Abstract

Support vector machine has been explored and many applications found within various research areas and application domains. Many support vector machine techniques have been specifically developed for certain application domains. This paper is an attempt to provide an overview on applications of support vector machines within the oil refineries to the professionals inside oil refineries, researchers and academicians. This paper has grouped and summarized applications of support vector machines within various units inside refineries. Application of support vector machines to a particular domain within refineries can be used as guidelines to assess the effectiveness of the support vector machines in that domain. This survey provides a better understanding of the different applications that have been developed for one area which allows finding of applications in other domains.

Key words: Support vector machines, data mining, machine learning, oil refinery, oil refining.

Abbreviation

ARIMA, Auto regressive integrated moving average; ANN-MLP, artificial neural network multilayer perceptron; BPNN, back-propagation neural network; C-SVM, classification SVM; DCS, distributed control system; DEA, data envelopment analysis; DMUs, decision making units; KNN, K-nearest neighbor; LDA, linear discriminant analysis; LS-SVM, least Squares support vector machine; NIR, near infrared; PLS, partial least squares; PNN, probabilistic neural network; PSO, particle swarm optimization; QDA, quadratic discriminant analysis; QP, quadratic programming; RBF, radial basis function; RDA, regularized discriminant analysis; SIMCA, soft independent modeling of class analogy; SVM, support vector machine.