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.

Abstract: Biodegradation of trichloroethene (TCE) near a Dense Non Aqueous Phase Liquid (DNAPL) can enhance the dissolution rate of the DNAPL by increasing the concentration gradient at the DNAPL-water interface. Two-dimensional flow-through sand boxes containing a ICE DNAPL and inoculated with a TCE dechlorinating consortium were set up to measure this bio-enhanced dissolution under anaerobic conditions. The total mass of TCE and daughter products in the effluent of the biotic boxes was 3-6 fold larger than in the effluent of the abiotic box. However, the mass of daughter products only accounted for 19-55% of the total mass of chlorinated compounds in the effluent, suggesting that bio-enhanced dissolution factors were maximally 1.3-2.2. The enhanced dissolution most likely primarily resulted from variable DNAPL distribution rather than biodegradation. Specific dechlorination rates previously determined in a stirred liquid medium were used in a reactive transport model to identify the rate limiting factors. The model adequately simulated the overall TCE degradation when predicted resident microbial numbers approached observed values and indicated an enhancement factor for TCE dissolution of 1.01. The model shows that dechlorination of TCE in the 20 box was limited due to the short residence time and the self-inhibition of the TCE degradation. A parameter sensitivity analysis predicts that the bio-enhanced dissolution factor for this TCE source zone can only exceed a value of 2 if the TCE self-inhibition is drastically reduced (when a TCE tolerant dehalogenating community is present) or if the DNAPL is located in a low-permeable layer with a small Darcy velocity. (C) 2012 Elsevier Ltd. All rights reserved.

Abstract: Knowledge on groundwater-surface water interaction and especially on exchange fluxes between streams and aquifers is an important prerequisite for the study of transport and fate of contaminants and nutrients in the hyporheic zone. One possibility to quantify groundwater-surface water exchange fluxes is by using heat as an environmlental tracer. Modern field equipment including multilevel temperature sticks and the novel open-source analysis tool LPML make this technique ever more attractive. The recently developed LPML method solves the one-dimensional fluid flow and heat transport equation by combining a local polynomial method with a maximum likelihood estimator. In this study, we apply the LPML method on field data to quantify the spatial and temporal variability of vertical fluxes and their uncertainties from temperature-time series measured in a Belgian lowland stream. Over several months, temperature data were collected with multilevel temperature sticks at the streambed top and at six depths for a small stream section. Long-term estimates show a range from gaining fluxes of -291 mm day(-1) to loosing fluxes of 12 mm day(-1); average seasonal fluxes ranged from -138 mm day(-1) in winter to -16 mm day(-1) in summer. With our analyses, we could determine a high spatial and temporal variability of vertical exchange fluxes for the investigated stream section. Such spatial and temporal variability should be taken into account in biogeochemical cycling of carbon, nutrients and metals and in fate analysis of contaminant plumes. In general, the stream section was gaining during most of the observation period. Two short-term high stream stage events, seemingly caused by blockage of the stream outlet, led to a change in flow direction from gaining to losing conditions. We also found more discharge occurring at the outer stream bank than at the inner one indicating a local flow-through system. With the conducted analyses, we were able to advance our understanding of the regional groundwater flow system. Copyright (C) 2015 John Wiley & Sons, Ltd.

Abstract: The use of temperature-time series measured in streambed sediments as input to coupled water flow and heat transport models has become standard when quantifying vertical groundwater-surface water exchange fluxes. We develop a novel methodology, called LPML, to estimate the parameters for 1-D water flow and heat transport by combining a local polynomial (LP) signal processing technique with a maximum likelihood (ML) estimator. The LP method is used to estimate the frequency response functions (FRFs) and their uncertainties between the streambed top and several locations within the streambed from measured temperature-time series data. Additionally, we obtain the analytical expression of the FRFs assuming a pure sinusoidal input. The estimated and analytical FRFs are used in an ML estimator to deduce vertical groundwater-surface water exchange flux and its uncertainty as well as information regarding model quality. The LPML method is tested and verified with the heat transport models STRIVE and VFLUX. We demonstrate that the LPML method can correctly reproduce a priori known fluxes and thermal conductivities and also show that the LPML method can estimate averaged and time-variable fluxes from periodic and nonperiodic temperature records. The LPML method allows for a fast computation of exchange fluxes as well as model and parameter uncertainties from many temperature sensors. Moreover, it can utilize a broad frequency spectrum beyond the diel signal commonly used for flux calculations.