01902nas a2200253   4500008004100000022001400041245013400055210006900189260010600258300001400364490000700378520091400385653001801299653001601317653000801333653004501341653003601386100002201422700001801444700002101462700002601483700002101509856011801530       2008                        eng d  a0301-932200aSVR-based prediction of point gas hold-up for bubble column reactor through recurrence quantification analysis of LDA time-series0 aSVRbased prediction of point gas holdup for bubble column reacto  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcDEC  a1099-11070 v343 a<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>10aBubble column10aGas hold-up10aLDA10aRecurrence quantification analysis (RQA)10aSupport vector regression (SVR)1 aGandhi, Ankit, B.1 aJoshi, J., B.1 aKulkarni, A., A.1 aJayaraman, Valadi, K.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/svr-based-prediction-point-gas-hold-bubble-column-reactor-through-recurrence-0