or aging temperature were interpreted on the basis of development, growth, and dissolution of formed phases. The alterations of the lattice strain, dislocation density, average crystallite size, and stacking fault probability were evaluated from the XRD profiles of the investigated alloys. Their changes with Cu content and/or aging temperature agree well with the Vickers hardness results. An artificial neural network (ANN) model was employed to simulate and predict the Vickers microhardness of the present alloys. To check the adequacy of the ANN model, the calculated results were compared with experimental data. The results confirm the high ability of the ANN model for simulating and predicting the Vickers microhardness profile for the investigated alloys. Moreover, an equation describing the experimental results was obtained mathematically depending on the ANN model.

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