Pulp & Paper

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.

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In several parts of the world including North America, Emerson Process Management sells some of its products and services through local business partners. I came across a great Pulp & Paper magazine article, Control Valve Management Can Pay Off Big, written by Jeff Klatt. Jeff is with one of these local business partners, R.E. Mason.

Jeff recounted his experiences as a large paper mill's asset manager. What struck me about the article were not the technologies they ultimately applied, but rather his systematic approach to process improvement. I'll highlight some of the steps he recounts in the article to see if they might spur some ideas for improvement in your operations.

Jeff cited a study conducted by Emerson's Fisher Valve business that found that 80% of the control valves used by process manufacturers were not operating within their optimum parameters. Getting process improvements by addressing these was a large part of last week's post, Start with the Basics to Reduce Process Variability.

He described his initial step:

It seemed logical to first get acquainted with the valves in the mill and understand their roles in the papermaking process. One-by-one, I visited valves throughout the three main sections of the mill -utilities, fibers and product (papermaking) - documenting every one and building a personal database. Identifying, locating, and visually inspecting nearly 1,600 control valves in the mill turned out to be a monumental task that took months to complete.

Through this tedious process, he also engaged operations, which:

...explained which control loops had the greatest effect on product quality, productivity, and safety/environmental considerations. This knowledge was essential in establishing the most important valves, and in the end about 25% of all the valves were prioritized as critical to the mill's mission. These became the valves on which the majority of maintenance attention was focused.

As is often the case, this tedious work lays the foundation for future savings. He also had all the storerooms spare parts identified, tagged and catalogued. This effort allowed greater use of existing stock and fewer purchases of new parts, which improved the mill's working capital. In one year alone, 20 good control valves taken out of service and put into one of the storerooms were returned into service saving $55,000 (USD) in cost.

The prioritization of the critical control valves also provided focus on where to apply the technologies to improve the performance of the process. Jeff and team used the Flowscanner tool to find out more about the condition of the highest priority valves to direct the maintenance efforts. Also, digital valve controllers were added to these critical control valves over time to provide real-time diagnostics with the AMS software to begin a program of predictive maintenance. A valve's signature can be compared with its baseline performance to identify problems. These can be addressed before actual failures or variability-creating conditions occur. Jeff's team documented $50,000 a year in maintenance cost savings.

Jeff highlighted other savings such as a valve variability problem on a CIO2 flow valve being identified and addressed resulting in an annual savings of a $140,000. Another was documenting the useful lifecycle extension of 162 tested valves by an average of two years. Calculated cost savings were $86,000.

While the savings are impressive because they reoccur over time, the approach is what I found instructional. It started with a commitment to focus time and energy on these control valves because of their critical role in the process. Next was the discipline to analyze the current state and work with operations to identify the most critical control valves. This process laid the groundwork for the application of some of the technologies described to achieve lower costs and greater efficiency. From Jeff's quantified results, it appears this focus paid dividends.

I saw an email about a success at a northern U.S. paper maker that set a new production record. To whom did they attribute this success? Since this is a blog about the experts around Emerson Process Management, you might guess the answer. And you'd be correct. They attributed their new production record to the work of our Control Performance team and their process and control study process.

I caught up with Andrew Waite, a principal process control consultant on the Control Performance team. Andrew began the study by using the EnTech toolkit which collects data from a variety of sources including pneumatic controllers, 4-20mA analog values, and can import digital data from smart field devices and digital automation systems using the OSIsoft PI data historian. The toolkit performs analysis and tuning recommendations based upon the data it collects.

Andrew noted that he uncovered all of the typical problems: tuning, control strategy issues, control valve problems, and process design limitations. The mill's maintenance department went to work fixing the control valve issues while Andrew provided tuning recommendations and improvements that could be made to the existing control strategies.

The mill attributed the increased production to taking care of the basics and having a fresh set of eyes come in to audit the existing performance. Not too bad for a couple of weeks work.

We discussed improvement of multi-fuel boilers in an earlier post. Similarly, pulp and paper manufacturers often wrestle with chemical recovery boilers because of the complexity of the combustion process. This complexity is largely driven by the variability in the "fuel" (black liquor) and often by swings in production rate.

The variation in the BTU content of the incoming black liquor can cause difficulty in meeting the emissions restrictions, can lead to fouling of the boiler, may impact boiler efficiency, and can limit liquor throughput. Safety is also a major concern around a recovery boiler process.

Bob Sabin, a consultant in Emerson's Industrial Energy Solutions organization described the challenge as maximizing liquor throughput while minimizing the fouling of the upper boiler and maintaining optimal unit thermal efficiency. This can be done if the boiler combustion controls are configured to compensate for liquor BTU changes.

The process Bob and the team follow with pulp and paper manufacturers typically begins with an analysis where they measure the mills operating performance and compare it with world class performance. Some benchmarks include: maintaining excess oxygen at 1.5% to maximize unit efficiency, maximizing liquor throughput to either permit or steaming limits, minimize fouling to require one water wash per year, and running the recovery boiler in fully automatic mode more than 95% of the time.

Through this benchmarking process deficiencies and mechanical design limits can be identified and corrected. The economic benefits of process improvements can also be calculated.

Next a detailed field audit of valves, instrumentation, wiring, and control system performance is performed to find areas requiring attention.

With this assessment completed a complete cost estimate and return on investment calculation and justification can be developed to improve the performance of the recovery boiler. The expertise of the team has been packaged into a SmartProcess Recovery boiler solution which encompasses design, installation, commissioning, start-up, and operations personnel training.

Pulp and paper manufacturers typically experience project payback in three to six months through increased liquor throughput, better thermal efficiency, water wash reductions, and reduced variability in green liquor reduction.

As process manufacturers grapple with high fuel costs to create the steam for their processes, they often look to increase the use of biomass and alternate fuels in their boilers.

The key measurement is typically the cost per pound of steam. This can be reduced by maximizing the use of cheaper fuels like wood, stoker coal, and other forms of biomass while minimizing the use of natural gas and oil.

I spoke with Chip Rennie in Emerson's Industrial Energy Solutions organization on the control challenges of operating boilers when running non-fossil fuels. These fuels can vary in moisture, consistency of particle size, BTU content, combustion air requirements, and boiler emissions performance limits.

From Chip and the consulting team, well operating multi-fuel boilers can often generate 90% of the plant's steam, operate in automatic control over 95% of the time, minimize carbon in ash, and maintain emissions to specified levels.

Chip stresses the key to optimizing the operation of these boilers begins with an assessment of the mechanical components and instruments. Optimum business results cannot be achieved if these underlying components greatly limit performance. Examples of issues to be resolved include include fuel conveyor changes, fuel bins and distribution equipment, overfire or undergrate air system modifications, fan upgrades, or damper improvements.

Chip and his team have bundled their expertise on multi-fuel boilers into a SmartProcess application and call it SmartProcess Boiler. This application provides complete automatic control of the boiler at all times including start-up, automatically adjusts for changing fuel BTU per volume, and the system allows a multi-fuel boiler to be used as a swing boiler while burning least cost fuels.

The application automates many functions that are often done manually and allows a higher percentage of steam to be generated with biomass or alternate fuels.

Projects are typically done as a turnkey including design, installation, commissioning, start-up and training of the operations staff to run the boiler using the newly optimized equipment, firing methods, and control tools. Given the high costs of fossil fuels today, payback on the entire project is typically 3 to 6 months.