Measurement Selection for Model Predictive Control of the Weyerhaeuser Benchmark Digester Problem

Wisnewski, Philip A.; Doyle, Francis J.; Kayihan, Ferhan; Arkun, Yaman

doi:10.1016/s1474-6670(17)43189-2

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, no improvement in unmeasured composition disturbances was noticed. Wisnewski et al [79] and Wisnewski and Doyle [80] explored a systematic method for selecting manipulated variable (MV) and secondary measurements to design a multi-rate linear MPC for Kappa number control. The authors concluded that either modified continuous cooking (MCC) or EMCC trim flow rates should be used for the Kappa number control.…”

Section: Kappa Number Controlmentioning

confidence: 99%

A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters

2020

View full text Add to dashboard Cite

Being at the heart of modern pulp mills, continuous pulp digesters have attracted much attention from the research community. In this article, a comprehensive review in the area of modeling, control and diagnostics of continuous pulp digesters is conducted. The evolution of research focus within these areas is followed and discussed. Particular effort has been devoted to identifying the state-of-the-art and the research gap in a summarized way. Finally, the current and future research directions in the areas have been analyzed and discussed. To date, digester modeling following the Purdue approach, Kappa number control using model predictive controllers and health index-based diagnostic approaches by utilizing different statistical methods have dominated the field. While the rising research interest within the field is evident, we anticipate further developments in advanced sensors and integration of these sensors for improving model prediction and controller performance; and the exploration of different AI-based approaches will be at the core of future research.

show abstract

Section: Kappa Number Controlmentioning

confidence: 99%