Please use this identifier to cite or link to this item: https://idr.l4.nitk.ac.in/jspui/handle/123456789/10190
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dc.contributor.authorShrivastava, R.
dc.contributor.authorMahalingam, H.
dc.contributor.authorDutta, N.N.
dc.date.accessioned2020-03-31T08:18:42Z-
dc.date.available2020-03-31T08:18:42Z-
dc.date.issued2017
dc.identifier.citationChemical Engineering Communications, 2017, Vol.204, 5, pp.591-598en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/10190-
dc.description.abstractBioreactors and associated bioprocesses are quite complex and nonlinear in nature. A small change in initial condition can greatly alter the output product quality. It is pretty difficult at times to model the system mathematically. In this work, the fault detection problem is studied in the context of bioreactors, mainly, a reactor from the penicillin production process. It is very important to identify the faults in a live process to avoid product quality deterioration. We have focused on the process history-based methods to identify the faults in a bioreactor. We want to introduce random forest (RF), a powerful machine learning algorithm, to identify several types of faults in a bioreactor. The algorithm is simple, easy to use, shows very good generalization ability without compromising much on the classification accuracies, and also has an ability to give variable importance as a part of the algorithm output. We compared its performance with two popular methods, namely support vector machines (SVM) and artificial neural networks (ANN), and found that the overall performance is superior in terms of classification accuracies and generalization ability. 2017, Copyright Taylor & Francis Group, LLC.en_US
dc.titleApplication and Evaluation of Random Forest Classifier Technique for Fault Detection in Bioreactor Operationen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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