Research Article
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Sıvı kristallerde faz geçişlerinin tahmini için yeni bir araç

Year 2018, Volume: 22 Issue: 4, 1086 - 1094, 01.08.2018
https://doi.org/10.16984/saufenbilder.290944

Abstract

Lo Bu araştırmada Sıvı Kristallerden C37H59NO2,
C37H59NO3 ve C41H67NO2
nin yüksek basınç ortamında Diferansiyel Termal Analiz (DTA) gereci ile Termal
Analizi yapılmıştır. İncelenen Sıvı Kristallerin Faz geçiş sıcaklıkları,
Entalpi ve Entropi değerleri araştırılmıştır. Ayrıca yapay zeka yöntemlerinden
Yapay Sinir Ağlarını (YSA) kullanarak, yapay sinir ağlarından alınan sonuçlar
ile belirli aralıklarda cihaz ile yapılan ölçümlerden elde edilen sonuçların
uyumu gösterilmiştir. Aralık dışında kalan değerler için de yapay sinir
ağlarından elde edilen değerler üretilmiştir. Bu çalışmada üç katmanlı ileri beslemeli
geriye yayınımlı YSA modeli kullanıldı. Bu yaklaşım ile, YSA’nın faz geçişleri
üzerinde yapılan çalışmaların tahmini için yararlı bir araç olduğunu
kanıtlamıştır.

References

  • [1] N. Clayton, N. Musolino, E. Giannini, V. Garnier, and R. Flükiger, «New Apparatus for DTA at 2000 bar: Thermodynamic Studies on Au, Ag, Al and HTSC Oxides”, Supercond. Sci. Technology,» Supercond. Sci. Technology, cilt 17, no. 3, pp. 395-406, 2004.
  • [2] A. Langier-Kuzniarowa, «Standardization in Thermal Analysis,» J. Therm. Anal. Calorimetry, cilt 24, p. 913, 1984.
  • [3] S. Yariv, «The Role of Charcoal on DTA Curves of Organo-Clay Complexes: an Overview,» Applied Clay Science, cilt 29, pp. 225-236, 2004.
  • [4] Alain F Plante, José M Fernández, J Leifeld, «Application of Thermal Analysis Techniques in Soil Science,» Geoderma, cilt 153, no. 1-2, pp. 1-10, 2009.
  • [5] G. Tammann, Z.S. Phys. Chem, cilt 20, p. 743, 1912.
  • [6] P.W.Bridgman, Phys.Review3, p. 126, 1914.
  • [7] R. L. Stone, «Differential Thermal Analysis by The Dynamic Gas Technique,» Anal. Chem., cilt 32, p. 1582, 1960.
  • [8] L. Kaufman, Material Science and Engineering Series, New York: McGraw-Hill, 1963.
  • [9] M. Beken, «The Neural Network and Multivariate Linear Regression Approach for Observing Phase Transitions of Polymers With The Differential Thermal Analysis Method,» J. Therm. Anal. Calorimetry, cilt 101, no. 1, pp. 339-347, 2010.
  • [10] S. Agatonovic-Kustrin and R. Beresford, «Basic Concepts of Artificial Neural Network (ANN) Modeling and Its Application in Pharmaceutical Research,» Jour. Of Pharm. Biol. Anal. , cilt 22, pp. 717-727, 2000.
  • [11] M. Culloch and W. Pitts, «A Logical Calculus Of The Idea Immanent In Nervous Activity,» Bulletin of Math. W. S. Biophys., cilt 5, pp. 115-133, 1943.
  • [12] O. Maimon and L. Rokach, Data Mining and Knowledge Discovery Handbook, Springer, 2005.
  • [13] Kotfica, E. Tomaszewicz and M., «Application of Neural Networks in Analysis of Thermal Decomposition of CoSO4·7H2O,» J. Therm. Anal. Calorimetry, cilt 74, p. 583, 2003.
  • [14] G. Zhang, B.E. Patuwo, M.Y. Hu, «Forecasting with Artificial Neural Networks: The State of the Art,» Inter. Journal of Forecasting, cilt 15, no. 1998, pp. 35-62, 1998.
  • [15] R. C. O. Sebastiao, J. P. Braga, and M. I. Yoshida, «Competition Between Kinetic Models in Thermal Decomposition: Analysis by Artificial Neural network,» Thermochimica Acta, cilt 412, no. 1-2, pp. 107-111, 2004.
  • [16] J. Straszko, A. Biedunkiewicz, and A. Strzelczak, «Application of Artificial Neural Nerworks in Oxidation Kinetic Analysis of Nanocomposites,» Polish Journal of Chemical Technology, cilt 10, no. 3, pp. 21-28, 2008.
  • [17] J. A. Conesa, J. A. Caballero, and J. A. Reyes-Labarta, «Artificial Neural Network for Modelling Termal Decompositions,» J. Anal. Appl. Pyrolysis, cilt 71, pp. 343-352, 2004.
  • [18] M. Beken, Yüksek Basınç Altında Çalışan Diferansiyel Termal Analiz Cihazının Geliştirilmesi, İstanbul: Yıldız Teknik Üniversitesi, 2002.
  • [19] M. Beken, «Artifıcial Neural Network Prediction for Thermal Decomposition of Potassıum Nitrate (KNO3) and Benzoic Acid (C6H5COOH),» Mod. Phys. Lett. B, cilt 24, no. 17, pp. 1855-1868, 2010.
  • [20] R. Erb, «Introduction to Backpropagation Neural Network Computation,» Pharmaceutical Research, cilt 10, no. 2, pp. 165-170, 1993.
  • [21] N. Sbirrazzuolia, D. Brunel, and L. Elegant, «Neural Networks for Kinetic Parameters Determination, Signal Filtering and Deconvolution in Thermal Analysis,» J. Therm. Anal. Calorimetry, cilt 49, no. 3, pp. 1553-1564, 1997.
  • [22] B. Widrow and M. E. Ho, «Adaptive Switching Circuits,» IRE WESCON ConventionRecord, pp. 96-104, 1960.

A new tool for prediction of phase transitions in liquid crystals

Year 2018, Volume: 22 Issue: 4, 1086 - 1094, 01.08.2018
https://doi.org/10.16984/saufenbilder.290944

Abstract

In this article, fundamental analysis of
C37H59NO2, C37H59NO3, and C41H67NO2 from among liquid crystals is conducted via
Differential Thermal Analysis (DTA) device in high pressure environment. Phase
transition temperature, entalphy, and entrophy of these liquid crystals are
observed. In addition, an Artificial Neural Network (ANN), which is a method of
Artificial Intelligence, is modeled. Then, output values of ANN model and DTA
device are compared, and correlation between them is demonstrated. For the
values which are not measured with DTA device, outputs are produced by ANN
model. In this article, three layered feed-forward back propagation ANN model
is used. With this approach, it is proved that, ANN is a resourceful method for
prediction in studies conducted about phase transition.

References

  • [1] N. Clayton, N. Musolino, E. Giannini, V. Garnier, and R. Flükiger, «New Apparatus for DTA at 2000 bar: Thermodynamic Studies on Au, Ag, Al and HTSC Oxides”, Supercond. Sci. Technology,» Supercond. Sci. Technology, cilt 17, no. 3, pp. 395-406, 2004.
  • [2] A. Langier-Kuzniarowa, «Standardization in Thermal Analysis,» J. Therm. Anal. Calorimetry, cilt 24, p. 913, 1984.
  • [3] S. Yariv, «The Role of Charcoal on DTA Curves of Organo-Clay Complexes: an Overview,» Applied Clay Science, cilt 29, pp. 225-236, 2004.
  • [4] Alain F Plante, José M Fernández, J Leifeld, «Application of Thermal Analysis Techniques in Soil Science,» Geoderma, cilt 153, no. 1-2, pp. 1-10, 2009.
  • [5] G. Tammann, Z.S. Phys. Chem, cilt 20, p. 743, 1912.
  • [6] P.W.Bridgman, Phys.Review3, p. 126, 1914.
  • [7] R. L. Stone, «Differential Thermal Analysis by The Dynamic Gas Technique,» Anal. Chem., cilt 32, p. 1582, 1960.
  • [8] L. Kaufman, Material Science and Engineering Series, New York: McGraw-Hill, 1963.
  • [9] M. Beken, «The Neural Network and Multivariate Linear Regression Approach for Observing Phase Transitions of Polymers With The Differential Thermal Analysis Method,» J. Therm. Anal. Calorimetry, cilt 101, no. 1, pp. 339-347, 2010.
  • [10] S. Agatonovic-Kustrin and R. Beresford, «Basic Concepts of Artificial Neural Network (ANN) Modeling and Its Application in Pharmaceutical Research,» Jour. Of Pharm. Biol. Anal. , cilt 22, pp. 717-727, 2000.
  • [11] M. Culloch and W. Pitts, «A Logical Calculus Of The Idea Immanent In Nervous Activity,» Bulletin of Math. W. S. Biophys., cilt 5, pp. 115-133, 1943.
  • [12] O. Maimon and L. Rokach, Data Mining and Knowledge Discovery Handbook, Springer, 2005.
  • [13] Kotfica, E. Tomaszewicz and M., «Application of Neural Networks in Analysis of Thermal Decomposition of CoSO4·7H2O,» J. Therm. Anal. Calorimetry, cilt 74, p. 583, 2003.
  • [14] G. Zhang, B.E. Patuwo, M.Y. Hu, «Forecasting with Artificial Neural Networks: The State of the Art,» Inter. Journal of Forecasting, cilt 15, no. 1998, pp. 35-62, 1998.
  • [15] R. C. O. Sebastiao, J. P. Braga, and M. I. Yoshida, «Competition Between Kinetic Models in Thermal Decomposition: Analysis by Artificial Neural network,» Thermochimica Acta, cilt 412, no. 1-2, pp. 107-111, 2004.
  • [16] J. Straszko, A. Biedunkiewicz, and A. Strzelczak, «Application of Artificial Neural Nerworks in Oxidation Kinetic Analysis of Nanocomposites,» Polish Journal of Chemical Technology, cilt 10, no. 3, pp. 21-28, 2008.
  • [17] J. A. Conesa, J. A. Caballero, and J. A. Reyes-Labarta, «Artificial Neural Network for Modelling Termal Decompositions,» J. Anal. Appl. Pyrolysis, cilt 71, pp. 343-352, 2004.
  • [18] M. Beken, Yüksek Basınç Altında Çalışan Diferansiyel Termal Analiz Cihazının Geliştirilmesi, İstanbul: Yıldız Teknik Üniversitesi, 2002.
  • [19] M. Beken, «Artifıcial Neural Network Prediction for Thermal Decomposition of Potassıum Nitrate (KNO3) and Benzoic Acid (C6H5COOH),» Mod. Phys. Lett. B, cilt 24, no. 17, pp. 1855-1868, 2010.
  • [20] R. Erb, «Introduction to Backpropagation Neural Network Computation,» Pharmaceutical Research, cilt 10, no. 2, pp. 165-170, 1993.
  • [21] N. Sbirrazzuolia, D. Brunel, and L. Elegant, «Neural Networks for Kinetic Parameters Determination, Signal Filtering and Deconvolution in Thermal Analysis,» J. Therm. Anal. Calorimetry, cilt 49, no. 3, pp. 1553-1564, 1997.
  • [22] B. Widrow and M. E. Ho, «Adaptive Switching Circuits,» IRE WESCON ConventionRecord, pp. 96-104, 1960.
There are 22 citations in total.

Details

Subjects Computer Software, Metrology, Applied and Industrial Physics, Material Production Technologies
Journal Section Research Articles
Authors

Murat Beken

Publication Date August 1, 2018
Submission Date February 9, 2017
Acceptance Date September 15, 2017
Published in Issue Year 2018 Volume: 22 Issue: 4

Cite

APA Beken, M. (2018). A new tool for prediction of phase transitions in liquid crystals. Sakarya University Journal of Science, 22(4), 1086-1094. https://doi.org/10.16984/saufenbilder.290944
AMA Beken M. A new tool for prediction of phase transitions in liquid crystals. SAUJS. August 2018;22(4):1086-1094. doi:10.16984/saufenbilder.290944
Chicago Beken, Murat. “A New Tool for Prediction of Phase Transitions in Liquid Crystals”. Sakarya University Journal of Science 22, no. 4 (August 2018): 1086-94. https://doi.org/10.16984/saufenbilder.290944.
EndNote Beken M (August 1, 2018) A new tool for prediction of phase transitions in liquid crystals. Sakarya University Journal of Science 22 4 1086–1094.
IEEE M. Beken, “A new tool for prediction of phase transitions in liquid crystals”, SAUJS, vol. 22, no. 4, pp. 1086–1094, 2018, doi: 10.16984/saufenbilder.290944.
ISNAD Beken, Murat. “A New Tool for Prediction of Phase Transitions in Liquid Crystals”. Sakarya University Journal of Science 22/4 (August 2018), 1086-1094. https://doi.org/10.16984/saufenbilder.290944.
JAMA Beken M. A new tool for prediction of phase transitions in liquid crystals. SAUJS. 2018;22:1086–1094.
MLA Beken, Murat. “A New Tool for Prediction of Phase Transitions in Liquid Crystals”. Sakarya University Journal of Science, vol. 22, no. 4, 2018, pp. 1086-94, doi:10.16984/saufenbilder.290944.
Vancouver Beken M. A new tool for prediction of phase transitions in liquid crystals. SAUJS. 2018;22(4):1086-94.