Fault Diagnosis of Batch Reactor Using Machine Learning Methods

Abstract

Fault diagnosis of a batch reactor gives the early detection of fault and minimizes the risk of thermal runaway. It provides superior performance and helps to improve safety and consistency. It has become more vital in this technical era. In this paper, support vector machine (SVM) is used to estimate the heat release (Qr) of the batch reactor both normal and faulty conditions. The signature of the residual, which is obtained from the difference between nominal and estimated faultyQrvalues, characterizes the different natures of faults occurring in the batch reactor. Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA) techniques. artificial neural network (ANN) classifiers like multilayer perceptron (MLP), radial basis function (RBF), and Bayes net are used to classify the different types of faults from the reduced features. It is observed from the result of the comparative study that the proposed method for fault diagnosis with limited number of features extracted from only one estimated parameter (Qr) shows that it is more efficient and fast for diagnosing the typical faults.

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Early detection and diagnosis of thermal runaway reactions using model-based approaches in batch reactors

“…In their article, Subramanian et al (2014) proposed a method based on statistical learning theory to estimate the reactor heat release under normal and defective conditions. The faults detection is carried out according to the residue obtained and the faults classification is carried out from the extracted image characteristics.…”