Modeling of a hazelnut dryer assisted heat pump by using artificial neural networks

被引：43

作者：

Ceylan, Ilhan ^{[1
]}

Aktas, Mustafa ^{[2
]}

机构：

[1] Karabuk Univ, Mech Educ Dept, Tech Educ Fac, Karabuk, Turkey

[2] Gazi Univ, Mech Educ Dept, Tech Educ Fac, TR-06503 Ankara, Turkey

来源：

APPLIED ENERGY | 2008年 / 85卷 / 09期

关键词：

heat pump dryer; hazelnut; artificial neural networks;

D O I：

10.1016/j.apenergy.2007.10.013

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：

The artificial neural network (ANN) approach is generic technique for mapping non-linear relationships between inputs and outputs without knowing the details of these relationships. In this paper, an application of the ANN has been presented for a PID controlled heat pump dryer. In PID controlled heat pump dryer, air velocity changed according to the temperature value which is set in process control device. Heat pump dryer was tested drying of hazelnut at 40 degrees C, 45 degrees C and 50 degrees C drying air temperatures. By training the experiment results with ANN, drying air velocities, moisture content of hazelnuts and total drying time were predicted for 42 degrees C,44 degrees C, 46 degrees C and 48 degrees C drying air temperatures. (c) 2008 Elsevier Ltd. All rights reserved.

引用

页码：841 / 854

页数：14

共 50 条

[21] Modeling Lipase Production Process Using Artificial Neural Networks [J].

Sheta, Alaa F. ;

Hiary, Rania .

2012 INTERNATIONAL CONFERENCE ON MULTIMEDIA COMPUTING AND SYSTEMS (ICMCS), 2012, :1158-1163

[22] Modeling medium carbon steels by using artificial neural networks [J].

Reddy, N. S. ;

Krishnaiah, J. ;

Hong, Seong-Gu ;

Lee, Jae Sang .

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2009, 508 (1-2) :93-105

[23] Using artificial neural networks for modeling suspended sediment concentration [J].

Wang, Yu-Min ;

Traore, Seydou ;

Kerh, Tienfuan .

MMACTEE' 08: PROCEEDINGS OF THE 10TH WSEAS INTERNATIONAL CONFERENCE MATHERMATICAL METHODS AND COMPUTATIONAL TECHNIQUES IN ELECTRICAL ENGINEERING: COMPUTATIONAL METHODS AND INTELLIGENT SYSTEMS, 2008, :108-+

[24] Modeling Historical Traffic Data using Artificial Neural Networks [J].

Ghanim, Mohammad S. ;

Abu-Lebdeh, Ghassan ;

Ahmed, Kamran .

2013 5TH INTERNATIONAL CONFERENCE ON MODELING, SIMULATION AND APPLIED OPTIMIZATION (ICMSAO), 2013,

[25] Modeling and Simulation of Biomass Drying Using Artificial Neural Networks [J].

Francik, Slawomir ;

Lapczynska-Kordon, Boguslawa ;

Francik, Renata ;

Wojcik, Artur .

RENEWABLE ENERGY SOURCES: ENGINEERING, TECHNOLOGY, INNOVATION, 2018, :571-581

[26] Class-modeling using Kohonen artificial neural networks [J].

Marini, F ;

Zupan, J ;

Magrì, AL .

ANALYTICA CHIMICA ACTA, 2005, 544 (1-2) :306-314

[27] Modeling of Inverted Pendulum on a Cart by Using Artificial Neural Networks [J].

Korkmaz, Deniz ;

Bal, Cafer ;

Gokbulut, Muammer .

2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, :2642-2645

[28] Modeling of steam boiler evaporators, using artificial neural networks [J].

Stanislawski, W .

ESM'99 - MODELLING AND SIMULATION: A TOOL FOR THE NEXT MILLENNIUM, VOL II, 1999, :273-277

[29] WiFi Antenna Design and Modeling using Artificial Neural Networks [J].

Abbassi, Passant K. ;

Badra, N. M. ;

Allam, A. M. M. A. ;

El-Rafei, Ahmed .

PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN COMPUTER ENGINEERING (ITCE 2019), 2019, :270-274

[30] Mixtures flow boiling: modeling heat transfer through artificial neural networks [J].

Scalabrin, G. ;

Condosta, M. ;

Marchi, P. .

INTERNATIONAL JOURNAL OF THERMAL SCIENCES, 2006, 45 (07) :664-680

← 1 2 3 4 5 →