01846nas a2200181 4500008004100000022001400041245007400055210006900129260007100198300001400269490000700283520116700290100002201457700002701479700002601506700002601532856010601558 2007 eng d a0888-588500aData-driven dynamic modeling and control of a surface aeration system0 aDatadriven dynamic modeling and control of a surface aeration sy a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcDEC a8607-86130 v463 a

In this study we have developed a support vector regression (SVR) based data-driven model for predicting two important design parameters of surface aerators, namely, the volumetric mass transfer coefficient (k(L)(a) under bar) and fractional gas hold-up (epsilon(G)). The dynamical state of the surface aerator system was captured by acquiring pressure fluctuation signals (PFSs) at various design and operating conditions. The most informative features from PFS were extracted using the chaos analysis technique, which includes estimation of Lyapunov exponent, correlation dimensions, and Kolmogorov entropy. At similar conditions the values of k(L)(a) under bar and epsilon(G) were also measured. Two different SVR models for predicting the volumetric mass transfer coefficient (k(L)(a) under bar) and overall gas hold-up (epsilon(G)) as a function of chaotic invariants, design parameters, and operating parameters were developed showing test accuracies of 98.8% and 97.1%, respectively. Such SVM based models for the surface aerator can be potentially useful on a commercial scale for online monitoring and control of desired process output variables.

1 aGandhi, Ankit, B.1 aJoshi, Jyeshtharaj, B.1 aJayaraman, Valadi, K.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/data-driven-dynamic-modeling-and-control-surface-aeration-system-002628nas a2200217 4500008004100000022001400041245016400055210006900219260010600288300001400394490000700408520168500415653002602100653002402126653003002150100002202180700002702202700002602229700002602255856012902281 2007 eng d a0009-250900aDevelopment of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems0 aDevelopment of support vector regression SVRbased correlation fo aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcDEC a7078-70890 v623 a

The objective of this study was to develop a unified data-driven correlation for the overall gas hold-up for various gas-liquid systems using support vector regression (SVR)-based modeling technique. Over the years, researchers have amply quantified the hydrodynamics of bubble column reactors in terms of the overall gas hold-up. In this work, about 1810 experimental points were collected from 40 open sources spanning the years 1965-2007. The model for overall gas hold-up was established as a function of several parameters which include superficial gas velocity, superficial liquid velocity, gas density, molecular weight of gas, sparger type, sparger hole diameter, number of sparger holes, liquid viscosity, liquid density, liquid surface tension, operating temperature, operating pressure and column diameter of the gas-liquid system. For understanding the hold-up behavior, the data used for training the model was grouped into various gas-liquid systems viz., air-water, gas-aqueous viscous liquids, gas-organic liquids, gas-aqueous electrolyte solutions and gas-liquid systems operated over a wide range of pressure. A generalized model established using SVR was evaluated for its performance for various gas-liquid systems. Statistical analysis showed that the proposed generalized SVR-based correlation for overall gas hold-up has prediction accuracy of 97% with average absolute relative error (% AARE) of 12.11%. A comparison of this correlation with the selected system specific correlations in the literature showed that the developed SVR-based correlation significantly gives enhanced prediction of overall gas hold-up. (C) 2007 Published by Elsevier Ltd.

10abubble column reactor10aoverall gas hold-up10asupport vector regression1 aGandhi, Ankit, B.1 aJoshi, Jyeshtharaj, B.1 aJayaraman, Valadi, K.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/development-support-vector-regression-svr-based-correlation-prediction-overall-gas-hold-003142nas a2200229 4500008004100000022001400041245026000055210006900315260007100384300001400455490000700469520211800476100002402594700002502618700002102643700002202664700002302686700002702709700002602736700002602762856012402788 2009 eng d a0888-588500aDevelopment of correlations for overall gas hold-up, volumetric mass transfer coefficient, and effective interfacial area in bubble column reactors using hybrid genetic algorithm-support vector regression technique: viscous newtonian and non-newtonian liq0 aDevelopment of correlations for overall gas holdup volumetric ma a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcNOV a9631-96540 v483 a

The objective of this study was to develop hybrid genetic algorithm-support vector regression (GA-SVR)-based correlations for overall gas hold-up (epsilon(G)), volumetric mass-transfer coefficient (kt,a), and effective interfacial area (a) in bubble Column reactors for gas-liquid systems employing Viscous Newtonian and non-Newtonian systems as the liquid phase. The hybrid GA-SVR is a novel technique based on the feature 0 generation approach using genetic algorithm (GA). In the present study, GA has been used for nonlinear rescaling of attributes. These, exponentially scaled, are eventually subjected to SVR training The technique is an extension of conventional SVR technique, showing relatively enhanced results For this purpose an extensive literature search was done. From the published literature, 1629 data points for viscous Newtonian and 845 data points for VISCOUS non-Newtonian systems for cc;, 500 data points for viscous Newtonian and 556 data points for viscous non-Newtonian systems for k(L)a, and 208 data points for viscous non-Newtonian systems for a, respectively, were collected These data sets were collected spanning the years 1965-2007 Correlations were developed after taking into account all the parameters affecting epsilon(G), k(1)a, and a such as column and sparger geometry, gas-liquid properties, operating temperature, pressure, and Superficial gas and liquid velocities. The correlations thus developed gave prediction accuracies of 0.994 and 0.999 and average absolute relative errors (AARE) of 3.75 and 1.65% for viscous Newtonian and non-Newtonian systems for epsilon(G), prediction accuracies of 0.983 and 0.998 and AARE of 8 62 and 1.91% for viscous Newtonian and non-Newtonian systems for k(1)a, and prediction accuracy of 0.999 and AARE of 1% for viscous non-Newtonian systems for a, respectively. These correlations also showed much improved results when compared with all the existing correlations proposed in literature. To facilitate their usage, all the hybrid GA-SVR-based correlations have been uploaded on the web link http-//wwwesnips.com/web/UICT-NCL.

1 aGupta, Prashant, P.1 aMerchant, Shamel, S.1 aBhat, Akshay, U.1 aGandhi, Ankit, B.1 aBhagwat, Sunil, S.1 aJoshi, Jyeshtharaj, B.1 aJayaraman, Valadi, K.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/development-correlations-overall-gas-hold-volumetric-mass-transfer-coefficient-and-002056nas a2200193 4500008004100000022001400041245020100055210006900256260007100325300001400396490000700410520119500417100002201612700002401634700002701658700002601685700002601711856012501737 2009 eng d a0888-588500aDevelopment of unified correlations for volumetric mass-transfer coefficient and effective interfacial area in bubble column reactors for various gas-liquid systems using support vector regression0 aDevelopment of unified correlations for volumetric masstransfer  a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcMAY a4216-42360 v483 a

The objective of this study was to develop a unified correlation for the volumetric mass-transfer coefficient (k(L)a) and effective interfacial area (a) in bubble columns for various gas-liquid systems using support vector regression (SVR-) based modeling technique. From the data published in the open literature, 1600 data points from 27 open sources spanning the years 1965-2007 for k(L)a and 1330 data points from 28 open sources spanning the years 1968-2007 for a were collected. Generalized SVR-based models were developed for the relationship between k(L)a (and a) and each design and operating parameters such as column and sparger geometry, gas-liquid physical properties, operating temperature, pressure, superficial gas velocity, and so on. Further, these models for k(L)a and a are available online at http://www.esnips.com/web/UICT-NCL. The proposed generalized SVR-based correlations for k(L)a and a have prediction accuracies of 99.08% and 98.6% and average absolute relative errors (AAREs) of 7.12% and 5.01%, respectively. Also, the SVR-based correlation provided much improved predictions compared to those obtained using empirical correlations from the literature.

1 aGandhi, Ankit, B.1 aGupta, Prashant, P.1 aJoshi, Jyeshtharaj, B.1 aJayaraman, Valadi, K.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/development-unified-correlations-volumetric-mass-transfer-coefficient-and-effective-0