Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 819-836 |
| Number of pages | 18 |
| Journal | International Journal of Systems Science |
| Volume | 34 |
| Issue number | 14-15 |
| DOIs | |
| Publication status | Published - 2003 |
| MoE publication type | A1 Journal article-refereed |
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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 journal › Article › Scientific › peer-review
TY - JOUR
T1 - Data-driven fuzzy modelling of rotary dryer
AU - Yliniemi, Leena
AU - Koskinen, Jukka
AU - Leiviskä, K.
PY - 2003
Y1 - 2003
N2 - 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.
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.
U2 - 10.1080/00207720310001640304
DO - 10.1080/00207720310001640304
M3 - Article
VL - 34
SP - 819
EP - 836
JO - International Journal of Systems Science
JF - International Journal of Systems Science
SN - 0020-7721
IS - 14-15
ER -