Food Engineering / Gıda Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/12
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Article Citation - WoS: 18Citation - Scopus: 21Haccp With Multivariate Process Monitoring and Fault Diagnosis Techniques: Application To a Food Pasteurization Process(Elsevier Ltd., 2005) Tokatlı, E. Figen; Tokatlı, Figen; Schlesser, Joseph E.; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyMultivariate statistical process monitoring (SPM), and fault detection and diagnosis (FDD) methods are developed to monitor the critical control points (CCPs) in a continuous food pasteurization process. Multivariate SPM techniques effectively use information from all process variables to detect abnormal process behavior. Fault diagnosis techniques isolate the source cause of the deviation in process variable(s). The methods developed are illustrated by implementing them to monitor the critical control points and diagnose causes of abnormal operation of a high temperature short time (HTST) pasteurization pilot plant. The detection power of multivariate SPM and FDD techniques over univariate SPM techniques is shown and their integrated use to ensure the product safety and quality in food processes is demonstrated.Article Citation - WoS: 3Citation - Scopus: 6Fault Detection and Diagnosis in a Food Pasteurization Process With Hidden Markov Models(John Wiley and Sons Inc., 2004) Tokatlı, Figen; Tokatlı, Figen; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyHidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a high-temperature-short-time pasteurization system showed that HMM can diagnose the faults with certain characteristics such as fault duration and magnitude.