Prediction of performance of Stirling engine using least squares support machine technique

Stirling engine is an environmental friendly heat engine which could reduce CO2 emission through combustion process. Output power, shaft torque and brake specific fuel consumption represent the efficiency and robustness of the Stirling engines. The present research tries to determine the three aforementioned parameters with high accuracy and low uncertainty. In this research a new type of intelligent models named “least square support vector machine (LSSVM) was employed to predict output power, shaft torque and brake specific fuel consumption. Furthermore, high accurate actual values of the required parameters from previous studies were implemented to develop the robust intelligent model. A great advantage of LSSVM model over ANN is that in the present model over fitting does not happen. Expected statistical parameters of the suggested intelligent model have been indicated and validate the high efficiency of the suggested LSSVM model. Good agreement between LSSVM results and actual values was observed. Solutions obtained from the developed support vector machine model could help us in exact designing of Stirling engine with low uncertainty.

[1]  Colin R. Goodall,et al.  13 Computation using the QR decomposition , 1993, Computational Statistics.

[2]  Ahmet Öztopal,et al.  Artificial neural network approach to spatial estimation of wind velocity data , 2006 .

[3]  D. J. Shendage,et al.  An analysis of beta type Stirling engine with rhombic drive mechanism , 2011 .

[4]  Gholamhassan Najafi,et al.  Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends , 2010 .

[5]  Dominique Richon,et al.  Enhancement of the extended corresponding states techniques for thermodynamic modeling. I. Pure fluids , 2006 .

[6]  Mohammad Ali Ahmadi,et al.  Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach , 2014 .

[7]  Roman M. Balabin,et al.  Near-Infrared (NIR) Spectroscopy for Biodiesel Analysis: Fractional Composition, Iodine Value, and Cold Filter Plugging Point from One Vibrational Spectrum , 2011 .

[8]  Mohammad Ali Ahmadi,et al.  Evolving predictive model to determine condensate-to-gas ratio in retrograded condensate gas reservoirs , 2014 .

[9]  Can Çinar,et al.  Nodal analysis of a Stirling engine with concentric piston and displacer , 2006 .

[10]  Roman M. Balabin,et al.  Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? , 2011, Physical chemistry chemical physics : PCCP.

[11]  Zhide Hu,et al.  Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine. , 2005, The journal of physical chemistry. B.

[12]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

[13]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[14]  Fatih Aksoy,et al.  An experimental study on the development of a β-type Stirling engine for low and moderate temperature heat sources , 2009 .

[15]  Roman M. Balabin,et al.  Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data. , 2011, Analytica chimica acta.

[16]  Mehrdad Abedi,et al.  Short term wind speed forecasting for wind turbine applications using linear prediction method , 2008 .

[17]  E. El-Saadany,et al.  Grey predictor for wind energy conversion systems output power prediction , 2006, IEEE Transactions on Power Systems.

[18]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[19]  Angkee Sripakagorn,et al.  Design and performance of a moderate temperature difference Stirling engine , 2011 .

[20]  Mohammad Ali Ahmadi,et al.  Evolving smart approach for determination dew point pressure through condensate gas reservoirs , 2014 .

[21]  Julian Meng,et al.  Short-Term Wind Speed Forecasting Based On Fuzzy Artmap , 2011 .

[22]  Amir H. Mohammadi,et al.  Artificial neural network, ANN-PSO and ANN-ICA for modelling the Stirling engine , 2016 .

[23]  Martin Brown,et al.  Network Performance Assessment for Neurofuzzy Data Modelling , 1997, IDA.

[24]  Can Çinar,et al.  Torque and power characteristics of a helium charged Stirling engine with a lever controlled displacer driving mechanism , 2010 .

[25]  Roman M. Balabin,et al.  Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy , 2011 .

[26]  Millaray Curilem,et al.  Neural Networks and Support Vector Machine Models Applied to Energy Consumption Optimization in Semiautogeneous Grinding , 2011 .

[27]  Ghislain Despesse,et al.  Analytical model for Stirling cycle machine design , 2010 .

[28]  Michel Feidt,et al.  Connectionist intelligent model estimates output power and torque of stirling engine , 2015 .

[29]  Wen Lih Chen,et al.  A numerical analysis on the performance of a pressurized twin power piston gamma-type Stirling engine , 2012 .

[30]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[31]  Marc A. Rosen,et al.  Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine , 2015 .

[32]  İsmail Yabanova,et al.  Artificial neural network modeling of geothermal district heating system thought exergy analysis , 2012 .

[33]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[34]  J Fano,et al.  A new equation representing the performance of kinematic Stirling engines , 2000 .

[35]  Richard S. J. Tol.  Autoregressive Conditional Heteroscedasticity in daily wind speed measurements , 1997 .

[36]  E.F. El-Saadany,et al.  Annual Wind Speed Estimation Utilizing Constrained Grey Predictor , 2009, IEEE Transactions on Energy Conversion.

[37]  Thomas Ackermann,et al.  Wind Power in Power Systems , 2005 .

[38]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[39]  Chin-Hsiang Cheng,et al.  Numerical model for predicting thermodynamic cycle and thermal efficiency of a beta-type Stirling engine with rhombic-drive mechanism , 2010 .

[40]  Erich Podesser.  Electricity production in rural villages with a biomass Stirling engine , 1999 .

[41]  D. G. Thombare,et al.  TECHNOLOGICAL DEVELOPMENT IN THE STIRLING CYCLE ENGINES , 2008 .

[42]  Huei-Lin Chang,et al.  Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs , 2010 .

[43]  Alireza Bahadori,et al.  A developed smart technique to predict minimum miscible pressure—eor implications , 2013 .

[44]  M. Ahmadi.  Neural network based unified particle swarm optimization for prediction of asphaltene precipitation , 2012 .

[45]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[46]  D. Richon,et al.  Modeling of thermodynamic properties using neural networks: Application to refrigerants , 2002 .