7 resultados para Thermodynamic Cycles

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


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The increase in environmental and healthy concerns, combined with the possibility to exploit waste as a valuable energy resource, has led to explore alternative methods for waste final disposal. In this context, the energy conversion of Municipal Solid Waste (MSW) in Waste-To-Energy (WTE) power plant is increasing throughout Europe, both in terms of plants number and capacity, furthered by legislative directives. Due to the heterogeneous nature of waste, some differences with respect to a conventional fossil fuel power plant have to be considered in the energy conversion process. In fact, as a consequence of the well-known corrosion problems, the thermodynamic efficiency of WTE power plants typically ranging in the interval 25% ÷ 30%. The new Waste Framework Directive 2008/98/EC promotes production of energy from waste introducing an energy efficiency criteria (the so-called “R1 formula”) to evaluate plant recovery status. The aim of the Directive is to drive WTE facilities to maximize energy recovery and utilization of waste heat, in order to substitute energy produced with conventional fossil fuels fired power plants. This calls for novel approaches and possibilities to maximize the conversion of MSW into energy. In particular, the idea of an integrated configuration made up of a WTE and a Gas Turbine (GT) originates, driven by the desire to eliminate or, at least, mitigate limitations affecting the WTE conversion process bounding the thermodynamic efficiency of the cycle. The aim of this Ph.D thesis is to investigate, from a thermodynamic point of view, the integrated WTE-GT system sharing the steam cycle, sharing the flue gas paths or combining both ways. The carried out analysis investigates and defines the logic governing plants match in terms of steam production and steam turbine power output as function of the thermal powers introduced.

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Photosynthetic organisms have sought out the delicate balance between efficient light harvesting under limited irradiance and regulated energy dissipation under excess irradiance. One of the protective mechanisms is the thermal energy dissipation through the xanthophyll cycle that may transform harmlessly the excitation energy into heat and thereby prevent the formation of damaging active oxygen species (AOS). Violaxanthin deepoxidase (VDE) converts violaxanthin (V) to antheraxanthin (A) and zeaxanthin (Z) defending the photosynthetic apparatus from excess of light. Another important biological pathway is the chloroplast water-water cycle, which is referred to the electrons from water generated in PSII reducing atmospheric O2 to water in PSI. This mechanism is active in the scavenging of AOS, when electron transport is slowed down by the over-reduction of NADPH pool. The control of the VDE gene and the variations of a set of physiological parameters, such as chlorophyll florescence and AOS content, have been investigated in response to excess of light and drought condition using Arabidopsis thaliana and Arbutus unedo.. Pigment analysis showed an unambiguous relationship between xanthophyll de-epoxidation state ((A+Z)/(V+A+Z)) and VDE mRNA amount in not-irrigated plants. Unexpectedly, gene expression is higher during the night when xanthophylls are mostly epoxidated and VDE activity is supposed to be very low than during the day. The importance of the water-water cycle in protecting the chloroplasts from light stress has been examined through Arabidopsis plant with a suppressed expression of the key enzyme of the cycle: the thylakoid-attached copper/zinc superoxide dismutase. The analysis revealed changes in transcript expression during leaf development consistent with a signalling role of AOS in plant defence responses but no difference was found any in photosynthesis efficiency or in AOS concentration after short-term exposure to excess of light. Environmental stresses such as drought may render previously optimal light levels excessive. In these circumstances the intrinsic regulations of photosynthetic electron transport like xanthophyll and water-water cycles might modify metabolism and gene expression in order to deal with increasing AOS.

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The present PhD thesis summarizes the three-years study about the neutronic investigation of a new concept nuclear reactor aiming at the optimization and the sustainable management of nuclear fuel in a possible European scenario. A new generation nuclear reactor for the nuclear reinassance is indeed desired by the actual industrialized world, both for the solution of the energetic question arising from the continuously growing energy demand together with the corresponding reduction of oil availability, and the environment question for a sustainable energy source free from Long Lived Radioisotopes and therefore geological repositories. Among the Generation IV candidate typologies, the Lead Fast Reactor concept has been pursued, being the one top rated in sustainability. The European Lead-cooled SYstem (ELSY) has been at first investigated. The neutronic analysis of the ELSY core has been performed via deterministic analysis by means of the ERANOS code, in order to retrieve a stable configuration for the overall design of the reactor. Further analyses have been carried out by means of the Monte Carlo general purpose transport code MCNP, in order to check the former one and to define an exact model of the system. An innovative system of absorbers has been conceptualized and designed for both the reactivity compensation and regulation of the core due to cycle swing, as well as for safety in order to guarantee the cold shutdown of the system in case of accident. Aiming at the sustainability of nuclear energy, the steady-state nuclear equilibrium has been investigated and generalized into the definition of the ``extended'' equilibrium state. According to this, the Adiabatic Reactor Theory has been developed, together with a New Paradigm for Nuclear Power: in order to design a reactor that does not exchange with the environment anything valuable (thus the term ``adiabatic''), in the sense of both Plutonium and Minor Actinides, it is required indeed to revert the logical design scheme of nuclear cores, starting from the definition of the equilibrium composition of the fuel and submitting to the latter the whole core design. The New Paradigm has been applied then to the core design of an Adiabatic Lead Fast Reactor complying with the ELSY overall system layout. A complete core characterization has been done in order to asses criticality and power flattening; a preliminary evaluation of the main safety parameters has been also done to verify the viability of the system. Burn up calculations have been then performed in order to investigate the operating cycle for the Adiabatic Lead Fast Reactor; the fuel performances have been therefore extracted and inserted in a more general analysis for an European scenario. The present nuclear reactors fleet has been modeled and its evolution simulated by means of the COSI code in order to investigate the materials fluxes to be managed in the European region. Different plausible scenarios have been identified to forecast the evolution of the European nuclear energy production, including the one involving the introduction of Adiabatic Lead Fast Reactors, and compared to better analyze the advantages introduced by the adoption of new concept reactors. At last, since both ELSY and the ALFR represent new concept systems based upon innovative solutions, the neutronic design of a demonstrator reactor has been carried out: such a system is intended to prove the viability of technology to be implemented in the First-of-a-Kind industrial power plant, with the aim at attesting the general strategy to use, to the largest extent. It was chosen then to base the DEMO design upon a compromise between demonstration of developed technology and testing of emerging technology in order to significantly subserve the purpose of reducing uncertainties about construction and licensing, both validating ELSY/ALFR main features and performances, and to qualify numerical codes and tools.

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During the last few years, a great deal of interest has risen concerning the applications of stochastic methods to several biochemical and biological phenomena. Phenomena like gene expression, cellular memory, bet-hedging strategy in bacterial growth and many others, cannot be described by continuous stochastic models due to their intrinsic discreteness and randomness. In this thesis I have used the Chemical Master Equation (CME) technique to modelize some feedback cycles and analyzing their properties, including experimental data. In the first part of this work, the effect of stochastic stability is discussed on a toy model of the genetic switch that triggers the cellular division, which malfunctioning is known to be one of the hallmarks of cancer. The second system I have worked on is the so-called futile cycle, a closed cycle of two enzymatic reactions that adds and removes a chemical compound, called phosphate group, to a specific substrate. I have thus investigated how adding noise to the enzyme (that is usually in the order of few hundred molecules) modifies the probability of observing a specific number of phosphorylated substrate molecules, and confirmed theoretical predictions with numerical simulations. In the third part the results of the study of a chain of multiple phosphorylation-dephosphorylation cycles will be presented. We will discuss an approximation method for the exact solution in the bidimensional case and the relationship that this method has with the thermodynamic properties of the system, which is an open system far from equilibrium.In the last section the agreement between the theoretical prediction of the total protein quantity in a mouse cells population and the observed quantity will be shown, measured via fluorescence microscopy.

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This work presents the results of theoretical and experimental characterization of thermodynamic, mechanical and transport properties in polymer solvent systems. The polymer solvent pairs considered ranged to those in which the polymer is rubbery, to those in which the initially glassy polymeric matrix is plasticized by the action of the low molecular weight species. Advanced Equation of State models have been adopted for thermodynamic modeling,along with a rigorous procedure that enables to extend their applicability to the non equilibrium, glassy region. Mass sorption kinetics had been modeled with phenomenological models and with advanced kinetic models.

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It is well known that many realistic mathematical models of biological systems, such as cell growth, cellular development and differentiation, gene expression, gene regulatory networks, enzyme cascades, synaptic plasticity, aging and population growth need to include stochasticity. These systems are not isolated, but rather subject to intrinsic and extrinsic fluctuations, which leads to a quasi equilibrium state (homeostasis). The natural framework is provided by Markov processes and the Master equation (ME) describes the temporal evolution of the probability of each state, specified by the number of units of each species. The ME is a relevant tool for modeling realistic biological systems and allow also to explore the behavior of open systems. These systems may exhibit not only the classical thermodynamic equilibrium states but also the nonequilibrium steady states (NESS). This thesis deals with biological problems that can be treat with the Master equation and also with its thermodynamic consequences. It is organized into six chapters with four new scientific works, which are grouped in two parts: (1) Biological applications of the Master equation: deals with the stochastic properties of a toggle switch, involving a protein compound and a miRNA cluster, known to control the eukaryotic cell cycle and possibly involved in oncogenesis and with the propose of a one parameter family of master equations for the evolution of a population having the logistic equation as mean field limit. (2) Nonequilibrium thermodynamics in terms of the Master equation: where we study the dynamical role of chemical fluxes that characterize the NESS of a chemical network and we propose a one parameter parametrization of BCM learning, that was originally proposed to describe plasticity processes, to study the differences between systems in DB and NESS.

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Forest models are tools for explaining and predicting the dynamics of forest ecosystems. They simulate forest behavior by integrating information on the underlying processes in trees, soil and atmosphere. Bayesian calibration is the application of probability theory to parameter estimation. It is a method, applicable to all models, that quantifies output uncertainty and identifies key parameters and variables. This study aims at testing the Bayesian procedure for calibration to different types of forest models, to evaluate their performances and the uncertainties associated with them. In particular,we aimed at 1) applying a Bayesian framework to calibrate forest models and test their performances in different biomes and different environmental conditions, 2) identifying and solve structure-related issues in simple models, and 3) identifying the advantages of additional information made available when calibrating forest models with a Bayesian approach. We applied the Bayesian framework to calibrate the Prelued model on eight Italian eddy-covariance sites in Chapter 2. The ability of Prelued to reproduce the estimated Gross Primary Productivity (GPP) was tested over contrasting natural vegetation types that represented a wide range of climatic and environmental conditions. The issues related to Prelued's multiplicative structure were the main topic of Chapter 3: several different MCMC-based procedures were applied within a Bayesian framework to calibrate the model, and their performances were compared. A more complex model was applied in Chapter 4, focusing on the application of the physiology-based model HYDRALL to the forest ecosystem of Lavarone (IT) to evaluate the importance of additional information in the calibration procedure and their impact on model performances, model uncertainties, and parameter estimation. Overall, the Bayesian technique proved to be an excellent and versatile tool to successfully calibrate forest models of different structure and complexity, on different kind and number of variables and with a different number of parameters involved.