Dynamic simulation of the continuous flow of bulk material during production to improve the statistical modeling of final product strength properties
Reigler M., N. O. Andre, M. Gronalt, and T. M. Young.  2015.  International Journal of Production Research, 53(21):66296636.

In regression analyses, the correlations between independent variables (e.g., process variables) and dependent variables (e.g., product quality) are of major interest. This information from the regression models developed from significant correlations and the corresponding coefficients or weights is used to predict outcomes of dependent variables. To obtain regression models with acceptable validation that are influenced by temporal phenomena (e.g., industrial processes), accurate alignment in time of independent variables is crucial. In this study, the commonly used static form of time alignment, where only the distances between consecutive process parameters and the average production speed are considered, is compared to a newly developed dynamic calculation of time lags. The dynamic calculation of time lags is achieved by modeling the continuous material flow of wood particles in the industrial production of particleboards. Results show that the use of dynamically calculated time lags improves the predictability of regression models by 36 % compared to statically calculated time lags. Consequently, product qualities can be predicted more accurately, which should lead to lower costs of rejects and a higher efficiency of material inputs.