“Design and construction of an experimental setup to enhance mineral weathering through the activity of soil organisms”. Calogiuri T, Hagens M, Van Groenigen JW, Corbett T, Hartmann J, Hendriksen R, Janssens I, Janssens IA, Ledesma Dominguez G, Loescher G, Mortier S, Neubeck A, Niron H, Poetra RP, Rieder L, Struyf E, Van Tendeloo M, De Schepper T, Verdonck T, Vlaeminck SE, Vicca S, Vidal A, Journal of visualized experiments , e65563 (2023). http://doi.org/10.3791/65563
Abstract: Enhanced weathering (EW) is an emerging carbon dioxide (CO2) removal technology that can contribute to climate change mitigation. This technology relies on accelerating the natural process of mineral weathering in soils by manipulating the abiotic variables that govern this process, in particular mineral grain size and exposure to acids dissolved in water. EW mainly aims at reducing atmospheric CO2 concentrations by enhancing inorganic carbon sequestration. Until now, knowledge of EW has been mainly gained through experiments that focused on the abiotic variables known for stimulating mineral weathering, thereby neglecting the potential influence of biotic components. While bacteria, fungi, and earthworms are known to increase mineral weathering rates, the use of soil organisms in the context of EW remains underexplored. This protocol describes the design and construction of an experimental setup developed to enhance mineral weathering rates through soil organisms while concurrently controlling abiotic conditions. The setup is designed to maximize weathering rates while maintaining soil organisms' activity. It consists of a large number of columns filled with rock powder and organic material, located in a climate chamber and with water applied via a downflow irrigation system. Columns are placed above a fridge containing jerrycans to collect the leachate. Representative results demonstrate that this setup is suitable to ensure the activity of soil organisms and quantify their effect on inorganic carbon sequestration. Challenges remain in minimizing leachate losses, ensuring homogeneous ventilation through the climate chamber, and avoiding flooding of the columns. With this setup, an innovative and promising approach is proposed to enhance mineral weathering rates through the activity of soil biota and disentangle the effect of biotic and abiotic factors as drivers of EW.
Keywords: A1 Journal article; Engineering sciences. Technology; Internet Data Lab (IDLab); Applied mathematics; Sustainable Energy, Air and Water Technology (DuEL); Plant and Ecosystems (PLECO) – Ecology in a time of change
Impact Factor: 1.2
DOI: 10.3791/65563
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“A GLUE uncertainty analysis of a drying model of pharmaceutical granules”. Mortier STFC, Van Hoey S, Cierkens K, Gernaey KV, Seuntjens P, De Baets B, De Beer T, Nopens I, European journal of pharmaceutics and biopharmaceutics 85, 984 (2013). http://doi.org/10.1016/J.EJPB.2013.03.012
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.
Keywords: A1 Journal article; Pharmacology. Therapy; Sustainable Energy, Air and Water Technology (DuEL)
DOI: 10.1016/J.EJPB.2013.03.012
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