Temperature prediction of the molten salt collector tube using BP neural network

The collector tubes in a receiver play a vital role in the solar power tower system, and directly influence the cost of the power generation. Fast forecast of the temperatures of the collector tubes from the limited number of the temperature measurement data is important. Different from the common computational fluid dynamics prediction method, in this study a back-propagation neural network method is developed to fast acquire the temperature of the receiver, such as the peak temperatures of the inner and outer surfaces, and the outlet mean temperature and the outlet highest temperature of the molten salt. The numerical simulations are implemented to validate the feasibility and effectiveness of the proposed method. Moreover, in the proposed method the temperatures of the tube wall and the molten salt can be fast forecasted without the thermal physical parameters of materials, the boundary conditions or the initial conditions, and the solution of the complicated governing equations.

[1]  Chin-Hui Lee,et al.  An artificial neural network approach to automatic speech processing , 2014, Neurocomputing.

[2]  Robert A. Taylor,et al.  High temperature solar thermal central-receiver billboard design , 2013 .

[3]  Josua P. Meyer,et al.  Numerical modelling and optimisation of natural convection heat loss suppression in a solar cavity receiver with plate fins , 2015 .

[4]  María José Montes,et al.  Proposal of a fluid flow layout to improve the heat transfer in the active absorber surface of solar central cavity receivers , 2012 .

[5]  Alvaro Sanchez-Gonzalez,et al.  Solar flux distribution on central receivers: A projection method from analytic function , 2015 .

[6]  Akiba Segal,et al.  Simulation of an integrated steam generator for solar tower , 2012 .

[7]  Robert Flesch,et al.  On the influence of wind on cavity receivers for solar power towers: An experimental analysis , 2015 .

[8]  Evgueniy Entchev,et al.  Performance prediction of a solar thermal energy system using artificial neural networks , 2014 .

[9]  Soteris A. Kalogirou,et al.  MODELING OF SOLAR DOMESTIC WATER HEATING SYSTEMS USING ARTIFICIAL NEURAL NETWORKS , 1999 .

[10]  Daniel Rivero,et al.  Generation and simplification of Artificial Neural Networks by means of Genetic Programming , 2010, Neurocomputing.

[11]  A. Khellaf,et al.  Thermal performance prediction and sensitivity analysis for future deployment of molten salt cavity receiver solar power plants in Algeria , 2015 .

[12]  M. R. Rodríguez-Sánchez,et al.  Comparison of simplified heat transfer models and CFD simulations for molten salt external receiver , 2014 .

[13]  Beatriz Souza Leite Pires de Lima,et al.  ANN-based surrogate models for the analysis of mooring lines and risers , 2013 .

[14]  K. S. Reddy,et al.  Investigation of convective and radiative heat losses from modified cavity based solar dish steam generator using ANN , 2015 .

[15]  Fardin Ahmadizar,et al.  Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm , 2015, Eng. Appl. Artif. Intell..

[16]  Hamid Taghavifar,et al.  A numerical investigation on the wall heat flux in a DI diesel engine fueled with n-heptane using a coupled CFD and ANN approach , 2015 .

[17]  Euripidis Glavas,et al.  Neural network construction and training using grammatical evolution , 2008, Neurocomputing.

[18]  Juan Carlos Burgos,et al.  Algorithm based on the mesh analysis for computing 2-D magnetostatic fields by the finite difference method , 2014 .

[19]  Jing Lei,et al.  An inversion approach for the inverse heat conduction problems , 2012 .

[20]  Hadi Sadoghi Yazdi,et al.  Ensemble of online neural networks for non-stationary and imbalanced data streams , 2013, Neurocomputing.

[21]  Paulo J. G. Lisboa,et al.  Hybrid Neural Network Predictive-Wavelet Image Compression System , 2015, Neurocomputing.

[22]  Antonio Cammi,et al.  Molten salt mixture properties in RELAP5 code for thermodynamic solar applications , 2008 .

[23]  Wen-Ching Chen,et al.  A unified evolutionary training scheme for single and ensemble of feedforward neural network , 2014, Neurocomputing.

[24]  Shih-Hung Yang,et al.  An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications , 2012, Neurocomputing.

[25]  M. Ranjan,et al.  Solar resource estimation using artificial neural networks and comparison with other correlation models , 2003 .

[26]  Haruhiko Kohno,et al.  An efficient, high-order finite element method using the nodal averaging technique for incompressible fluid flows , 2015, Comput. Phys. Commun..

[27]  Christiaan M. Klaij.  On the stabilization of finite volume methods with co-located variables for incompressible flow , 2015, J. Comput. Phys..

[28]  Huanhuan Chen,et al.  Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.