Accurate Prediction of Preheat Temperature in Solar Flash Desalination Systems Using Kernel Ridge Regression
Publication: Journal of Energy Engineering
Volume 142, Issue 2
Abstract
Thermal desalination systems consist of phase-change operations to separate freshwater from bulk seawater. Solar desalination systems involve partial vaporization of seawater, known as flashing, using solar heat then condensation of flashed steam to produce fresh water. The latent heat of the condensing steam is usually utilized to preheat seawater, thus increasing the energy efficiency of the desalination systems. Evaluating the feasibility of a solar desalination system requires accurate determination of seawater preheat temperature exiting the condenser to enter the evaporator. Determining this temperature is very challenging due to the complicated phase-change dynamics and the existence of noncondensable gases in the condenser that were dissolved in seawater. The preheat temperature depends on several factors such as seawater flow rate, system vacuum, and flashed vapor temperature. This study utilizes the kernel ridge regression (KRR) method to predict the preheat temperature as a function of these factors. KRR is an efficient algorithm that utilizes kernel methods to model highly nonlinear relations. The results show the prediction of preheat temperature is highly accurate and it outperforms the state-of the-art regression model of support vector machines in terms of efficiency.
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References
Abtahi, H. (1988). “Investigation of local pressure characteristics in gas-loaded heat pipes.” Proc., ASME National Heat Transfer Conf., Vol. 1, ASME, New York, 347–352.
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.
Abutayeh, M., Goswami, D. Y., and Stefanakos, E. (2013). “Theoretical and experimental simulation of passive vacuum solar flash desalination.” J. Solar Energy Eng., 135(2), 021013.
Borishanski, V. M., et al. (1982). “Shell-side coefficient of heat transfer from steam contaminated with non-condensable gases.” Heat Trans.-Soviet Res., 14, 15.
Chang, C. C., and Lin, C. J. (2011). “LIBSVM: A library for support vector machines.” 〈http://www.csie.ntu.edu.tw/∼cjlin/libsvm〉.
Cristianini, N., and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, New York.
Efron, B., and Tibshirani, R. J. (1994). An introduction to the bootstrap, Chapman & Hall/CRC, Boca Raton, FL.
Exterkate, P., et al. (2011). “Modelling issues in kernel ridge regression.” Tinbergen Institute, Amsterdam, Netherlands.
Hoerl, A. E., and Kennard, R. W. (1970). “Ridge regression: Biased estimation for nonorthogonal problems.” Technometrics, 12(1), 69–82.
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.
Lewis, J. M., Lakshmivarahan, S., and Dhall, S. (2006). Dynamic data assimilation: A least squares approach, Cambridge University Press, Cambridge, U.K.
Maalouf, M., and Homouz, D. (2014). “Kernel ridge regression using truncated newton method.” Knowledge-Based Syst., 71, 339–344.
Montenegro, L. C. C., Colosimo, E. A., Cordeiro, G. M., and Cruz, F. R. B. (2004). “Bias correction in the cox regression model.” J. Stat. Comput. Simul., 74(5), 379–386.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2012). Introduction to linear regression analysis, 5th Ed., Wiley, Hoboken, NJ.
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., 24–28.
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.
Saunders, C., Gammerman, A., and Vovk, V. (1998). “Ridge regression learning algorithm in dual variables.” Proc., 15th Int. Conf. on Machine Learning, Morgan Kaufmann, Burlington, MA, 515–521.
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.
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. (1991). “Condensation in a natural circulation loop with noncondensable gases: Part I.” Proc., Int. Conf. on Multiphase Flows, T. Yutaka, ed., Japan Society of Multiphase Flow, Ashiya, Hyogo, Japan, 183–186.
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© 2015 American Society of Civil Engineers.
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Received: Jun 9, 2015
Accepted: Sep 22, 2015
Published online: Dec 22, 2015
Discussion open until: May 22, 2016
Published in print: Jun 1, 2016
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