Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/224764
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DC FieldValueLanguage
dc.coverage.spatialChemical Engineering
dc.date.accessioned2018-12-26T11:33:04Z-
dc.date.available2018-12-26T11:33:04Z-
dc.identifier.urihttp://hdl.handle.net/10603/224764-
dc.description.abstractSince, Chemical and bioprocesses are complex nonlinear systems and are difficult to model in real life, it becomes quite important to study process dynamics of these systems, as small changes in initial conditions can give dramatic output product quality, which is not desirable, e.g., in case of bioreactor, small change in pH can affect the growth kinetics or small changes in temperature might damage the cells. Chemical industries also suffer from various accidents mainly due to equipment failure and operator error. Robust fault detection and diagnosis systems are always required for such type of processes. The objectives of fault detection and diagnosis systems are early detection and diagnosis of fault, fast process recovery, avoid abnormal event progression and to satisfy environment and safety regulations. At the same time, it will reduce the product rejection rate, improve the quality of products and reduce the number of accidents in the industry. So a proper fault detection and diagnosis system will provide an economical and safe process. newlineThe idea of computerized on-line monitoring has been broadly researched over the past numerous years. Various approaches to deal with the fault detection and diagnosis have been developed in this period. These methodologies can be ordered into three categories: Methods based on mathematical models of physical systems; Methods make use of available information and knowledge of a physical system; Methods require neither first principles nor qualitative knowledge however rather a lot of historical data that contain the typical trends and fault information. Historical data-based methods are more suitable for such type of processes. All the machine learning techniques, for example, artificial neural networks, support vector machines, random forest, etc., belong to the class of historical data-based methods. In the past researchers have implemented and evaluated the performance of many techniques. Regardless, none of the already reported works gives adequate certainty.
dc.format.extentvi,109p.
dc.languageEnglish
dc.relation118
dc.rightsuniversity
dc.titleFault detection and diagnosis in chemical and biochemical processes
dc.title.alternative
dc.creator.researcherShrivastava, Rahul
dc.subject.keywordBoosting Algorithms
dc.subject.keywordChemical and Biochemical Processes
dc.subject.keywordEngineering and Technology,Engineering,Engineering Chemical
dc.subject.keywordFault Detection
dc.description.noteList of Publications
dc.contributor.guideGupta, K.N and Dutta, N.N
dc.publisher.placeGuna
dc.publisher.universityJaypee University of Engineering and Technology, Guna
dc.publisher.institutionDeaprtment of Chemical Engineering
dc.date.registered22/07/2011
dc.date.completed10/12/2018
dc.date.awarded14/12/2018
dc.format.dimensions29.5X20.5"
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Deaprtment of Chemical Engineering

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01_title.pdf.pdfAttached File21.37 kBAdobe PDFView/Open
02_certificate.pdf.pdf16.39 kBAdobe PDFView/Open
03_abstract.pdf.pdf14.51 kBAdobe PDFView/Open
04_declaration.pdf.pdf16.15 kBAdobe PDFView/Open
05_acknowledgement.pdf.pdf11.08 kBAdobe PDFView/Open
06_contents.pdf.pdf10.94 kBAdobe PDFView/Open
07_list_of_tables.pdf.pdf10.06 kBAdobe PDFView/Open
08_list_of_figures.pdf.pdf35.84 kBAdobe PDFView/Open
09_abbreviations.pdf.pdf6.91 kBAdobe PDFView/Open
10_chapter1.pdf.pdf85.97 kBAdobe PDFView/Open
11_chapter2.pdf.pdf451.75 kBAdobe PDFView/Open
12_chapter3.pdf.pdf884.9 kBAdobe PDFView/Open
13_chapter4.pdf.pdf128.52 kBAdobe PDFView/Open
14_chapter5.pdf.pdf127.98 kBAdobe PDFView/Open
15_chapter6.pdf.pdf344.06 kBAdobe PDFView/Open
16_chapter7.pdf.pdf15.16 kBAdobe PDFView/Open
17_conclusion.pdf11.87 kBAdobe PDFView/Open
18_bibliography.pdf53.17 kBAdobe PDFView/Open
19_list of publication.pdf10.75 kBAdobe PDFView/Open


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