3 resultados para Fitting parameters

em AMS Tesi di Dottorato - Alm@DL - Università di Bologna


Relevância:

60.00% 60.00%

Publicador:

Resumo:

Chromatography represents one of the most important and widely used unit operation in the biotechnology industry. However this technique suffers from several limitations such as high pressure drop, slow mass transfer through the diffusive pores and strong dependence of the binding capacity on flow rate. In this work, affinity membranes with improved capacity have been considered as an alternative technology for the capturing step in antibody manufacturing. Several affinity membranes have been prepared starting from various membrane supports. Different affinity ligands have been utilized like Protein A, the natural ligand of choice for antibodies, as well as synthetic ligands that exhibit affinity for the Fc portion of antibodies. The membranes have been characterized in detail: binding and elution performance was evaluated in adsorption experiments using pure IgG solutions, while membrane selectivity was evaluated using complex solutions like a cell culture supernatant. The most promising affinity membranes were extensively tested in dynamic experiments. The effects of operating parameters like feed concentration and flow rate on separation performances like binding capacity, selectivity and process yield have been studied in detail in order to find the optimal conditions for binding and elution steps. The membranes have been used over several complete chromatographic cycles to evaluate the effects of ageing and of membrane regeneration on dynamic binding capacity. A novel mathematical model is proposed that can describe all the chromatographic steps involved in the membrane affinity chromatography process for protein purification. The mathematical description is based on the species continuity equation coupled with a proper binding kinetic equation, and suitable to describe adequately the dispersion phenomena occurring both in the micro-porous membranes as well as in the extra-column devices used in the system. The model considers specifically all the different chromatographic steps, namely adsorption, washing and elution. The few relevant fitting parameters of the model were derived from a calibration with the experimental affinity cycles performed with pure IgG solutions, then the model is used to describe experimental data obtained in chromatographic cycles carried out with complex feeds as the cell culture supernatant. Simulations reveal a good agreement with experimental data in all the chromatography steps, both in the case of pure IgG solutions and for the cell culture supernatant considered.

Relevância:

60.00% 60.00%

Publicador:

Resumo:

This thesis tackles the problem of the automated detection of the atmospheric boundary layer (BL) height, h, from aerosol lidar/ceilometer observations. A new method, the Bayesian Selective Method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in an statistically optimal way different sources of information. Firstly atmospheric stratification boundaries are located from discontinuities in the ceilometer back-scattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneus physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice. The BSM algorithm has been tested for four months of continuous ceilometer measurements collected during the BASE:ALFA project and is shown to realistically diagnose the BL depth evolution in many different weather conditions. Then the BASE:ALFA dataset is used to investigate the boundary layer structure in stable conditions. Functions from the Obukhov similarity theory are used as regression curves to fit observed velocity and temperature profiles in the lower half of the stable boundary layer. Surface fluxes of heat and momentum are best-fitting parameters in this exercise and are compared with what measured by a sonic anemometer. The comparison shows remarkable discrepancies, more evident in cases for which the bulk Richardson number turns out to be quite large. This analysis supports earlier results, that surface turbulent fluxes are not the appropriate scaling parameters for profiles of mean quantities in very stable conditions. One of the practical consequences is that boundary layer height diagnostic formulations which mainly rely on surface fluxes are in disagreement to what obtained by inspecting co-located radiosounding profiles.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

BTES (borehole thermal energy storage)systems exchange thermal energy by conduction with the surrounding ground through borehole materials. The spatial variability of the geological properties and the space-time variability of hydrogeological conditions affect the real power rate of heat exchangers and, consequently, the amount of energy extracted from / injected into the ground. For this reason, it is not an easy task to identify the underground thermal properties to use when designing. At the current state of technology, Thermal Response Test (TRT) is the in situ test for the characterization of ground thermal properties with the higher degree of accuracy, but it doesn’t fully solve the problem of characterizing the thermal properties of a shallow geothermal reservoir, simply because it characterizes only the neighborhood of the heat exchanger at hand and only for the test duration. Different analytical and numerical models exist for the characterization of shallow geothermal reservoir, but they are still inadequate and not exhaustive: more sophisticated models must be taken into account and a geostatistical approach is needed to tackle natural variability and estimates uncertainty. The approach adopted for reservoir characterization is the “inverse problem”, typical of oil&gas field analysis. Similarly, we create different realizations of thermal properties by direct sequential simulation and we find the best one fitting real production data (fluid temperature along time). The software used to develop heat production simulation is FEFLOW 5.4 (Finite Element subsurface FLOW system). A geostatistical reservoir model has been set up based on literature thermal properties data and spatial variability hypotheses, and a real TRT has been tested. Then we analyzed and used as well two other codes (SA-Geotherm and FV-Geotherm) which are two implementation of the same numerical model of FEFLOW (Al-Khoury model).