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: When applying hydrological models, different sources of uncertainty are present, and evaluations of model performances should take these into account to assess model outcomes correctly. Furthermore, uncertainty in the discharge observations complicates the model identification, both in terms of model structure and parameterization. In this paper, the authors compare two different lumped model structures (PDM and NAM) considering uncertainty coming from the rating curve. Limits of acceptability for the model simulations were determined based on derived uncertainty bounds of the discharge observations. The authors applied the DYNamic Identifiability Approach (DYNIA) to identify structural failure of both models and to evaluate the configuration of their structures. In general, similar model performances are observed. However, the model structures tend to behave differently in the course of time, as revealed by the DYNIA approach. Based on the analyses performed, the probability based soil storage representation of the PDM model outperforms the NAM structure. The incorporation of the observation error did not prevent the DYNIA analysis to identify potential model structural deficiencies that are limiting the representation of the seasonal variation, primarily indicated by shifting regions of parameter identifiability. As such, the proposed approach is able to indicate where deficiencies are found and model improvement is needed.

Abstract: The effects on river water quality of sewer overflows are not well known. Since the duration of the overflow is in the order of magnitude of minutes to hours, continuous measurements of water quality are needed and traditional grab sampling is unable to quantify the pollution loads. The objective of this paper was to demonstrate the applicability of high frequency measurements for assessing the impacts of waste water treatment plants on the water quality of the receiving surface water. In our in situ water quality monitoring setup, two types of multiparameter sensors mounted on a floating fixed platform were used to determine the dynamics of dissolved oxygen, specific conductivity, ammonium-N, nitrate-N and dissolved organic carbon downstream of a waste water treatment plant (WWTP), in combination with data on rainfall, river discharge and WWTP overflow discharge. The monitoring data for water quantity and water quality were used to estimate the pollution load from waste water overflow events and to assess the impact of waste water overflows on the river water quality. The effect of sewer overflow on a small river in terms of N load was shown to be significant. The WWTP overflow events accounted for about 1/3 of the river discharge. The NH4-N loads during overflow events contributed 29% and 21% to the August 2010 and June 2011 load, respectively, in only 8% and 3% of the monthly time span. The results indicate that continuous monitoring is needed to accurately represent the effects of sewer overflows in river systems.

Abstract: We introduce LPMLE3, a new 1-D approach to quantify vertical water flow components at streambeds using temperature data collected in different depths. LPMLE3 solves the partial differential equation for coupled water flow and heat transport in the frequency domain. Unlike other 1-D approaches it does not assume a semi-infinite halfspace with the location of the lower boundary condition approaching infinity. Instead, it uses local upper and lower boundary conditions. As such, the streambed can be divided into finite subdomains bound at the top and bottom by a temperature-time series. Information from a third temperature sensor within each subdomain is then used for parameter estimation. LPMLE3 applies a low order local polynomial to separate periodic and transient parts (including the noise contributions) of a temperature-time series and calculates the frequency response of each subdomain to a known temperature input at the streambed top. A maximum-likelihood estimator is used to estimate the vertical component of water flow, thermal diffusivity, and their uncertainties for each streambed subdomain and provides information regarding model quality. We tested the method on synthetic temperature data generated with the numerical model STRIVE and demonstrate how the vertical flow component can be quantified for field data collected in a Belgian stream. We show that by using the results in additional analyses, nonvertical flow components could be identified and by making certain assumptions they could be quantified for each subdomain. LPMLE3 performed well on both simulated and field data and can be considered a valuable addition to the existing 1-D methods.