Prediction of Steam Turbine Performance as a Waste Heat Recovery Mechanism on Naturally Aspirated Spark Ignition Engine Using Artificial Neural Network

2016 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Kamarulhelmy Talib ◽  
Shamsul Anuar Shamsudin ◽  
Azma Putra ◽  
Mohd Tahir Musthafah ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Steam Turbine ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Recovery Mechanism ◽  
Turbine Performance

scholarly journals Prediction of generated power from steam turbine waste heat recovery mechanism system on naturally aspirated spark ignition engine using artificial neural network

2017 ◽  
Vol 22 (18) ◽  
pp. 5955-5964 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Kamarulhelmy Talib ◽  
Azma Putra ◽  
Ahmad Faris Ismail ◽  
Shamsul Anuar Shamsudin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Steam Turbine ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Recovery Mechanism ◽  
Artificial Neural

scholarly journals Prediction on Power Produced from Power Turbine as a Waste Heat Recovery Mechanism on Naturally Aspirated Spark Ignition Engine Using Artificial Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Abdul Hakim Rohhaizan ◽  
Ahmad Faris Ismail ◽  
Shamsul Anuar Shamsudin ◽  
Azma Putra ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Simulation Method ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Recovery Mechanism ◽  
Artificial Neural

The waste heat from exhaust gases represents a significant amount of thermal energy, which has conventionally been used for combined heating and power applications. This paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM) in a sedan car. The amount of heat energy from exhaust is presented and the experimental test results suggest that the concept is thermodynamically feasible and could significantly enhance the system performance depending on the load applied to the engine. However, the existence of WHRM affects the performance of engine by slightly reducing the power. The simulation method is created using an artificial neural network (ANN) which predicts the power produced from the WHRM.

scholarly journals Study on waste heat recovery from exhaust gas spark ignition (S.I.) engine using steam turbine mechanism

2016 ◽  
Vol 90 ◽  
pp. 01057 ◽  
Author(s):  
Kamarulhelmy Talib ◽  
Safarudin G. Herawan ◽  
Musthafah M. Tahir ◽  
Azma Putra ◽  
Shamsul A. Shamsudin
Keyword(s):  
Steam Turbine ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Spark Ignition ◽  
Exhaust Gas

scholarly journals Influences of Gasoline Fuels on Waste Heat Recovery Potentiality of a Spark Ignition Engine

2017 ◽  
Vol 10 (7) ◽  
pp. 1-5
Author(s):  
I. M. Yusri ◽  
R. Mamat ◽  
W. H. Azmi ◽  
Omar I. Awad ◽  
M. F. Othman ◽  
...  
Keyword(s):  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Spark Ignition ◽  
Spark Ignition Engine

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.

scholarly journals Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

2017 ◽  
Vol 21 (1 Part B) ◽  
pp. 401-412 ◽  
Author(s):  
Erdi Tosun ◽  
Kadir Aydin ◽  
Simona Merola ◽  
Adrian Irimescu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Catalytic Converter ◽  
Direct Injection ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Back Propagation Algorithm ◽  
Brake Mean Effective Pressure ◽  
Artificial Neural

This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

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