Leveraging Features of MPC for Optimizing Paper Machine Profitability, TAPPICon24

Simple approaches to process control involve controllers tasked with keeping controlled variables at setpoints by adjusting manipulated variables. Following this approach and keeping controllers well-tuned will result in stable process operations with quick rejection of disturbances and low variability around the chosen operating targets. However, keeping controlled variables at setpoint may not necessarily lead to optimal profitability. For example, setpoints may need to be adjusted as process behavior and process economics change; often production rates should be maximized while ensuring that quality variables stay within specifications; when there are more process inputs available than outputs to be controlled the more expensive inputs should be reduced as much as possible. All of these situations are manageable by human operators; however, operators in most mills have a host of tasks and even the most diligent and motivated will not be able to monitor operations on a minute-by-minute basis to catch changes as they occur and keep the mill running optimally at all times.

Fortunately, most model predictive control (MPC) technologies come equipped with features needed to help automate setpoint selection, throughput maximization, and energy cost minimization. Model predictive control is a well-established technology that will be familiar to many in the papermaking process control community. By using a model of how the process outputs will respond to changes in process inputs, these controllers are able to choose input moves that will steer future process movements along optimal paths. These controllers have decades of demonstrated success across many industries due to their inherent ability to handle time delay, multivariable interactions, and process constraints. However, MPCs often will have additional capabilities. Two important examples are: (1) the ability to support range control – keeping a controlled variable within a range rather than strictly at a setpoint, and (2) a flexible optimization layer that adjusts MV and CV targets to optimize profitability.

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