Reducing Cost of Ownership and Improving Long Term Performance of QCS Controls, 18PaperCon

The ongoing loss of an experienced workforce, combined with complex advanced controls, result in ever increasing pressure on paper manufacturers to look for Quality Control System (QCS) suppliers that differentiate on ease of use and low cost of ownership. 

In this paper, we will review recent advancements in model predictive control (MPC) that focus on providing a user experience that makes it easier to commission, maintain, and operate highly performant QCS supervisory controls. These advancements allows users to upgrade from classic, single-input-single-output, controls to MPC with minimal training effort, retaining the ease of use of classic controls while enjoying the performance improvements of MPC. These concepts are illustrated with examples from a recent installation. 

Adaptive controls can reduce the maintenance cost of a control system, and, at the same time, ensure high performance over the long term. The present and future state of adaptive QCS control is reviewed and an adaptive control framework that has been proven successful for cross directional (CD) alignment is presented. Recent research on how that framework can be extended to adaptive MPC of paper machines, leveraging new techniques from machine learning, is also presented. 

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