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.

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.

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 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 Effect of plastic oil addition on performance and emission characteristics of biogas-diesel dual fuel engine using taguchi method and prediction of performance parameter using artificial neural network

2021 ◽  
Vol 850 (1) ◽  
pp. 012033
Author(s):  
P. Laxmi Narasimha Raju ◽  
Manas ◽  
Pavan Sai A. ◽  
M B Shyam Kumar ◽  
Ayub Ahmed Janvekar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Taguchi Method ◽  
Research Work ◽  
Dual Fuel ◽  
Emission Characteristics ◽  
Fuel Blend ◽  
Performance And Emission ◽  
Marquardt Algorithm ◽  
Artificial Neural

Abstract Ever increasing usage of fossil fuels and dwindling natural resources led researchers to concentrate on investigating other sources which can satisfy our demands and reduce pollution levels. Present research work aims to investigate the performance and emission characteristics of plastic, diesel and biogas as fuel blend operated in a dual-fuel engine with biogas as a primary fuel and plastic oil – diesel blends as secondary fuel and also predict the output variable using artificial neural network. A modified four-stroke single cylinder CI engine was used for experiments conducted at varying load, percentage of plastic oil percentage in diesel and biogas flow rate. Based on the levels and factors a Taguchi L9 orthogonal matrix was designed to find the optimal combination of input indices. The signal to noise ratios in taguchi method were applied based on the desired output characteristics and according to the respective SNR ratios an ANOVA table was created to determine the major contributor effecting output parameters such as brake thermal efficiency, CO, HC NOx and smoke emissions. ANN model helped to predict BTE with same input parameters but with an increased set of sample data. Based on Gradient descent and Levenberg-Marquardt algorithm the ANN architecture was trained, validated and tested to predict the response with least error. The ANOVA calculated indicates load to be the prime factor effecting BTE and NOx emission

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