The wood drying process has evolved significantly in recent years. Think about how moisture content (MC), an important drying quality indicator, is measured and tracked. With a few notable exceptions, planer mills now feature an in-line moisture meter to measure the MC of all pieces in a kiln load. Many lumber manufacturers have installed dielectric measurement systems inside their kilns to improve kiln shutdown, a key step when it comes to maximizing quality, productivity, and energy consumption. In addition, MC can now be estimated at the exit of the sawmill by using information on package mass, wood volume and species density.
Grade and MC information can also be grouped per load at the planer, something that is more than highly recommended when a traceability system has been implemented.
Most recently, the real-time monitoring of conditions has been integrated with the detection of kiln operating problems to ensure compliance with drying conditions and to optimize equipment productivity.
Despite this progress, drying precision can be further improved. Kiln loads are still dried without being optimized from a point of view of quality and/or productivity and/or energy efficiency.

Pieces of the puzzle to put in place

A major challenge is consolidating all this information in one location where analyses can be carried out, and findings and recommendations made.
It’s as if each information component was a puzzle piece. While most mills have all the puzzle pieces, they have not necessarily put them in place.
How much times has it been necessary to track down the following information in different places at the mill during technical support interventions in the form of coaching?

• Composition of the kiln load (often found in kiln load print reports);
• Freshness of the bundles loaded onto loading trains and time between package formation at the sawmill and the planing operation (information often compiled by hand and not used);
• Drying schedule and compliance with conditions (printed drying chart or available in the control system);
• Direct and indirect MC values provided by equipment integrated into kilns and used for shutdown (in the control system or Excel files);
• Moisture content values from validation hot checks using hand-held moisture meters (hard copies or in an Excel file);
• Length of time between drying completion and planing;
• Data on final grade at the planer, MC measurement, and planer productivity.

Many drying-related issues can be resolved by using the information provided by all these puzzle pieces. Grade and MC distribution per load at planing makes it possible to target successful and problematic loads after the fact and then to guide actions to improve the process. This obviously requires – as has been said – being able to proceed to the traceability of the bundles at the planer. This is a critical piece of the puzzle that the industry must prioritize to promote advancement in the drying process.

Judge drying success first

A successful kiln load depends on successfully attaining a certain level of moisture content and controlling a small percentage of tolerated green lumber as well as the grade distribution that leads directly to product value. Both are closely linked. The goal of wood drying is to reduce the MC to the level required by the market. Too much wet wood will result in downgrading. On the other hand, it is known that over-drying wood will cause more drying defects due to higher shrinkage levels. Shutting down kilns at the right time at the greatest frequency possible becomes a key element of successful drying.
Once it is ensured that every kiln load at the planer that contains an acceptable percentage of green wood based on a set target is analyzed, a decision on when to shut down the kilns can be made. Loads whose green wood proportion exceeds the set limit become non-compliant, as do charges containing no green wood whatsoever.
A success criterion for the grade must also be set by each company based on its objectives.
Consequently, there is a need to integrate baseline data and automated updates per load on drying success with just a few mouse clicks on one platform and per kiln load. It’s a tool that should be prioritized to assist the drying operation.
Loads that meet both the MC and grade compliance criteria are deemed a success. It must be possible to establish links between the raw material, how the wood has been dried, kiln shutdown, storage periods, and drying success. These winning conditions will become a guide for obtaining more compliant loads. A recipe for success may vary from one product to another.
Loads that do not meet the MC criterion once drying has been completed present a problem regarding kiln shutdown. Special attention must be paid to the MC measured at the kilns (by integrated systems or by validation through portable instruments) and the MC measured in the final product at the planer.
Loads that meet neither the grade criterion nor the MC criterion must first be brought into compliance with the MC criterion. Once that has been done, there is a strong likelihood that the grade will improve. If it does not, over-drying and other potential causes that may or may not be related to drying will have to be looked at. And this is where an integrated system becomes even more compelling as it will help in quickly pinpointing potential causes that could lead to changes. Changes guided by knowledge of the process will make it possible to improve the operation and a company’s key performance indicators at the same time.

Linking the operation to the company

There is often a tendency to confuse “key performance indicator” with “process performance indicator” or operational metric. A key performance indicator relates to a business’s performance, is found at the highest level, can be used for several departments, and is used for strategic business decisions. A process performance indicator, or an operational metric, on the other hand, is more closely tied to the performance of a specific process.
For example, final grade distribution at the planer is more a key performance indicator for a business, while products’ final MC distribution is more closely tied to a drying process performance indicator.
There are, of course, important connections between both and any contributions made to improve the performance indicators related to final MC distribution will help to improve the key performance indicator for final grades.
Good process knowledge will help to determine and prioritize the right process performance indicators. Process specialists will assist greatly with this prioritization process and are a centrepiece of the overall optimization exercise.
Tracking both performance indicators’ levels concurrently and connecting them will lead to enhanced process optimization. Obviously, actions and follow-ups will be necessary for performance to improve.
In conclusion, tools, knowledge of the process, actions, and tracking enable the overall improvement of process and business performance.