I recently exchanged some emails with Emerson’s Sergei Kuznetsov, part of TAG projects organization, and based in Minneapolis, Minnesota. Sergei is principal control systems engineer, certified professional engineer, and has an MSEE degree.
He shared with me an article, Staying In Control that he had written for Engineered Wood Journal magazine. The article describes ways to improve flake blending and mat forming in older oriented strand board (OSB) mills. For those unfamiliar with OSB, Wikipedia defines it:
Oriented strand board, or OSB, or waferboard, or Sterling board (UK) or SmartPly (UK & Ireland) is an engineered wood product formed by layering strands (flakes) of wood in specific orientations.
The issue with many older OSB mills built in the 1970s and 1980s is that they have large transport delays in the conveyors, which connect process equipment spread across the mill. Sergei notes that the problem most adversely impacts the blender inflow control and mat forming bin level control. These areas have large impact on the quality and consistency of the final product.
Such a problem of course is not limited to OSB production lines. Any process that involves a particulate material via conveyers can potentially have its deadtime affecting efficient control of related process variables.
From a control strategy perspective, Sergei described the challenge and solution:
A conventional PID (proportional-integral-derivative) feedback controller will not work well in applications with long process deadtimes. Good control can be accomplished, even in older mills, by employing the Smith Predictor control algorithm to address processes with significant transport delays or deadtimes.
In some extreme cases, this deadtime can be five minutes from the dry wood bin to the blender and then to the forming bin. If this deadtime is ignored in the tuning of the forming bin level controller and wood flow controller, process changes will prompt overcorrections and likely oscillatory conditions, unless the controllers are substantially detuned. Detuning causes sluggish response to changes and impacts the quality and consistency of the strand board.
Sergei detailed how the Smith Predictor algorithm addresses this deadtime:
The Smith Predictor uses a process model to calculate predicted process change in response to a control action as if there is no deadtime. This change is added to the PID process variable so the controller is made to “believe” that the corrective action actually took effect immediately, and thus will not take additional action. With such a modification, the PID controller can be aggressively tuned so it can provide good control of its process variable.
For the blending wood flow control, the flow can deviate due to the woodpile shape or differences in the bins that feed the conveyor. With a Smith Predictor accounting for transport deadtime, the loops can be aggressively tuned to handle the natural deviations in flow and bin switching. By closely controlling the wood flow, the proper ratios of wood to wax/resin can be maintained in the blender.
For the forming bin, controlling this level in older mills is notoriously difficult and typically requires a high level of operator intervention. Deadtime from long conveyors and blender retention time is a large part of this control challenge. A high forming bin level can cause unplanned shutdowns and bin level deviations can impact quality and consistency. A PID-based level controller with a Smith Predictor can account for this deadtime so that the level loop can be tuned aggressively to handle changes in the process and hold the level steady.
Sergei shared how these two loops are cascaded where the level controller is the master loop and the flow controller is the slave loop. He wrote:
When a forming bin level gets too high, the master sends a lower flow setpoint to the flow controller. If the level gets too high, flow setpoint is reduced. Both slave and master have their respective process deadtimes compensated by the Smith Predictor algorithm, so the cascaded pair works almost as if there is no deadtime at all.
The Smith Predictor does very well in processes with a fixed deadtime. When the deadtime varies, advanced process control (APC) strategies like Model Predictive Control can help provide reliable control.