Abstract: High rate activated sludge (HRAS) processes have a high potential for carbon and energy recovery from sewage, yet they suffer frequently from poor settleability due to flocculation issues. The process of flocculation is generally optimized using jar tests. However, detailed jar hydrodynamics are often unknown, and average quantities are used, which can significantly differ from the local conditions. The presented work combined experimental and numerical data to investigate the impact of local hydrodynamics on HRAS flocculation for two different jar test configurations (i.e., radial vs. axial impellers at different impeller velocities) and compared the hydrodynamics in these jar tests to those in a representative section of a full scale reactor using computational fluid dynamics (CFD). The analysis showed that the flocculation performance was highly influenced by the impeller type and its speed. The axial impeller appeared to be more appropriate for floc formation over a range of impeller speeds as it produced a more homogeneous distribution of local velocity gradients compared to the radial impeller. In contrast, the radial impeller generated larger volumes (%) of high velocity gradients in which floc breakage may occur. Comparison to local velocity gradients in a full scale system showed that also here, high velocity gradients occurred in the region around the impeller, which might significantly hamper the HRAS flocculation process. As such, this study showed that a model based approach was necessary to translate lab scale results to full scale. These new insights can help improve future experimental setups and reactor design for improved HRAS flocculation.

Abstract: A shift from batch processing towards continuous processing is of interest in the pharmaceutical industry. However, this transition requires detailed knowledge and process understanding of all consecutive unit operations in a continuous manufacturing line to design adequate control strategies. This can be facilitated by developing mechanistic models of the multi-phase systems in the process. Since modelling efforts only started recently in this field, uncertainties about the model predictions are generally neglected. However, model predictions have an inherent uncertainty (i.e. prediction uncertainty) originating from uncertainty in input data, model parameters, model structure, boundary conditions and software. In this paper, the model prediction uncertainty is evaluated for a model describing the continuous drying of single pharmaceutical wet granules in a six-segmented fluidized bed drying unit, which is part of the full continuous from-powder-to-tablet manufacturing line (Consigma (TM), GEA Pharma Systems). A validated model describing the drying behaviour of a single pharmaceutical granule in two consecutive phases is used. First of all, the effect of the assumptions at the particle level on the prediction uncertainty is assessed. Secondly, the paper focuses on the influence of the most sensitive parameters in the model. Finally, a combined analysis (particle level plus most sensitive parameters) is performed and discussed. To propagate the uncertainty originating from the parameter uncertainty to the model output, the Generalized Likelihood Uncertainty Estimation (GLUE) method is used. This method enables a modeller to incorporate the information obtained from the experimental data in the assessment of the uncertain model predictions and to find a balance between model performance and data precision. A detailed evaluation of the obtained uncertainty analysis results is made with respect to the model structure, interactions between parameters and uncertainty boundaries. (C) 2013 Elsevier B.V. All rights reserved.