Wood is good, but it doesn’t always behave. Getting the moisture content in four thousand pieces of lumber in a dry kiln to stack up neatly along a bell-shaped curve is almost impossible for every charge. Sometimes it works but sometimes it doesn’t. Much of the batch will fall into place but when there are renegade pieces with high moisture content, they skew the end results. 

“Every piece of wood is different,” says Ed Wagner, president of Wagner Electronics, a worldwide leader in moisture measurement technology. “That’s just the way wood is. This has been an issue for lumber producers for as long as they’ve been drying lumber in large batches. Some of the wood in a charge will end up at 15 percent moisture content, some a little below, and some at 25 to 30 percent. This produces a long tailed statistical curve (called Lognormal) and basing drying decisions on a Normal bell-shaped curve can lead to inaccuracies.” 

To illustrate the problem, let’s assume the target MC for a charge is an average of 15 percent with a standard deviation of three. Because some pieces will dry down to only 20 to 30 percent, the final average will fall more into the 16.5 percent range. Because of these numbers, the kiln operator may decide to compensate by adding extra hours of drying time when, in reality, most of the wood is in the target moisture range. As a result, future batches may become overdried. 

Batch after batch, the errors can escalate, resulting in degraded and inconsistent products, slower production throughput and unnecessary energy consumption. 

“According to industry experts, this can result in significant losses,” says Wagner. 

Michael Milota, professor at Oregon State University’s Wood Science and Engineering Department, and a well-known expert on the topic, says that in a 1997 study* on white fir, “we determined that $3.23/mbf of lumber value was lost for each percent moisture content decrease.  Even in a $300/mbf market this would be well over $2/mbf loss for each percent moisture content decrease, or $400,000 per year to a mill drying 100 mmbf annually.”

Not your average math 

These losses and other negative effects of overdrying can be reduced if drying statistics are interpreted correctly.

Researchers Thomas Maness and Catalin Ristea at the University of British Columbia (UBC) worked with Wagner Electronics engineers to come up with a revolutionary solution - reinvent the math used to develop the statistical averages.  This new math model replaces the misleading MC average with another number when the standard math doesn’t make sense. It’s the foundation of a new process from Wagner called Moisture Management Grade Recovery (MMGR).

“The name sums it up,” says Wagner. “We’ve developed this process to help mills manage moisture in order to optimize lumber quality. It can also improve lumber consistency, allow higher throughput and save energy.”

MMGR is currently in the process of being implemented in four lumber mills and is gathering data which will be used to help develop the software for refining this process. 

Michael Milota has taken a year’s sabbatical leave to work with Wagner and finalize this step. “My observation over 20 years of working with mills is that they do not relate planer mill moisture measurements to what happened in the production process,” he explains. “Many mills would greatly benefit by simply graphing kiln performance over time along with other process variables.  SPC could provide addition benefits, however, using traditional SPC to analyze drying data can often give misleading results because the data is not always normally distributed.”