An experimental two-roll mill was developed and instrumented for computeriz
ed data acquisition. Milling tests were performed on three classes of wheat
. Included in the study were six independent variables each with three leve
ls, namely, class of wheat, moisture content, feed rate, fast roll speed, r
oll speed differential, and roll gap. Two covariates, single kernel hardnes
s and single kernel weight, were also included in the statistical analysis.
Prediction models were constructed for five dependent variables (fast roll
power, slow roll power, net power, energy per unit mass and specific energ
y). The prediction models fitted the experimental data well (r(2) = 0.88 si
milar to 0.95). The power and energy requirements for size reduction of whe
at were highly correlated with the single kernel characteristics of wheat.
Feed rate affected fast roll power; slow roll power and net power significa
ntly. Roll gap had a significant effect on roller mill grinding. Additional
milling tests were conducted by randomly selecting independent variables a
nd covariates to verify the robustness and validity of the prediction model
s.