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Future of Lumber Drying Lies in Predictive Control
New technology for controlling lumber drying process shows promise, according to researchers at Penn State University.
By Sarah Evans
Date Posted: 10/1/2005
A new process of drying lumber could decrease drying time and save manufacturers millions of dollars in energy costs. The process, developed by researchers at the Pennsylvania State University, combines traditional drying techniques with more modern ones.
Unlike traditional dry kiln operation systems that rely on reactive controls to maintain target drying conditions, the new process will provide predictive controls through the utilization of "time series controllers."
The controllers will use statistical techniques to analyze information from the kiln sensors, recognize the trends of the variables and predict future conditions of the wood and kiln environment. Preemptive adjustments can then be made to minimize deviation from optimal drying conditions.
"In 93% of one hundred simulated drying runs, these time series controllers performed better than schedule-based control as performed by a professional master kiln operator, both in time to objective and variability over the drying period," said Penn State researcher Chuck Ray.
The reduced variation drastically cuts wasted time and energy that would be required to adjust the heat under the schedule-based system. The kiln time gained will be valuable in that it will allow for increased production. Grade recovery will potentially be increased as well as temperature fluctuations will be reduced.
The research has been proven and recently published in Wood and Fiber Science and The Forest Products Journal. Researchers will now work on validating the techniques with many different lumber species under varying kiln conditions, Ray explained.
"Penn State encourages inquires from companies that may be interested in becoming research partners for the remaining validation and commercialization work," he added.
Contact Ray at 814-865-0679 or email: email@example.com for additional