"MINIMUM EFFORT" ADAPTIVE CONTROL OF PULP BRIGHTNESS

Bill Gough / John Kay
Research and Development Manager / Product Manager
Universal Dynamics Technologies Inc.
Richmond, British Columbia,  Canada

INTRODUCTION

Adaptive control is a general term that has been applied to control schemes that automatically adjust their control characteristics under various operating conditions to maintain control of a process. The adaptive mechanism used and the degree to which the control scheme adapts to the process result in a wide variety of adaptive process controllers.

The Universal Adaptive Controller (UAC) is a breakthrough in adaptive control based on new theory developed by Dr. Guy Dumont at the University of British Columbia. The advantage of the UAC is that it does not require a predetermined model of the process to be controlled. The UAC's unique feature is its ability to learn the process transfer function while it is controlling the process. The UAC adapts its control action to changing process dynamics and dead times by building and continuously updating a mathematical model of the process being controlled. The UAC is also able to learn the effects of measured process disturbances (feedforward signals), resulting in further improvements. The UAC uses its mathematical model of the process transfer function to forecast process response so that set point is attained as quickly as possible with little or no overshot, using a minimum of control effort (actuator manipulation).

BLEACH PLANT BRIGHTNESS CONTROL

The UAC was installed in the bleach plant of a western Canadian mill to control the compensated brightness and after tower brightness of the pulp in the first bleaching stage.

The objective of the compensated brightness control is to regulate the dosage of chlorine dioxide (ClO2) in order to achieve a given pulp brightness after a brief reaction time (about 1 minute) with some residual ClO2 remaining to complete the bleaching reaction in the 30-minute retention tower. A measurement of pulp brightness after the tower is used to determine the set point for the compensated brightness controller. A control schematic is given in Figure 1.

Compensated Brightness Control

The compensated brightness signal is created from a measurement of the optical brightness of the pulp and the level of residual ClO2 before the pulp enters the retention tower.

The existing controller was a distributed control system (DCS) based PID controller.

The control problems on this loop are:

- one minute dead time between ClO2 dosage change and change in compensated brightness,

- process gain changes due to sensitivity changes of the residual ClO2 sensor at different levels of ClO2 substitution for chlorine (Cl2) (60% ClO2 or 100% ClO2),

- process gain changes due to different wood species and variations in lignin content (K-number) of the pulp from the digester.

Figure 1. First Stage Pulp Brightness Control Schematic

A comparison of PID versus UAC performance was done over a period of 16 hours. Figure 2 shows the distribution of data compared to the set point for PID control and UAC control. The PID had a standard deviation of 0.84, while the standard deviation of the UAC was 48.8% lower at 0.43.

Figure 2. UAC/PID Distribution of Compensated Brightness from Set Point (100% ClO2)

After Tower Brightness Control

A brightness sensor installed immediately following the first bleaching tower is used to adjust the set point of the compensated brightness controller to reduce deviations in after tower brightness and kappa number.

Due to the long and varying dead times, setting this control loop up properly using PID algorithms is difficult and time-consuming. Dead times vary with production rate between 25 and 40 minutes. Care must be taken to ensure that the loop is stable at all production rates.

A tool available to most DCS users is the Smith Predictor PID. A properly tuned Smith Predictor can significantly increase performance for loops with long dead times. However, if the dead time estimate is not accurate, the Smith Predictor will not work. When a process has varying dead times, it becomes much more difficult to enter accurate dead time values into the Smith Predictor. To get stable control, the action of the Smith Predictor must be held back or slowed down to compensate for errors in the dead time estimate, which leads to less than optimum control of the after tower brightness deviations.

The Smith Predictor PID had a standard deviation of 1.57 while the standard deviation of the UAC was 48.1% to lower at 0.81. Figure 3 shows the distribution of after tower brightness values compared to set point for both the Smith Predictor PID controller and the UAC controller.

Figure 3. UAC/PID After Tower Brightness Distribution from Set Point

CONCLUSIONS

Adaptive Control solves difficult control problems such as those caused by:

• long process time delays,
• non-linear process characteristics,
• changes in process gains and time constants.

The unique ability of the UAC to learn the process behaviour automatically and continuously ensures optimum process performance all the time. There is no need to re-tune the controller after it is started up. The problems of long development and set-up time, repeated tuning and poor reliability associated with controllers such as Smith-Predictor PID and model-based controller designs are solved with the UAC.

The superior control performance of the UAC reduces product variability and enables the potential quality improvement benefits of supervisory and statistical process control systems to be realized. The cascade effects of many small improvements provided by tighter control on individual loops can improve the complete process or plant substantially.

The UAC is a new tool available for the control engineer to implement the continuous improvement concepts advocated by Deming [1] and Juran [2] in their Total Quality philosophies.

REFERENCES

[1] Deming, W.E., "Out of the Crisis", M.I.T. Center for Advanced Engineering Studies, 1989.

[2] Juran, J.M., "Juran's Quality Control Handbook", McGraw-Hill, 1988.