Neural network and polynomial model to improve the coefficient of performance prediction for solar intermittent refrigeration system

被引：11

作者：

Escobedo-Trujillo, B. A. ^{[1
]}

Colorado, D. ^{[2
]}

Rivera, W. ^{[3
]}

Alaffita-Hernandez, F. A. ^{[4
]}

机构：

[1] Univ Veracruzana, Fac Ingn, Campus Coatzacoalcos,Ave Univ Km 7-5, Coatzacoalcos 96535, Veracruz, Mexico

[2] Univ Veracruzana, Ctr Invest Recursos Energet & Sustentables, Ave Univ Km 7-5, Coatzacoalcos 96535, Veracruz, Mexico

[3] Univ Nacl Autonoma Mexico, Inst Energias Renovables, Privada Xochicalco S-N, Temixco 62580, Mor, Mexico

[4] Univ Veracruzana, Fac Matemat, Posgrad Matemat, Circuito Gonzalo Aguirre Beltran S-N, Xalapa 91090, Veracruz, Mexico

来源：

SOLAR ENERGY | 2016年 / 129卷

关键词：

Lithium bromide solution; Multivariable fitting; Residual analysis; Gaussian distribution; Correlation matrix; COP PREDICTION; HEAT TRANSFORMER; ICE PRODUCTION;

D O I：

10.1016/j.solener.2016.01.041

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This study presents a novel hybrid methodology to estimate the coefficient of performance in an absorption intermittent cooling system; the system is for ice production and operates with an ammonia/lithium nitrate mixture. The hybrid model integrates a polynomial fitting method and an artificial neural network model to improve the network performance and the estimation of the COPs. The improvement uses fewer hidden neurons without sacrificing accuracy in the prediction. The proposed hybrid model has two neurons in the input and two in the hidden layers and shows better results than those obtained through polynomial fitting or artificial neural networks separately. The developed model presents an excellent agreement between experimental and simulated values of the coefficient of performance with a determination coefficient R-2 > 0.9978. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：28 / 37

页数：10

共 8 条

[1] COEFFICIENT OF PERFORMANCE PREDICTION BY A POLYNOMIAL MODEL OF ABSORPTION HEAT TRANSFORMER
Escobedo-Trujillo, B. A.
Alaffita-Hernandez, F. A.
Colorado, D.
Siqueiros, J.
REVISTA MEXICANA DE INGENIERIA QUIMICA, 2014, 13 (03): : 907 - 917
[2] Optimal COP prediction of a solar intermittent refrigeration system for ice production by means of direct and inverse artificial neural networks
Hernandez, J. A.
Rivera, W.
Colorado, D.
Moreno-Quintanar, G.
SOLAR ENERGY, 2012, 86 (04) : 1108 - 1117
[3] Selection of the best coefficient of performance prediction by artificial neural network model considering uncertainty
Colorado-Garrido, D.
Escobedo-Trujillo, B. A.
Cobaxin-Munoz, I.
Alaffita-Hernandez, F. A.
Herrera-Romero, J. V.
DESALINATION AND WATER TREATMENT, 2017, 92 : 60 - 71
[4] Coefficient of performance prediction by a polynomial of a heat transformer with two-duplex components
Ramirez-Hernandez, C.
Escobedo-Trujillo, B. A.
Colorado, A.
Alaffita-Hernandez, F. A.
Morales, L. I.
Hernandez, J. A.
APPLIED THERMAL ENGINEERING, 2017, 114 : 1193 - 1202
[5] Evaluation of a solar intermittent refrigeration system for ice production operating with ammonia/lithium nitrate
Rivera, W.
Moreno-Quintanar, G.
Rivera, C. O.
Best, R.
Martinez, F.
SOLAR ENERGY, 2011, 85 (01) : 38 - 45
[6] Prediction of global solar radiation by artificial neural network based on a meteorological environmental data
Diaz-Gomez, J.
Parrales, A.
Alvarez, A.
Silva-Martinez, S.
Colorado, D.
Hernandez, J. A.
DESALINATION AND WATER TREATMENT, 2015, 55 (12) : 3210 - 3217
[7] Innovation prediction of new energy vehicle enterprises based on improved hybrid neural network model
Hao, Ying
Guo, Ming-Shun
Zeng, Hui
JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2022, 22 (06) : 2413 - 2423
[8] Advanced Process Defect Monitoring Model and Prediction Improvement by Artificial Neural Network in Kitchen Manufacturing Industry: a Case of Study
Massaro, Alessandro
Manfredonia, Ivano
Galiano, Angelo
Xhahysa, Benny
2019 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR INDUSTRY 4.0 AND INTERNET OF THINGS (METROIND4.0&IOT), 2019, : 64 - 67

← 1 →