Sawmilling technology is becoming increasingly complex and, for the past 15 years, optimized systems have replaced human decision-making on the log and lumber processing line. Optimized systems, however, require close monitoring and adequate parameters. Given the increasing shortage of qualified personnel, the use of effective monitoring and control tools is more critical than ever. Monitoring systems are now required to shorten the periods of time during which the primary breakdown process is not controlled.
This research report presents algorithms developed for monitoring the primary breakdown process. The method used to this end is based on available variables for the relevant machine centres. As this method does not require the installation of new sensors, it does not add to the maintenance burden. It should be noted, on the other hand, that certain components of the primary breakdown process cannot be monitored because of a lack of available variables. Such is the case for several mechanical components the monitoring of which requires the installation of specialized sensors.
The algorithms generated by this project use simple mathematical models that can easily be adapted to the majority of softwood lumber sawmills. These models can be integrated into programmable controllers, data acquisition systems, in-house production monitoring software or specialized systems, such as the Smart Mill Assistant system.
The economic potential of the monitoring models can be determined by using optimization system variables and offline monitoring models. The graphical representation of results generated by the models reveals the number of times the primary breakdown process deviated from process parameters, as well as the duration of such deviations. Their economic impact can be calculated on the basis of this information. The economic benefits of real-time monitoring and control systems depend on initial sawmill performance, the skill of sawmill personnel in quickly detecting and identifying process deviations and the time required to solve related problems.