English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this study, Kappa number prediction and diagnosis in continuous Downflow Lo-Solids cooking application is investigated. Gustafson’s Kappa number model is applied for the prediction of the blowline Kappa number. New cooking temperature set point is solved iteratively based on the difference between the predicted and target blow-line kappa numbers. The main active variables for the Kappa number are monitored using self-organizing map (SOM). The diagnosis and Kappa control are combined into a fault tolerant system. The data is collected from industrial continuous Downflow Lo-Solids cooking digester. Good results were achieved using the proposed approach.

ENHANCING KAPPA NUMBER CONTROL IN DOWNFLOW LO-SOLIDSTM DIGESTER USING DIAGNOSIS AND MODELLING