Automatic measurement and control in a panel board plant

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.<>