Data-driven fuzzy modelling of rotary dryer

Leena Yliniemi, Jukka Koskinen, K. Leiviskä

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)

Abstract

It was examined how different fuzzy modelling approaches, such as neuro-fuzzy, fuzzy clustering and linguistic equation methods, apply to the modelling of a rotary dryer. Because rotary drying, one of the oldest process in industry, is a highly nonlinear, strongly interactive multivariable process, its modelling is a demanding task. Its mathematical model, consisting of partial differential equations with several experimental parameters, is very complex and cumbersome. Therefore, the data-driven model is attractive, especially because many experimental observations and operating experience exist. The paper describes the fuzzy modelling approaches applied to the modelling of a rotary dryer. The applicability of different approaches has been evaluated by simulations, with the data collected from a pilot plant rotary dryer. The performance was estimated by an error index root means squared method and by comparing the modelling results with the results achieved by a linear regression model and a neural network model. The results show that neuro-fuzzy, fuzzy clustering and linguistic equation methods apply well, and no big differences can be detected between the methods.

Original languageEnglish
Pages (from-to)819-836
Number of pages18
JournalInternational Journal of Systems Science
Volume34
Issue number14-15
DOIs
Publication statusPublished - 2003
MoE publication typeA1 Journal article-refereed

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Fuzzy Modeling
Data-driven
Fuzzy clustering
Linguistics
Neuro-fuzzy
Fuzzy Clustering
Modeling
Pilot plants
Linear regression
Partial differential equations
Drying
Process Modeling
Linear Regression Model
Neural Network Model
Mathematical models
Neural networks
Partial differential equation
Roots
Industry
Mathematical Model

Cite this

Yliniemi, Leena ; Koskinen, Jukka ; Leiviskä, K. / Data-driven fuzzy modelling of rotary dryer. In: International Journal of Systems Science. 2003 ; Vol. 34, No. 14-15. pp. 819-836.
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Yliniemi, L, Koskinen, J & Leiviskä, K 2003, 'Data-driven fuzzy modelling of rotary dryer', International Journal of Systems Science, vol. 34, no. 14-15, pp. 819-836. https://doi.org/10.1080/00207720310001640304

Data-driven fuzzy modelling of rotary dryer. / Yliniemi, Leena; Koskinen, Jukka; Leiviskä, K.

In: International Journal of Systems Science, Vol. 34, No. 14-15, 2003, p. 819-836.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Data-driven fuzzy modelling of rotary dryer

AU - Yliniemi, Leena

AU - Koskinen, Jukka

AU - Leiviskä, K.

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AB - It was examined how different fuzzy modelling approaches, such as neuro-fuzzy, fuzzy clustering and linguistic equation methods, apply to the modelling of a rotary dryer. Because rotary drying, one of the oldest process in industry, is a highly nonlinear, strongly interactive multivariable process, its modelling is a demanding task. Its mathematical model, consisting of partial differential equations with several experimental parameters, is very complex and cumbersome. Therefore, the data-driven model is attractive, especially because many experimental observations and operating experience exist. The paper describes the fuzzy modelling approaches applied to the modelling of a rotary dryer. The applicability of different approaches has been evaluated by simulations, with the data collected from a pilot plant rotary dryer. The performance was estimated by an error index root means squared method and by comparing the modelling results with the results achieved by a linear regression model and a neural network model. The results show that neuro-fuzzy, fuzzy clustering and linguistic equation methods apply well, and no big differences can be detected between the methods.

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