Real-time dynamic simulation of continuous bulk material flow to improve the statistical modelling of final product strength properties

In regression analyses, correlations between independent variables (e.g. process variables) and dependent variables (e.g. product quality) are of major interest. However, only statistically significant correlations ensure a reliable interpretation of how process variables affect product qualities. In this respect, accurate time alignment of independent variables is crucial to obtain regression models with acceptable validation that are influenced by temporal phenomena (e.g. industrial processes) only. 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 was achieved by modelling the continuous bulk material flow. The two different methods of calculation were then applied on an industrial production of particleboards to predict final board strength properties. Results of regression models showed that the use of dynamically calculated time lags improved the predictability of the internal bond strength of boards by 67% compared to statically calculated time lags. Consequently, final product strength properties could be predicted more accurately, which should lead to lower costs of rejects and a higher efficiency of material inputs.