@article { ISI:000261281100001,
	title = {SVR-based prediction of point gas hold-up for bubble column reactor through recurrence quantification analysis of LDA time-series},
	journal = {International Journal of Multiphase Flow},
	volume = {34},
	number = {12},
	year = {2008},
	month = {DEC},
	pages = {1099-1107},
	publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
	address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
	abstract = {<p>Recurrence quantification analysis (RQA) has emerged as a useful tool for detecting singularities in nonstationary time-series data. In this paper, we use RQA to analyze the velocity-time data acquired using laser doppler anemometry (LDA) signals in a bubble column reactor for Single point and Multipoint point spargers. The recurring dynamical states within the velocity-time-series occurring due to the bubble and the liquid passage at the point of measurement, are quantified by RQA features (namely \% Recurrence, \% Determinism, \% Laminarity and Entropy), which in turn are regressed using support vector regression (SVR) to predict the point gas hold-up values. It has been shown that SVR-based model for the bubble column reactor can be potentially useful for online prediction and monitoring of the point gas hold-up for different sparging conditions. (C) 2008 Elsevier Ltd. All rights reserved.</p>},
	keywords = {Bubble column, Gas hold-up, LDA, Recurrence quantification analysis (RQA), Support vector regression (SVR)},
	issn = {0301-9322},
	doi = {10.1016/j.ijmultiphaseflow.2008.07.001},
	author = {Gandhi, Ankit B. and Joshi, J. B. and Kulkarni, A. A. and Jayaraman, Valadi K. and Kulkarni, B. D.}
}
