21 resultados para iron plants


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Dissertation presented to obtain the Ph.D degree in Biochemistry

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Thesis submitted to obtain the Doctoral degree in Energy and Bioenergy

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Fundação para a Ciência e a Tecnologia - PTDC/AGR-­AAM/101643/2008 NanoDC ; SFRH/BD/76070/2011 ; FP7-­PEOPLE-­IRSES-­2010-­269289-­ ELECTROACROSS

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Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.

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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.

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This project aimed to engineer new T2 MRI contrast agents for cell labeling based on formulations containing monodisperse iron oxide magnetic nanoparticles (MNP) coated with natural and synthetic polymers. Monodisperse MNP capped with hydrophobic ligands were synthesized by a thermal decomposition method, and further stabilized in aqueous media with citric acid or meso-2,3-dimercaptosuccinic acid (DMSA) through a ligand exchange reaction. Hydrophilic MNP-DMSA, with optimal hydrodynamic size distribution, colloidal stability and magnetic properties, were used for further functionalization with different coating materials. A covalent coupling strategy was devised to bind the biopolymer gum Arabic (GA) onto MNPDMSA and produce an efficient contrast agent, which enhanced cellular uptake in human colorectal carcinoma cells (HCT116 cell line) compared to uncoated MNP-DMSA. A similar protocol was employed to coat MNP-DMSA with a novel biopolymer produced by a biotechnological process, the exopolysaccharide (EPS) Fucopol. Similar to MNP-DMSA-GA, MNP-DMSA-EPS improved cellular uptake in HCT116 cells compared to MNP-DMSA. However, MNP-DMSA-EPS were particularly efficient towards the neural stem/progenitor cell line ReNcell VM, for which a better iron dose-dependent MRI contrast enhancement was obtained at low iron concentrations and short incubation times. A combination of synthetic and biological coating materials was also explored in this project, to design a dynamic tumortargeting nanoprobe activated by the acidic pH of tumors. The pH-dependent affinity pair neutravidin/iminobiotin, was combined in a multilayer architecture with the synthetic polymers poy-L-lysine and poly(ethylene glycol) and yielded an efficient MRI nanoprobe with ability to distinguish cells cultured in acidic pH conditions form cells cultured in physiological pH conditions.