Estimation of heat transfer coefficient in bubble column reactors using support vector regression

Novel application of support vector machines to model the two phase boiling heat transfer coefficient in a vertical tube thermosiphon reboiler

“…using a nonlinear mapping  , and subsequently regression is carried out linearly (Gandhi and Joshi, 2010) . The following linear model can be constructed in the high-dimensional feature space:…”

Prediction of flooding velocity in packed towers using least squares support vector machine

“…(2) The optimal set should contain a minimum number of selected variables. (3) The optimal set of the selected variables should be closely associated with the phenomenology of the flooding capacity [7][8][9][10][11][12][13][14].…”

“…Additionally, SVM has found an increasing amount of applications in modeling and control of many chemical processes [8][9][10], because of its good modeling performance. In contrast to NN, the primary advantage of SVM is that, for a given data-based modeling problem with a finite set of samples, it can automatically derive the optimal network structure in respect of generalization error [7][8][9][10]. In this sense, it can be expected, the SVM method can achieve a better prediction performance of the flooding velocity prediction with limited samples of the flooding data.…”

Predicting thermal–hydraulic performances in compact heat exchangers by support vector regression

“…Li [32,33] used the SVR model to predict the hourly cooling in the buildings. The estimation of heat transfer coefficient in bubble column reactors using SVR algorithm was presented by Gandhi [34]. Cai [35] applied SVR to predict the critical heat flux in concentric-tube open thermosiphon.…”