Improved Modeling of Solar Flash Desalination Using Support Vector Regression
Publication: Journal of Energy Engineering
Volume 143, Issue 4
Abstract
Accurate prediction of heat-transfer rates in condensers is a challenging task because of phase-change dynamics. This is further complicated if noncondensable gases are present since they tend to form an insulating layer around heat-exchange surfaces. This study examines the utilization of support vector regression in predicting the preheat temperature of seawater exiting a condenser upon its flashing in a vacuum chamber to produce fresh water. Gasses dissolved in seawater are released but not condensed. Thus, system vacuum and heat transfer slowly erode with time due to this accumulation of noncondensable gasses. The preheat temperature is modeled in this study as a function of system vacuum, seawater flow rate through the condenser, and flashed vapor temperature destined for condensation. In comparison with the least-squares polynomial method, the results indicate that support vector regression can predict the preheat temperature much more accurately, resulting in a better performance evaluation of the entire solar desalination system.
Get full access to this article
View all available purchase options and get full access to this article.
References
Abtahi, H. (1988). “Investigation of local pressure characteristics in gas-loaded heat pipes.” ASME 1988 National Heat Transfer Conf., ASME, New York.
Abutayeh, M. (2010). “Theoretical and experimental simulation of passive vacuum solar flash desalination.” Ph.D. thesis, Univ. of South Florida, Tampa, FL.
Abutayeh, M., and Goswami, D. Y. (2010). “Experimental simulation of solar flash desalination.” J. Solar Energy Eng., 132(4), 041015-1–041015-7.
Abutayeh, M., Yogi Goswami, D. D., and Stefanakos, E. K. (2013). “Theoretical and experimental simulation of passive vacuum solar flash desalination.” J. Solar Energy Eng., 135(2), 021013-1–021013-13.
Borishanski, V. M. (1982). “Shell-side coefficient of heat transfer from steam contaminated with non-condensable gases.” Soviet Res., 14, 15.
Chang, C. C., and Lin, C. J. (2011). “Libsvm: A library for support vector machines.” ACM Trans. Intell. Syst. Technol., 2(3), 27.
Efron, B., and Tibshirani, R. J. (1994). An Introduction to the bootstrap, Chapman and Hall/CRC, Boca Raton, FL.
Kageyama, T., Peterson, P. F., and Schrock, V. E. (1993). “Diffusion layer modeling for condensation in vertical tubes with noncondensable gases.” Nucl. Eng. Des., 141(1–2), 289–302.
Lee, Y., and Lee, C. (2003). “Classification of multiple cancer types by multicategory support vector machines using gene expression data.” Bioinformatics, 19(9), 1132–1139.
Maalouf, M., Khoury, N., Laguros, J. G., and Kumin, H. (2012). “Support vector regression to predict the performance of stabilized aggregate bases subject to wet-dry cycles.” Int. J. Numer. Anal. Methods Geomech., 36(6), 675–696.
Maalouf, M., Khoury, N., and Trafalis, T. B. (2008). “Support vector regression to predict asphalt mix performance.” Int. J. Numer. Anal. Methods Geomech., 32(16), 1989–1996.
MATLAB [Computer software]. MathWorks, Natick, MA.
Morgan, C., and Rush, C. G. (1983). “Experimental measurements of condensation heat transfer with noncondensible gases present in a vertical tube at high pressure.” 21st National Heat Transfer Conf., ASME, Seattle.
Narayan, G. P., Sharqawy, M. H., Lam, S., Das, S. K., and Lienhard, J. H. (2013). “Bubble columns for condensation at high concentrations of noncondensable gas: Heat-transfer model and experiments.” AIChE J., 59(5), 1780–1790.
Ogg, G. (1991). “Vertical downflow condensation heat transfer in gas-steam mixture.” M.S. thesis, Univ. of California, Berkeley, CA.
Pal, M. (2006). “Support vector machines-based modelling of seismic liquefaction potential.” Int. J. Numer. Anal. Methods Geomech., 30(10), 983–996.
Shawe-Taylor, J., and Cristianini, N. (2004). Kernel methods for pattern analysis, Cambridge University Press, Cambridge, U.K.
Siddique, M., Golay, M. W., and Kazami, M. S. (1993). “Local heat transfer coefficients for forced-convection condensation of steam in a vertical tube in the presence of noncondensable gas.” Nucl. Technol., 102(3), 386–402.
Smola, A. J., and Scholkopf, B. (2004). “A tutorial on support vector regression.” Stat. Comput., 14(3), 199–222.
Trafalis, T. B., and Ince, H. (2000). “Support vector machine for regression and applications to financial forecasting.” Proc., IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks, Neural Computing: New Challenges and Perspectives for the New Millennium, Vol. 6, IEEE, New York, 348–353.
Trafalis, T. B., Ince, H., and Richman, M. (2003). “Tornado detection with support vector machines.” Int. Conf. on Computational Science,, Springer, Berlin, 2660, 289–298.
Vapnik, V. (1995). The nature of statistical learning, Springer, New York.
Vierow, K. M. (1990). “Behavior of steam-air systems condensing in concurrent vertical downflow.” M.S. thesis, Univ. of California, Berkeley, CA.
Vierow, K. M., and Schrock, V. E. (1991). “Condensation in a natural circulation loop with noncondensable gases: Part I.” Proc., Int. Conf. on Multiphase Flows, Japan Society of Multiphase Flow, Ashiya, Hyogo, Japan.
Information & Authors
Information
Published In
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jul 7, 2016
Accepted: Sep 30, 2016
Published ahead of print: Feb 7, 2017
Published online: Feb 8, 2017
Discussion open until: Jul 8, 2017
Published in print: Aug 1, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.