Adaptive‐predictive control of kamyr digester chip level

Abstract

Kamyr digester chip level poses a difficult control problem, because the chip column dynamics are complex and it is difficult to measure the chip-liquor interface. Control of the chip level is required to smooth digester operation and to stabilize the residence time. Conventional methods rely largely on fixed parameter controllers which are unable to compensate for changes in the process dynamics. Manual controller retuning is often required.An adaptive chip level controller based on Generalized Predictive Control (GPC) is presented. Closed-loop time series identification methods were used to develop the process model and to design the controller. A significant reduction in chip level and P-number variability has been observed since commissioning the controller on an industrial digester in September 1988. The adaptive controller has eliminated the need for manual retuning, reduced the need for operator intervention, and demonstrated the potential to predict hang-ups.

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

“…Control of continuous pulp digesters has been widely studied by both academic and industrial researchers. Initially, the research focus was set on the control of the chip level of continuous pulp digesters [104,106,107]. Eventually, the focus shifted to achieving maximum pulp production at a specific Kappa number with minimum chemical and energy consumption.…”

“…Dumont et al [104] and Belanger et al [105] showed self-tuning regulators (STR) using both chip feed and blow flow rate had comparable performances for Kamyr digester level control. Allison et al [106,107] demonstrated a MISO generalized predictive control (GPC) where the blow flow and the chip feed rate were manipulated simultaneously to control the chip level. However, the controller was discontinued after six months of operation due to increased Kappa number variability.…”

Control loop performance monitoring

“…The noise created a bias at one, but even if this was ignored, the data gave an inconsistent delay estimate, ranging between two and four. While this was in the range of likely values [16], there is no guarantee that the estimated change was actually due to a change in the delay and was not actually caused by the disturbance. As a result, a delay of two sampling intervals suggested in [ 161 as occurring most often will be used to determine $La.…”

Modified subspace identification for long-range prediction model for inferential control

“…Besides Kappa number, chip level is another important control variable, which effects the residence time of the chips in the reactor, and hence the extent of delignification process. Much research has been focused on chip level control (Allison, Dumont, Novak, & Cheetham, 1990;Wells, 1990). Although strong correlation exists between the chip level and the Kappa number, it is already realized that controlling the chip level tightly does not lead to good Kappa number control (Allison et al, 1990).…”

“…Much research has been focused on chip level control (Allison, Dumont, Novak, & Cheetham, 1990;Wells, 1990). Although strong correlation exists between the chip level and the Kappa number, it is already realized that controlling the chip level tightly does not lead to good Kappa number control (Allison et al, 1990). In this study, the cooking temperature is used to control the Kappa number, while chip level control is achieved through manipulations of the blow flow.…”