scholarly journals Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, an example from a North American Shale Gas Reservoir

2007 ◽  
Vol 2007 (1) ◽  
pp. 1-4 ◽  
Author(s):  
M. Reza Rezaee ◽  
Roger M. Slatt ◽  
Richard F. Sigal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Shale Gas ◽  
North American ◽  
Rock Properties ◽  
Gas Reservoir ◽  
Shale Gas Reservoir ◽  
Artificial Neural

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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