Automatic measurement and control in a panel board plant

Author

Self, Andrew ; Pearce, David

Author_Institution

Brighton Polytech., UK

Volume

5

Issue

3

fYear

1994

fDate

6/1/1994 12:00:00 AM

Firstpage

112

Lastpage

116

Abstract

Fibre quality and wetlap thickness, the most influential variables in the panel and board industries, have traditionally been manually controlled by operators. The effect of this is that product quality and, ultimately, plant efficiency and profitability are a function of operator opinion and diligence. This article describes the design and implementation of a predictive and self-learning rule-base controller in a panel board mill. The systems enable the company to automatically control product quality, meet the order-winning criteria and maximise efficiency regardless of feedstock quality. The systems also allow the user to manufacture quality board from scrap such as waste pallets, saw mill off-cuts and storm-damaged trees.<>

Keywords

adaptive control; intelligent control; manufacturing computer control; unsupervised learning; wood processing; automatic measurement; feedstock quality; fibre quality; operator opinion; panel and board industries; panel board mill; panel board plant; plant efficiency; predictive controller; product quality; profitability; saw mill off-cuts; self-learning rule-base controller; storm-damaged trees; waste pallets; wetlap thickness; Adaptive control; Industrial control; Intelligent control; Materials processing; Unsupervised learning; Wood industry;

fLanguage

English

Journal_Title

Computing & Control Engineering Journal

Publisher

iet

ISSN

0956-3385

Type

jour

DOI

10.1049/cce:19940304

Filename

289501