Online prediction of Pulp Brightness Using Fuzzy Logic Models

Sofiane Achiche; Luc Baron; Marek Balazinski; Mokhtar Benaoudia

doi:10.1016/j.engappai.2006.04.002

Online prediction of Pulp Brightness Using Fuzzy Logic Models

Sofiane Achiche, Luc Baron, Marek Balazinski, Mokhtar Benaoudia

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

The quality of thermomechanical pulp (TMP) is influenced by a large number of variables. To control the pulp and paper process, the operator has to manually choose the influencing variables, which can change significantly depending on the quality of the raw material (wood chips). Very little knowledge exists about the relationships between the quality of the pulp obtained by the TMP process and wood chip properties. The research proposed in this paper uses genetically generated knowledge bases to model these relationships while using measurements of wood chip quality, process parameter data and properties of raw material such as bleaching agents. The rule base of the knowledge bases will provide a better understanding of the relationships between the different influencing variables (input and outputs). r 2006 Elsevier Ltd. All rights reserved.

Original language	English
Journal	Engineering Applications of Artificial Intelligence
Volume	20
Issue number	1
Pages (from-to)	25-36
ISSN	0952-1976
DOIs	https://doi.org/10.1016/j.engappai.2006.04.002
Publication status	Published - 2006
Externally published	Yes

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title = "Online prediction of Pulp Brightness Using Fuzzy Logic Models",

abstract = "The quality of thermomechanical pulp (TMP) is influenced by a large number of variables. To control the pulp and paper process, the operator has to manually choose the influencing variables, which can change significantly depending on the quality of the raw material (wood chips). Very little knowledge exists about the relationships between the quality of the pulp obtained by the TMP process and wood chip properties. The research proposed in this paper uses genetically generated knowledge bases to model these relationships while using measurements of wood chip quality, process parameter data and properties of raw material such as bleaching agents. The rule base of the knowledge bases will provide a better understanding of the relationships between the different influencing variables (input and outputs). r 2006 Elsevier Ltd. All rights reserved.",

author = "Sofiane Achiche and Luc Baron and Marek Balazinski and Mokhtar Benaoudia",

year = "2006",

doi = "10.1016/j.engappai.2006.04.002",

language = "English",

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T1 - Online prediction of Pulp Brightness Using Fuzzy Logic Models

AU - Achiche, Sofiane

AU - Baron, Luc

AU - Balazinski, Marek

AU - Benaoudia, Mokhtar

PY - 2006

Y1 - 2006

N2 - The quality of thermomechanical pulp (TMP) is influenced by a large number of variables. To control the pulp and paper process, the operator has to manually choose the influencing variables, which can change significantly depending on the quality of the raw material (wood chips). Very little knowledge exists about the relationships between the quality of the pulp obtained by the TMP process and wood chip properties. The research proposed in this paper uses genetically generated knowledge bases to model these relationships while using measurements of wood chip quality, process parameter data and properties of raw material such as bleaching agents. The rule base of the knowledge bases will provide a better understanding of the relationships between the different influencing variables (input and outputs). r 2006 Elsevier Ltd. All rights reserved.

AB - The quality of thermomechanical pulp (TMP) is influenced by a large number of variables. To control the pulp and paper process, the operator has to manually choose the influencing variables, which can change significantly depending on the quality of the raw material (wood chips). Very little knowledge exists about the relationships between the quality of the pulp obtained by the TMP process and wood chip properties. The research proposed in this paper uses genetically generated knowledge bases to model these relationships while using measurements of wood chip quality, process parameter data and properties of raw material such as bleaching agents. The rule base of the knowledge bases will provide a better understanding of the relationships between the different influencing variables (input and outputs). r 2006 Elsevier Ltd. All rights reserved.

U2 - 10.1016/j.engappai.2006.04.002

DO - 10.1016/j.engappai.2006.04.002

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VL - 20

SP - 25

EP - 36

JO - Engineering Applications of Artificial Intelligence

JF - Engineering Applications of Artificial Intelligence

IS - 1

ER -

Online prediction of Pulp Brightness Using Fuzzy Logic Models

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