Two stage abe fermentation with in situ pervaporation and high cell density

Dissertation presented to Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa for obtaining the master degree in Membrane Engineering

Watershed-scale runoff routing and solute transport in a spatially aggregated hydrological framework

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

Structural and functional characterization of ModA and TupA proteins belonging to the molybdate and tungstate transport system from desulfovibrio alaskensis G20

Fundação para a Ciência e Tecnologia - EXPL/BBB-BEP/0274/2012

Numerical prediction of diffusion and electric field-induced iron nanoparticle transport

Zero valent iron nanoparticles (nZVI) are considered very promising for the remediation of contaminated soils and groundwaters. However, an important issue related to their limited mobility remains unsolved. Direct current can be used to enhance the nanoparticles transport, based on the same principles of electrokinetic remediation. In this work, a generalized physicochemical model was developed and solved numerically to describe the nZVI transport through porous media under electric field, and with different electrolytes (with different ionic strengths). The model consists of the Nernst–Planck coupled system of equations, which accounts for the mass balance of ionic species in a fluid medium, when both the diffusion and electromigration of the ions are considered. The diffusion and electrophoretic transport of the negatively charged nZVI particles were also considered in the system. The contribution of electroosmotic flow to the overall mass transport was included in the model for all cases. The nZVI effective mobility values in the porous medium are very low (10−7–10−4 cm2 V−1 s−1), due to the counterbalance between the positive electroosmotic flow and the electrophoretic transport of the negatively charged nanoparticles. The higher the nZVI concentration is in the matrix, the higher the aggregation; therefore, low concentration of nZVI suspensions must be used for successful field application.

Study of Helium II heat transport phenomena in superconducting rutherford type cables

This thesis is a study of how heat is transported in non-steady-state conditions from a superconducting Rutherford cable to a bath of superfluid helium (He II). The same type of superconducting cable is used in the dipole magnets of the Large Hadron Collider (LHC). The dipole magnets of the LHC are immersed in a bath of He II at 1.9 K. At this temperature helium has an extremely high thermal conductivity. During operation, heat needs to be efficiently extracted from the dipole magnets to keep their superconducting state. The thermal stability of the magnets is crucial for the operation of the LHC, therefore it is necessary to understand how heat is transported from the superconducting cables to the He II bath. In He II the heat transfer can be described by the Landau regime or by the Gorter-Mellink regime, depending on the heat flux. In this thesis both measurements and numerical simulation have been performed to study the heat transfer in the two regimes. A temperature increase of 8 2 mK of the superconducting cables was successfully measured experimentally. A new numerical model that covers the two heat transfer regimes has been developed. The numerical model has been validated by comparison with existing experimental data. A comparison is made between the measurements and the numerical results obtained with the developed model.

A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.