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<p>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.</p>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-0