Scale-up of precipitation processes
This thesis concerns the scale-up of precipitation processes aimed at predicting product particle characteristics. Although precipitation is widely used in the chemical and pharmaceutical industry, successful scale-up is difficult due to the absence of a validated methodology. It is found that none of the conventional scale-up criteria reported in the literature (equal power input per unit mass, equal tip speed, equal stirring rate) is capable of predicting the experimentally observed effects of the mixing conditions on kinetic rates and particle characteristics. As a result of high gradients in the supersaturation field during precipitation, particularly in the feed zone, high local gradients in the nucleation rate are to be expected. In this thesis, a compartmental mixing model (Segregated Feed Model SFM) linked to the population balance is proposed for scaling up both continuous and semibatch precipitation processes, and is validated with experiments on different scales. Experiments were carried out using two chemical systems (calcium oxalate CaC₂O₄ and calcium carbonate CaCO₃), varying the residence time/feed time, feed concentration, feed point position, impeller type, feed tube diameter and stirring rate in geometrically similar reactors ranging from 0.3 to 301. A new procedure is introduced in order to solve the inverse problem for determination of the kinetic parameters for nucleation, growth, disruption and agglomeration from the particle size distributions obtained in the continuous laboratory-scale experiments. This method, where the kinetic rates were extracted separately and sequentially from the particle size distribution, was found to be a reliable alternative to the conventional simultaneous estimation of all kinetic parameters from the distribution. Using the kinetic parameters extracted from the laboratory-scale experiments, the population balance is solved within the Segregated Feed Model. The local mixing parameters also required for solving the SFM are obtained from a sliding mesh Computational Fluid Dynamics (CFD) model. These are used to specify the different micromixing and mesomixing conditions in the feed and bulk zones of the reactor. The model accurately predicts the mean size, coefficient of variation and nucleation rate on different scales for different process and mixing conditions in both continuous and semibatch mode of operation. Furthermore, the model confirms the observed greater effect of mixing on product particle characteristics in semibatch than in continuous operation. This is thought to be due to direct mixing of the feed solution in semibatch operation with the other component already present in the reactor. The methodology proposed here for the scale-up of precipitation processes is very versatile and computationally efficient. It combines the advantages of both a CFD and a population balance approach without having to solve the equations together, which is currently still impracticable due to the excessive computational demand and simulation time required.