On-line Fault Detection and Supervision in the Chemical Process Industries, 2001: (CHEMFAS-4) : a Proceedings Volume from the 4th IFAC Workshop, Jejudo Island, Korea, 7-8 June 2001
G. Stephanopoulos, Josť Alberto Romagnoli, En Sup Yoon
International Federation of Automatic Control, 2001 - Technology & Engineering - 376 pages
This proceedings contains papers from the IFAC Symposium on On-line Fault Detection and Supervision in the Chemical Process Industries (CHEMFAS-4), held in Jejudo Island, Korea, 7-8 June 2001.
The proceedings includes theoretical contributions, as well as a wide range of industrial applications in process fault diagnosis, monitoring, and advanced supervision. The papers are organized around the following themes: fault detection and diagnosis, statistical and trend analysis, methodologies, sensor location and data reconciliation and applications.
The driving forces for on-line fault detection and improved supervision of process operation include human safety, environmental safeguards, and equipment protection, as well as economic considerations such as the improvement of product quality, increased production, and so on. These diverse incentives, together with the development and evaluation of novel methodologies for on-line process supervision and management, form the focus of the symposium and of the papers in this Proceedings. Altogether over 60 papers are presented, covering strategies including model-based and data-driven approaches, as well as knowledge-based systems, statistical techniques, and AI-based pattern recognition techniques. All the work presented is at the cutting edge of research in this dynamic field.
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abnormal alarm algorithm analysis application approach batch calculated cause Chemical Process coefficient component Comput considered corresponding cost defined described determined developed deviation disturbance dynamic effect Engineering equations error estimation event example failure fault detection fault diagnosis Figure filter flow function given graph heat identify IFAC important increase indicates industrial input inside film isolation knowledge limit linear loop matrix mean measurements method monitoring multivariate statistical neural network node noise nonlinear normal observability obtained on-line operation optimal output parameters performance plant possible prediction presented principal problem procedure proposed represents residual sample selected sensor shown shows signal simulation single situation solution statistical step structure Table techniques temperature transfer trend unit valve variables variations vector