Data-driven modeling of milling and sieving operations in a wheat milling process

Industrial wheat milling processes are based on a gradual size reduction approach, with repeated milling and sieving steps to achieve appropriate breakage of the wheat kernel and its effective reduction into flour. This approach greatly increases the flour yield, but makes the process more difficult to understand and operate. In this respect, mathematical modeling can be very useful to optimize industrial milling operations by relating the operating variables to the final product quality and quantity. As a first step toward the modeling of an entire wheat milling process, in this study experimental data were generated from roller mills and plansifters, and the data were used to build multivariate statistical models enabling one to improve process understanding and to predict the particle size distribution of the milled material and of the sieved material, as well as the amount of material remaining on each sieve, from the known operating conditions and wheat moisture. The unit operation models were then concatenated, and the propagation of prediction errors was analyzed. The results are very satisfactory, in that a comprehension of the breakage mechanism was obtained with a single modeling framework that unifies the modeling results obtained in existing studies, and the product quality and amount were predicted with good accuracy. The results on prediction error propagation show that, although the models are data-driven, the design of the plansifter model can be done independently of that of the roller mill model, since the prediction error did not propagate across the models strongly.