On-line Optimizer

Integrated pulp and paper mills are complex systems. The workflow consists of many interdependent sub-process. Disturbances at one stage can quickly propagate to other steps and lead to considerable production losses, quality deviations and wastage of material. Due to the many variables involved simultaneously, it is difficult for the human mind to work out the dynamics, predict the behavior of the system and take appropriate action when disturbances occur.

The plant’s advanced control system must maintain the desired quality under normal operating conditions. Besides reacting to disturbances, it must also permit individual sub-systems to be throttled to save energy at times and allow them to be closed down for maintenance – all while keeping output as close to the desired level as possible.

ABB’s Pulp and Paper On-line Optimizer helps meet these objectives. It predicts the dynamic behavior of the system, considers all constraints and suggests suitable remedial actions when disturbances occur, thereby achieving efficient production optimization. Material flows between sub-processes are simulated and forecasts computed from the present state. Consequently, the best possible operating strategy is charted to maximize production, and minimize the risk of unplanned stops and consequences of required stops or sudden disturbances.

• Increased paper production.
• Savings in make-up chemicals.
• More stable quality of active production chemicals.
• On-line identification of production bottlenecks easily viewed by the web-based user interface indicating current and future bottlenecks.
• Improved decision support for production management. When operation decisions must consider many secondary conditions in the mill, the on-line optimizer provides invaluable alternative scenarios for production.
• Common operation strategy for all shifts and control rooms. By using the on-line optimizer, operation management can systematically prioritize between different objectives and targets. When the preferred prioritization is configured in the database and the optimizer’s recommendation adopted, it is easier to motivate the operation strategy than before.
• More efficient preparation of the mill prior to maintenance stops is desirable. When a maintenance stop is introduced in the prediction horizon, the optimizer automatically considers this constraint to minimize production loss. It is also possible to evaluate a change in maintenance stop time and quantify the consequence of a longer than expected stop.
• Identification of faulty measurements by using state estimation diagnostic results.