Continuous time domain characterization of mixing in agitated pulp stock chests

Characterization of continuous-flow mixing processes, which is extensively employed by chemical process industry is challenging. Agitated pulp chests behave as low-pass filters to reduce high frequency variability in pulp properties ahead of many pulping and papermaking operation. The complex Rheology displayed by the pulp suspension can create considerable deviation from ideal mixing. The non-ideal flows identified were short-circuiting, recirculation and dead volume. Until now, the identification of non-ideal flows has been carried out in a  discrete-time domain with some approximations. In the present study, we characterize the agitated pulp chests in the continuous time domain, which obviates the restrictions imposed by the discrete time approaches. For this purpose, a  robust and efficient hybrid genetic algorithm is utilized along with a  differential-algebraic model of mixing. Both the algorithm and the model are successfully validated using experimental and simulated data. Superior characterizations at a  higher sampling time are obtained compared to those yielded by the discrete-time domain methods. This outcome highlights the benefit of the continuous time domain approach developed in this work.