1. Introduction - Firenze University Press

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Electricity distribution (EE) D Ep Es P S P Natural gas GT + Heat recovery Gas engine + Heat recovery Steam, 180°C 331 Steam boiler Steam/Hot Water HX Hot Water Boiler Hot Water, 90°C Hot Water/ Cooling Water HX Cooling water, ambient + 5°C 2x effect 1x effect EE EE EE Abs Abs Mechanical EE Chiller Chiller Chiller Chilled water, 5°C Cooling tower Figure 2 Superstructure of energy supply system for a hospital. D Heating Ambient Air D W Coolth 4. Optimal Mine Site Energy Supply – OMSES initial formulation For energy services superstructures such as those presented in Figures 1 and 2, following precedent practices [32] [34]], the decision variables for an optimization problem formulated to determine the optimal mine site energy supply may be characterized as follows: i) binary variables (denoting whether a technology is/is not installed); ii) integer variables (denoting the number of installed units of a technology); iii) continuous variables (denoting the energy flows between utilities). The latter include connections to distribution systems across the site boundary (to the utility company’s electrical and natural gas distribution systems – where appropriate) and energy flows between distributions and site loads. The constraints that may be taken to apply to the optimization process are manifold: <strong>1.</strong> Energy conversion technology constraints. These are constraints that reflect an energy conversion mass and energy balance across each technology of a specific type. A graphical example representation of such a constraint is presented in Figure 3 which shows the energy (and inferred mass) balance across a gas turbine equipped with a heat recovery unit. 2. Technology installation limit constraints. These constraints apply a threshold on the maximum number of units of a given technology type that may be installed. Such a constraint is useful to reflect practical considerations such as the amount of land footprint available to accommodate technology of a specific type. This also articulates a capacity constraint for the specific technologies. 3. Utility balancing off constraints. These are constraints that ensure that the net sums of energy flows from each of the indicated distributions are zero, which also ensure that energy supply (in all its forms) meets on-site demand. In the event that hourly on-site demand data
are available for an entire year, 8760 such constraints feature in the problem formulation, for a specific utility. In practice, various heuristics may be deployed to reduce the number of these constraints (e.g., considering only 12, 24 hour periods, each representative of a typical day in a typical month – see Figure 4). 4. Carbon dioxide equivalent emissions constraints. These are constraints that express the idea that the total emissions associated with the production of energy from the energy services system falls below a given threshold. In prior formulations [e.g., [32] [33]], such emissions include those attributable to the materials (manufacture and installation of the technologies at the site) and operation of the system. 5. Energy market constraints. These include inter alia thresholds on the quantities of energy that may be procured externally, exported off-site, or wasted. Figure 3: Showing (LHS) a cut-away diagram of a Capstone C600 gas turbine [35] and (RHS) the energy flows for this technology normalized to the electrical energy output. The objective function for the optimization process is defined in economic terms, expressed as a minimum total cost of meeting the on-site energy demand. Various components are considered in its formulation. Principally, the total cost is decomposed into an annuitized fixed cost element and a variable cost element. The fixed cost element thus accounts for the discounting process and the time value of money, as well as the replacement / major overhaul interval of the technology. The variable cost element is primarily expressed in terms of the unit cost of procurement of energy in a particular form, supplied to the site, multiplied by the volume of that energy used in a specified time period. Through constraints taken to apply in the optimization process, this cost element may reflect particular operational strategies imposed, for example, a user-defined decision to operate bespoke cogeneration plant components at full load. As it is within the scope of the energy services infrastructure to export energy in various forms off the site, revenues arising from any such sales are treated as negative costs. Given the nature of the decision variables indicated, in prior deployments Mixed Integer Programming (MIP) has been found effective in establishing optimal configurations and equipment operating strategies, this technique having been broadly applied in production planning, sequencing processes, distribution and logistics problems, refinery planning, power plant scheduling, and process design. MIP captures the complexity of polygeneration systems in synthesis problems such as that described for optimal on-site energy supply. Formulation of the problem in MIP compatible terms presents opportunities to benefit from significant recent advances in the mathematical programming field such as those found in [36]. 332
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Proceedings e report 90
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ECOS 2012 : the 25 th International
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Advisory Committee (Track Organizer
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The 25 th ECOS Conference 1987-2012
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VOLUME VI CONTENT VI. 1 CARBON CAPT
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-----------------------------------
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» Personal transportation energy c
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» Excess enthalpies of second gene
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VOLUME IV IV . 1 - FLUID DYNAMICS A
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» Exergy analysis and genetic algo
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» Optimal lighting control strateg
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» Stability and limit cycles in an
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PROCEEDINGS OF ECOS 2012 - THE 25 T
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Fig. 2. The exergy destruction of M
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ineffectiveness of the catalyst, in
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[ P EXmth EX SNG] ESR F where EXm
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Fuel inputkW 728221 203771 237719 3
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Not only at desined Rc (the ratio o
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4. DISCUSSION The graphic exergy an
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Figure 9(a) illustrates the energy
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Engineering Technical Conferences &
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generally there are four kinds of m
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However, even under the off-design
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Fig. 3. 600MW supercritical coal-fi
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CO2 rich loading(molCO2/molMEA) 0.4
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Net efficiency (%) 40.28 30.29 26.4
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Fig. 11. Schematic diagram of heat
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28.67%. In addition, through heat i
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Applied Thermal Engineering 2010;30
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Abstract: PROCEEDINGS OF ECOS 2012
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Fig. 1. Solution diagram of „four
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K ~ K 1 eK 1a p K 1a iK mK
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Oxygen recovery rate, % 105 95 85 7
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Calculated optimal compressor press
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The net amount of the flue gas leav
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Figure 3. Idea for lignite drying b
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Energy efficiency, % 34,00 33,00 32
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points efficiency increase related
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Figure A1b. Thermoflex flow sheet o
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Figure A3a. Thermoflex flow sheet o
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Figure A4. Thermoflex flow sheet of
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2.1. Life Cycle Assessment 2.1.1. G
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these factors. A sensitivity analys
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methane slip contributes to 13% of
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As can be seen in Fig. 5 the impact
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References [1] Eurobserv’er, The
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in which CO2 is separated from H2,
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dimensions of water droplets and wa
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Temperature (°C) 8 7 6 5 4 3 2 1 0
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4. Conclusions In the present paper
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penalty, but without separate captu
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0.5 - 0.7 kg/kg, resulting typicall
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3.3 - Utilising waste heat All exis
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4.5 - Suomusjärvi olivine deposits
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material will change from a few % t
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Fig. 5 Schematic diagram of the PFB
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8. Combined SO2 capture and CO2 min
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Fig. 10 Carbonate- (left) and sulph
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[13] Ekdahl, E., Idman, H. Statemen
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HEAT Fig. 1. Scheme and main reacti
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(raw) materials pre-heating and AS
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Fig 3- Aspen Plus® model 110
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Table 4. Elemental and XRD analysis
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the ÅA CSM route. The content of M
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moisture - 0,2237 ash - 0,0876 LHV
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2.2 CFB plant For the current momen
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The CO2 emission factor which indic
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The CO2 emission factor calculated
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Appendix A Fig A.1. Simulation mode
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(b) Fig. A.2. Simulation model of C
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Table A.1. Calculated parameters at
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References [1] Pfaff I., Oexmann J.
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with a CC installation could be avo
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2.3. Basic CHP plant indices. The b
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3. Off-design calculations The main
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Fig. 6. CHP plant efficiency Fig. 7
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amount of steam delivered to the he
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[5] Lukowicz H., Mroncz M.: Analysi
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water removal is feasible by coolin
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Figure 2. Sketch of the suggested P
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a b Figure 5. a) Exergy balance of
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The basis of comparison of each met
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Figure 7a. Impact of S/CATR on PCU
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However, more energy duty is requir
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3.1. JACOBIAN-ASPEN Interface ASPEN
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Fig. 3. Triple Pressure Bottoming S
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As seen from Table 1, the mass flow
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4. Conclusion Fig. 5. Partial Emiss
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[19] Yantovski E., Shokotov M., Sho
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investigation and the developing of
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University of L’Aquila and German
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hydrogen combustion technology at d
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eversibility of a pre-treated synth
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Fig. 1. TG curves collected for spe
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(see Fig 5). Carbon dioxide uptake
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period is reduced to the first 15 c
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CO2 capture capacity up to 150 th c
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the way for biogas utilisation is t
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projectand is described in the foll
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4. Pilot plant tests In order to de
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Concerning the absorption reaction,
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with Regeneration (AwR), consists i
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[25] Aspen Plus 2004.1. Cambridge,
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systems for fuel drying waste strea
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heated in a heat exchanger placed i
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Fig. 2. Lower heating value of fuel
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Acknowledgements The results presen
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large amount of CO2 [1,5-7]. APCr a
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N 1 i ( x x i n 1 m ) 2.3. Carbon
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DTA analysis of the untreated APCrs
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Table 4. Relative Error (%) of the
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CaO HCl CaCl H O 193kJ / mol (
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[1] M. Fernández Bertos, S.J.R. Si
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2. Process development 2.1. Backgro
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to dissolve the chloride salts prec
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The remaining 25% (5 ml/min) of the
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Table 6. Experimental conversions o
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p p 1 1. 5 p H 1 1. 5 2O p mi
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Calculating from the Aspen Plus mod
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[11] Mattila, H.-P., Experimental s
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Mg2SiO4(s) + 2CO2(g) →2MgCO3(s) +
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(Mg/Fe) of the mineral rock, Mg-sil
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Figure 2. Effect of Mg/Fe ratio of
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Figure 4 shows that an increase in
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Figure 5. Process flow diagram of M
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2. Only cold utility is needed belo
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extraction process at 400 °C (~ 62
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Table A2. Extraction equations and
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[13] Teir S, Kuusik R, Fogelholm CJ
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In the area of coal technologies al
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Tabel 1. Characteristics quantities
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N m eT 4a ~ T cp T4a 1 ( K
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Auxiliary power rate of ASU, % 40 4
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[8] Buhre B.J.P., Elliott L.K., She
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1.1 - Cement Manufacturing The ceme
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2. Numerical model The purpose of t
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-0.309x15.77x2 2.085x3 240x4 12.53x
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Table 4 - Results of the optimizati
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1. Define the studied system (in th
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cycle) or a lignin extraction plant
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limiting factor for the maximum siz
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Table 5. Key data for the four ener
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Page 337 and 338: In the presented paper a framework
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Page 341 and 342: PROCEEDINGS OF ECOS 2012 - THE 25 T
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Page 345 and 346: were decreased by 10°C.All scenari
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Page 349 and 350: 3.2.2. Input data and assumptions A
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Page 361 and 362: 6. Extensions to core methodology 6
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Page 367 and 368: Abstract: PROCEEDINGS OF ECOS 2012
Page 369 and 370: gasification [15], hybrid biomass g
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1. Introduction - Firenze University Press

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