scholarly journals Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and Killer Whale algorithm

2019 ◽  
Author(s):  
Totok R. Biyanto ◽  
Muhammad F. Auliya ◽  
Ronny D. Noriyati ◽  
Hendra Cordova ◽  
Martadji ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Consumption ◽  
Gasoline Engine ◽  
Killer Whale ◽  
Specific Fuel Consumption ◽  
Brake Specific Fuel Consumption ◽  
Brake Power ◽  
Artificial Neural

scholarly journals Optimization of Gasoline Engine to Maximize Brake Power and Minimize Brake Specific Fuel Consumption with Artificial Neural Network and Killer Whale Algorithm

2017 ◽  
Author(s):  
Totok R. Biyanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Consumption ◽  
Gasoline Engine ◽  
Killer Whale ◽  
Combustion Engine ◽  
Specific Fuel Consumption ◽  
Brake Specific Fuel Consumption ◽  
Marquardt Algorithm ◽  
Artificial Neural

In Formula Society of Automotive Engineering (SAE) competition, the design of efficient and powerful combustion engine is required. This paper discussed optimization of gasoline engine using Killer Whale algorithm. The modelling of gasoline engine was built using Multi-Layer Perceptron - Artificial Neural Network (MLP-ANN). A gasoline engine was simulated using Ricardo Wave commercial software to acquire data for training and testing the proposed ANN. The ANN weights were determined by utilizing Levenberg-Marquardt algorithm. The objective function in this paper is to maximize power, minimize the Brake Specific Fuel Consumption (BSFC) and minimize the operational cost. The optimized variables are engine speed (rpm), Air Fuel Ratio (AFR), Mass Fuel Flow (MFF), Intake Pressure (IP), Intake Air Temperature (IAT), Combustion Start (CS) and Throttle Angle (TA). Root Mean Square Error (RMSE) of ANN modelling is 0.021 kW for power and 0.00032 kg/kW.hr for BSFC. The optimization results show that the power increases to 13%, BSFC decreases to 11% and the cost operation decreases to 23% compare with existing design variables.

The Application of Artificial Neural Network in Prediction of the Performance of Spark Ignition Engine Running on Ethanol-Petrol Blends

2017 ◽  
Vol 12 ◽  
pp. 15-31 ◽  
Author(s):  
Olisaemeka C. Nwufo ◽  
Modestus Okwu ◽  
Chidiebere F. Nwaiwu ◽  
Johnson O. Igbokwe ◽  
O. Martin I. Nwafor ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Consumption ◽  
Spark Ignition ◽  
Combustion Efficiency ◽  
Spark Ignition Engine ◽  
Specific Fuel Consumption ◽  
Maximum Pressure ◽  
Brake Specific Fuel Consumption ◽  
Artificial Neural

The performance analysis of a single cylinder spark ignition engine fuelled with ethanol – petrol blends were carried out successfully at constant load conditions. E0 (Petrol), E10 (10% Ethanol, 90% Petrol), E20 (20% Ethanol, 80% Petrol) and E30 (30% Ethanol, 70% Petrol) were used as fuel. The Engine speed, mass flow rate, combustion efficiency, maximum pressure developed, brake specific fuel consumption and Exhaust gas temperature values were measured during the experiment. Using the experimental data, a Levenberg Marquardt Artificial Neural Network algorithm and Logistic sigmoid activation transfer function with a 4–10–2 model was developed to predict the brake specific fuel consumption, maximum pressure and combustion efficiency of G200 IMEX spark ignition engine using the recorded engine speed, mass flow rate, biofuels ratio and exhaust gas temperature as input variables. The performance of the Artificial Neural Network was validated by comparing the predicted data with the experimental results. The results showed that the training algorithm of Levenberg Marquardt was sufficient enough in predicting the brake specific fuel consumption, combustion pressure and combustion efficiency of the test engine. Correlation coefficient values of 0.974, 0.996 and 0.995 were obtained for brake specific fuel consumption, combustion efficiency and pressure respectively. These correlation coefficient obtained for the output parameters are very close to one (1) showing good correlation between the Artificial Neural Network predicted results and the experimental data while the Mean Square Errors were found to be very low (0.00018825 @ epoch 10 for brake specific fuel consumption, 1.0023 @ epoch 3 for combustion efficiency and 0.0013284@ epoch 5 for in-cylinder pressure). Therefore, Artificial Neural Network toolbox called up from MATLAB proved to be a useful tool for simulation of engine parameters. Artificial Neural Network model provided accurate analysis of these complex problems and has been found to be very useful for predicting the performance of the spark ignition engine. Thus, this has proved that Artificial Neural Network model could be used for predicting performance values in internal combustion engines, in this way it would be possible to conduct time and cost efficient studies instead of long experimental ones.

PFI Retrofit-Kit as Green Technology for Small 4-Stroke S.I. Engine

2013 ◽  
Vol 315 ◽  
pp. 453-457 ◽  
Author(s):  
Mohd Faisal Hushim ◽  
Ahmad Jais Alimin ◽  
Hazlina Selamat ◽  
Mohd Taufiq Muslim
Keyword(s):  
Carbon Dioxide ◽  
Carbon Monoxide ◽  
Fuel Consumption ◽  
Gasoline Engine ◽  
Green Technology ◽  
Specific Fuel Consumption ◽  
Brake Specific Fuel Consumption ◽  
Effective Pressure ◽  
Brake Power ◽  
Brake Mean Effective Pressure

This paper presents outcomes of the usage of a developed prototype of PFI retrofit-kit for small 4-stroke gasoline engine. The developed PFI retrofit-kit produced good and high brake power and brake mean effective pressure compared to the carburetor system with over 50% improvement. Exhaust-out emissions such as carbon monoxide, carbon dioxide and hydrocarbon have been reduced in the range of 39%, 185%, and 57% respectively. However, brake specific fuel consumption was found to be higher (125%) as compared to carburetor system.

Prediction of ship fuel consumption by using an artificial neural network

2018 ◽  
Vol 32 (12) ◽  
pp. 5785-5796 ◽  
Author(s):  
Miyeon Jeon ◽  
Yoojeong Noh ◽  
Yongwoo Shin ◽  
O-Kaung Lim ◽  
Inwon Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Consumption ◽  
Artificial Neural

Performance of a Compression-Ignition Engine Fueled with Diesel/Biodiesel Blends

2015 ◽  
Vol 730 ◽  
pp. 283-286
Author(s):  
Rong Fu Zhu ◽  
Yun Long Wang ◽  
Hui Wang ◽  
Yuan Tao Sun
Keyword(s):  
Fuel Consumption ◽  
Specific Fuel Consumption ◽  
Experimental Results ◽  
Nox Emission ◽  
Brake Specific Fuel Consumption ◽  
Compression Ignition ◽  
Compression Ignition Engine ◽  
Biodiesel Blends ◽  
Brake Power ◽  
Fuel Delivery

The performance of engine fueled with diesel/biodiesel blends was tested. It was indicated from the experimental results that the brake power, torque out and brake specific fuel consumption of engine fueled with diesel/biodiesel caused slight variations, while NOx emission increased significantly compared with engine fueled with diesel. In order to reduce NOx emission of engine fueled with pure biodiesel, retarding fuel delivery advance angle was used, and the NOx emission tests revealed that the NOx emission decreased significantly at different engine speeds.

scholarly journals An Educational Visual C++ Program Aid to Estimate Fuel Consumption for Tillage Equipment Based on Trained Artificial Neural Network

2010 ◽  
Vol 67 (1) ◽  
Author(s):  
Abdulrahman AL-JANOBI ◽  
Saad AL-HAMED ◽  
Abdulwahed ABOUKARIMA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Consumption ◽  
Undergraduate Students ◽  
Field Experiments ◽  
Soil Conditions ◽  
C Programming Language ◽  
C Programming ◽  
Artificial Neural ◽  
User Friendly

An educational program was developed to assist graduate and undergraduate students to estimate fuel consumption for tillage equipment. It supports to select the appropriate power of an agricultural tractor to operate with a particular tillage implement in specific operation and soil conditions to minimize fuel consumption. The program was written in visual C++ programming language. The program was based on training library of an artificial neural network. The program offers an educational help and clarification to most of the affecting parameters on fuel consumption. The program was validated by comparing predicted fuel consumption with the results obtained during field experiments. The program has proven to be very user-friendly and efficient to meet the requirement.

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