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Evaluation of models for the dew point temperature determination

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The accuracy of the available from the literature models for the dew point temperature determination was compared. The proposal of the modelling using artificial neural networks was also given. The experimental data were taken from the psychrometric tables. The accuracies of the models were measured using the mean bias error MBE, root mean square error RMSE, correlation coefficient R, and reduced chi-square χ2 . Model M3, especially with constants A=237, B=7.5, gave the best results in determining the dew point temperature (MBE: -0.0229 – 0.0038 K, RMSE: 0.1259 – 0.1286 K, R=0.9999, χ2 : 0.0159 – 0.0166 K2 ). Model M1 with constants A=243.5, B=17.67 and A=243.3, B=17.269 can be also considered as appropriate (MBE=-0.0062 and -0.0078 K, RMSE=0.1277 and 0.1261 K, R=0.9999, χ2 =0.0163 and 0.0159 K2 ). Proposed ANN model gave the good results in determining the dew point temperature (MBE=-0.0038 K, RMSE=0.1373 K, R=0.9999, χ2 =0.0189 K2 ).
Rocznik
Tom
Strony
241–--257
Opis fizyczny
Bibliogr. 52 poz., rys., tab., wykr.
Twórcy
autor
  • Katedra Podstaw Inżynierii, Wydział Inżynierii Produkcji, Szkoła Główna Gospodarstwa Wiejskiego, ul. Nowoursynowska 164, 02-787 Warszawa
  • Faculty of Production Engineering, Warsaw University of Life Sciences
autor
  • Faculty of Production Engineering, Warsaw University of Life Sciences
autor
  • Faculty of Production Engineering, Warsaw University of Life Sciences
Bibliografia
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  • WINICZENKO R., GÓRNICKI K., KALETA A., JANASZEK-MAŃKOWSKA M. 2016. Optimisation of ANN topology for predicting the rehydrated apple cubes colour change using RMS and GA. Neural Computing and Applications, 1-15. DOI: 10.1007/s00521-016-2801-y.
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Uwagi
Opracowanie w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1edd5d50-5013-48a1-b15a-0134e0068e96
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