(193d) Population Balance of Knife Milled Corn Stover

In this work we develop a predictive population balance model for knife milling of corn stover stalks, based on a probability and breakage function to predict the milled particle size distribution as a function of variation in mill tip speed, moisture content, initial particle size, and retention screen size. These probability and breakage parameters are dependent on material properties like the fracture resistance and minimum specific energy. Batch scale experiments were used to determine material and mill parameters within the breakage law. A breakage matrix was developed using fracture resistance and minimum specific energy, which were found from calibration tests with iterative regression to measured product size distributions as well as inputs for feed size, screen classification efficiency, amount of recirculated material and the number of impacts that a particle experiences. In the knife mill experiments, the initial particle size was obtained from analytical splits of the sample along with separations with an orbital screen. Screen opening was altered by interchangeable screen sizes while the mill speed was controlled with a VFD. These variables were associated with the impact energy, number of impacts and the size of the particles under impact. This work reports on the imparted control over resulting particle size distribution as a function of controllable operational parameters (e.g. tip speed, feed rate, initial material size and retention screen size). In this work, results for both a ‘once-through’ configuration in addition to a recirculating fractional milling technique was evaluated for degree of control over the comminution process, as well as potential energy savings and optimal configuration/recirculating rate. The model provides an estimate for daughter particle size distribution for a range of operational conditions which is then be validated by experimental results. Determining the highest throughput to achieve a specific particle size distribution will allow optimization on energy costs and milling configuration.