Using multivariate regression techniques to analyse the performance of a steam heated drying process

J. Balderud; M.J. Wade

Using multivariate regression techniques to analyse the performance of a steam heated drying process

J. Balderud, M.J. Wade

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper

Abstract

This paper explores the use of a multivariate statistics approach to monitor and diagnose the performance of a steam heated drying process. The results presented in this paper are a first stage investigation into the capabilities and limitations of these methods when applied to this particular process. The results show that, despite the non linear behaviour of the drier process, the model predictions correspond well with the process measurements, The results also indicate ability to detect process failures, however, accurate diagnosis of the failures remains a subject for future studies.

Original language	English
Number of pages	6
Publication status	Published - 2005
Event	IEEE International Conference on Industrial Electronics and Control Applications - Quito, Ecuador Duration: 29 Nov 2005 → 2 Dec 2005

Conference

Conference	IEEE International Conference on Industrial Electronics and Control Applications
Abbreviated title	ICIECA 2005
Country/Territory	Ecuador
City	Quito
Period	29/11/05 → 2/12/05

Keywords

multivariate regression techniques
steam heated drying process
multivariate statistics

Cite this

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title = "Using multivariate regression techniques to analyse the performance of a steam heated drying process",

abstract = "This paper explores the use of a multivariate statistics approach to monitor and diagnose the performance of a steam heated drying process. The results presented in this paper are a first stage investigation into the capabilities and limitations of these methods when applied to this particular process. The results show that, despite the non linear behaviour of the drier process, the model predictions correspond well with the process measurements, The results also indicate ability to detect process failures, however, accurate diagnosis of the failures remains a subject for future studies.",

keywords = "multivariate regression techniques , steam heated drying process, multivariate statistics",

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note = "IEEE International Conference on Industrial Electronics and Control Applications, ICIECA 2005 ; Conference date: 29-11-2005 Through 02-12-2005",

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AU - Balderud, J.

AU - Wade, M.J.

PY - 2005

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N2 - This paper explores the use of a multivariate statistics approach to monitor and diagnose the performance of a steam heated drying process. The results presented in this paper are a first stage investigation into the capabilities and limitations of these methods when applied to this particular process. The results show that, despite the non linear behaviour of the drier process, the model predictions correspond well with the process measurements, The results also indicate ability to detect process failures, however, accurate diagnosis of the failures remains a subject for future studies.

AB - This paper explores the use of a multivariate statistics approach to monitor and diagnose the performance of a steam heated drying process. The results presented in this paper are a first stage investigation into the capabilities and limitations of these methods when applied to this particular process. The results show that, despite the non linear behaviour of the drier process, the model predictions correspond well with the process measurements, The results also indicate ability to detect process failures, however, accurate diagnosis of the failures remains a subject for future studies.

KW - multivariate regression techniques

KW - steam heated drying process

KW - multivariate statistics

M3 - Paper

T2 - IEEE International Conference on Industrial Electronics and Control Applications

Y2 - 29 November 2005 through 2 December 2005

ER -

Using multivariate regression techniques to analyse the performance of a steam heated drying process

Abstract

Conference

Keywords

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Cite this