Robust Statistics for Soft Sensor Development in Cement Kiln

Lin, Bao; Recke, Bodil; Renaudat, Philippe; Knudsen, Jørgen; Jørgensen, Sten Bay

doi:10.3182/20050703-6-cz-1902.01720

Cited by 4 publications

(1 citation statement)

References 12 publications

(12 reference statements)

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“…Therefore, robust statistical approaches were employed to pre-process process measurements in order to provide reliable results in the presence of abnormal observation (Lin et al 2005). The generic approach for data-driven fault detection can be summarized in Fig.…”

Section: Optimal Unknown Input Observermentioning

confidence: 99%

Observer-Based and Regression Model-Based Detection of Emerging Faults in Coal Mills

Odgaard

Lin

Jørgensen

2006

IFAC Proceedings Volumes

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In this paper three different approaches for fault detections are compared on example with coal mill used at a power plant. The compared methods are based on: an optimal unknown input observer, static and dynamic regression model-based detections. These approaches are compared on data from a coal mill, where a fault emerges during the test set. The conclusion on the comparison is that observerbased scheme detects the fault 13 samples earlier than the dynamic regression model-based method, and that the static regression based method is not usable due to generation of far too many false detections.

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Section: Optimal Unknown Input Observermentioning

confidence: 99%

Observer-Based and Regression Model-Based Detection of Emerging Faults in Coal Mills

Odgaard

Lin

Jørgensen

2006

IFAC Proceedings Volumes

Self Cite

View full text Add to dashboard Cite

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Online cement clinker quality monitoring: A soft sensor model based on multivariate time series analysis and CNN

et al. 2021

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Support Vector Machine-based Soft Sensors in the Isomerisation Process

Herceg

Andrijić

Bolf

2021

Chem. biochem. eng. q. (Online)

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This paper presents the development of soft sensor empirical models using support<br /> vector machine (SVM) for the continual assessment of 2,3-dimethylbutane and 2-methylpentane mole percentage as important product quality indicators in the refinery isomerisation process. During the model development, critical steps were taken, including selection and pre-processing of the industrial process data, which are broadly discussed in this paper. The SVM model results were compared with dynamic linear output error model and nonlinear Hammerstein-Wiener model. Evaluation of the developed models on independent data sets showed their reliability in the assessment of the component contents. The soft sensors are to be embedded into the process control system, and serve primarily as a replacement during the process analysersb failure and service periods.

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Robust Statistics for Soft Sensor Development in Cement Kiln

Cited by 4 publications

References 12 publications

Observer-Based and Regression Model-Based Detection of Emerging Faults in Coal Mills

Observer-Based and Regression Model-Based Detection of Emerging Faults in Coal Mills

Online cement clinker quality monitoring: A soft sensor model based on multivariate time series analysis and CNN

Support Vector Machine-based Soft Sensors in the Isomerisation Process

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