Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2015

01.02.2015 | Original Article

Gene expression programming strategy for estimation performance of LiBr–H2O absorption cooling system

verfasst von: Erkan Dikmen

Erschienen in: Neural Computing and Applications | Ausgabe 2/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The estimation of the coefficient of performance (COP) and flow ratio (FR) of absorption cooling systems is an important and complex process. In order to simplify this complex process, a gene expression programming (GEP) model was used. In the present study, GEP has been utilized to evaluate the COP and FR of the LiBr–H2O absorption cooling system. The data used as input parameters consisted of evaporator temperature, condenser temperature, absorber temperature, generator temperature and strong and weak solution concentrations. According to these input parameters, in the GEP models, the COP and FR of the LiBr–H2O absorption cooling system were predicted. The training and testing results in the GEP model have shown an acceptable potential for estimating performance of the absorption cooling system in the considered range.
Vorheriger Artikel Manifold proximal support vector machine with mixed-norm for semi-supervised classification
Nächster Artikel Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Assilzadeh F, Kalogirou SA, Ali Y, Sopian K (2005) Simulation and optimization of a LiBr solar absorption cooling system with evacuated tube collectors. Renew Energy 30:1143–1159CrossRef
2.
Sun J, Fu L, Zhang S, Hou W (2010) A mathematical model with experiments of single effect absorption heat pump using LiBr–H2O. Appl Therm Eng 30:2753–2762CrossRef
3.
Sun J, Fu L, Zhang S (2010) Performance calculation of single effect absorption heat pump using LiBr:LiNO3:H2O as working fluid. Appl Therm Eng 30:2680–2684CrossRef
4.
Ozgoren M, Bilgili M, Babayigit O (2012) Hourly performance prediction of ammonia-water solar absorption refrigeration. Appl Therm Eng 40:80–90CrossRef
5.
Beccali M, Cellura M, Longo S, Nocke B, Finocchiaro P (2012) LCA of a solar heating and cooling system equipped with a small water–ammonia absorption chiller. Sol Energy 86:1491–1503CrossRef
6.
Kalkan N, Young EA, Celiktas A (2012) Solar thermal air conditioning technology reducing the footprint of solar thermal air conditioning. Renew Sustain Energy Rev 16:6352–6383CrossRef
7.
OECD/IEA (2012) Technology roadmap solar heating and cooling. International Energy Agency, Paris
8.
Yin YL, Zhai XQ, Wang RZ (2013) Experimental investigation and performance analysis of a mini-type solar absorption cooling system. Appl Therm Eng 59:267–277CrossRef
9.
Gunhan T, Ekren O, Demir V, Hepbasli A, Erek A, Sahin AS (2014) Experimental exergetic performance evaluation of a novel solar assisted LiCl–H2O absorption cooling system. Energy Build 68:138–146CrossRef
10.
Ketjoy N, Yongphayoon R, Mansiri K (2013) Performance evaluation of 35 kW LiBr–H2O solar absorption cooling system in Thailand. Energy Proc 34:198–210CrossRef
11.
Eicker U, Pietruschka D (2009) Design and performance of solar powered absorption cooling systems in office buildings. Energy Build 41:81–91CrossRef
12.
Rosiek S, Batlles FJ (2010) Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network. Renew Energy 35:2894–2901CrossRef
13.
Sözen A, Arcaklioğlu E, Özalp M (2004) Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks. Energy Convers Manag 45:2233–2253CrossRef
14.
Sözen A, Arcaklioğlu E, Özalp M (2003) A new approach to thermodynamic analysis of ejector–absorption cycle: artificial neural networks. Appl Therm Eng 23:937–952CrossRef
15.
Şencan A, Yakut KA, Kalogirou SA (2006) Thermodynamic analysis of absorption systems using artificial neural network. Renew Energy 31:29–43CrossRef
16.
Godarzi AA, Jalilian M, Samimi J, Jokar A, Vesaghi MA (2013) Design of a PCM storage system for a solar absorption chiller based on exergoeconomic analysis and genetic algorithm. Int J Refrig 36:88–101CrossRef
17.
Chow TT, Zhang GQ, Lin Z, Song CL (2002) Global optimization of absorption chiller system by genetic algorithm and neural network. Energy Build 34:103–109CrossRef
18.
Manohar HJ, Saravanan R, Renganarayanan S (2006) Modelling of steam fired double effect vapour absorption chiller using neural network. Energy Convers Manag 47:2202–2210CrossRef
19.
Nazari A (2012) Prediction performance of PEM fuel cells by gene expression programming. Int J Hydrog Energy 37:18972–18980CrossRef
20.
Landeras G, Lopez JJ, Kisi O, Shiri J (2012) Comparison of gene expression programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy Convers Manag 62:1–13CrossRef
21.
Shiri J, Kisi Ö, Landeras G, Lopez JJ, Nazemi AH, Stuyt LCPM (2012) Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). J Hydrol 414:302–316CrossRef
22.
Sarıdemir M (2011) Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming. Expert Syst Appl 38:14257–14268
23.
Samadianfard S (2012) Gene expression programming analysis of implicit Colebrook–White equation in turbulent flow friction factor calculation. J Pet Sci Eng 92–93:48–55CrossRef
24.
Gharagheizi F, Ilani-Kashkouli P, Farahanib N, Mohammadi AH (2012) Gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds. Fluid Phase Equilib 329:71–77CrossRef
25.
Shiri J, Sadraddini AA, Nazemi AH, Kisi O, Landeras G, Fard AF, Marti P (2014) Generalizability of gene expression programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. J Hydrol 508:1–11CrossRef
26.
Nazari A, Safarnejad MG (2013) Prediction early age compressive strength of OPC-based geopolymers with different alkali activators and seashell powder by gene expression programming. Ceram Int 39:1433–1442CrossRef
27.
Marti P, Shiri J, Duran-Ros M, Arbat G, Cartagena FR, Puig-Bargues J (2013) Artificial neural networks vs. Gene Expression Programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents. Comput Electron Agric 99:176–185CrossRef
28.
Güllü H (2014) Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom ash. Eng Appl Artif Intell 35:143–157CrossRef
29.
Mousavi SM, Mostafavi ES, Hosseinpour F (2014) Gene expression programming as a basis for new generation of electricity demand prediction models. Comput Ind Eng 74:120–128CrossRef
30.
Mermerdaş K, Güneyisi E, Gesoğlu M, Özturan T (2013) Experimental evaluation and modeling of drying shrinkage behavior of metakaolin and calcined kaolin blended concretes. Constr Build Mater 43:337–347CrossRef
31.
Ozbek A, Unsal M, Dikec A (2013) Estimating uniaxial compressive strength of rocks using genetic expression programming. J Rock Mec Geotech Eng 5:325–329CrossRef
32.
Shiri J, Marti P, Singh VP (2014) Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning. Hydrol Process 28:1215–1225CrossRef
33.
Traore S, Guven A (2013) New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa. Irrig Sci 31:1–10CrossRef
34.
Herold KE, Radermacher R, Klein SA (1996) Absorption chillers and heat pumps. CRC Press, Boca Raton
35.
Ozbek A, Unsal M, Dikec A (2013) Estimating uniaxial compressive strength of rocks using genetic expression programming. J Rock Mec Geotech Eng 5:325–329CrossRef
36.
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13:87–129MATH
37.
Teodorescu L, Sherwood D (2008) High energy physics event selection with gene expression programming. Comput Phys Commun 178:409–419CrossRef
38.
Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38:4080–4087CrossRef
39.
40.
Sun DW (1997) Thermodynamic design data and optimum design maps for absorption refrigeration systems. Appl Therm Eng 17:211–221CrossRef
Metadaten
Titel
Gene expression programming strategy for estimation performance of LiBr–H2O absorption cooling system
verfasst von
Erkan Dikmen
Publikationsdatum
01.02.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1723-9

Weitere Artikel der Ausgabe 2/2015

Neural Computing and Applications 2/2015 Zur Ausgabe

Original Article

Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term

Original Article

Discriminative structure discovery via dimensionality reduction for facial image manifold

Advances in Intelligent Data Processing and Analysis

Ranking of characteristic features in combined wrapper approaches to selection

Original Article

Stochastic stability analysis for neural networks with mixed time-varying delays

Original Article

Multiple dimensioned mining of financial fluctuation through radial basis function networks

Original Article

Affect-insensitive speaker recognition systems via emotional speech clustering using prosodic features

Premium Partner

    Bildnachweise