1. Introduction - Firenze University Press

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PROCEEDINGS OF ECOS 2012 - THE 25 TH INTERNATIONAL CONFERENCE ON EFFICIENCY, COST, OPTIMIZATION, SIMULATION AND ENVIRONMENTAL IMPACT OF ENERGY SYSTEMS JUNE 26-29, 2012, PERUGIA, ITALY Maximising the Use of Renewables with Variable Availability Andreja Nemet a , Jií Jaromír Klemeš a *, Petar Sabev Varbanov a , Zdravko Kravanja b a Centre for Process Integration and Intensification - CPI 2 , Research Institute of Chemical and Process Engineering, Faculty of Information Technology, University of Pannonia, Veszprém, Hungary, klemes@cpi.uni-pannon.hu (CA) b Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia, Abstract: A problem connected with the exploitation of renewable energy sources, such as wind and sun is, their fluctuating availability. The accelerating development has been very substantial for techniques, methodologies and equipment for exploiting solar energy [1]. The integration of renewables into an energy system needs an approach that accounts for the variations in energy supply availability, as well as for those of the demands. Dynamic models could be used for modelling precisely is intermittency. They are usually employed to solve servo- and regulatory tasks in process control. Dynamic models have been used to model solar thermal plants [2-4], but only a few models have been dedicated to estimating available energy from solar sources [5] and they usually evaluate only a part of the whole capture system – e.g. the thermal storage [6]. However, dynamic models are unsuitable for design or long-horizon operational optimisation. In the present work, the Heat Integration [7] for batch processes based on Time Slices [8, 9] is extended to the integration of solar thermal energy with certain variations. The main steps involve partitioning the measured/forecasted heat availability profile using a large number of candidate time boundaries and then approximating it by a piecewise-constant profile using high-precision. The approximation profile is obtained by subjecting the candidate superset of time-boundaries to MILP optimisation thus minimising the integral inaccuracy. The integration of solar thermal energy can be performed for each Time Slice, after the optimal number of Time Slices has been selected with approximated constant load. Using heat storage, the heat can then be transferred between Time Slices. Keywords: Variations of Renewables, Renewable Availability Curve, Solar Thermal Energy Integration, Time Slices, Heat Integration 1. Introduction An accelerated development of techniques, methodologies and equipment for exploiting solar energy has been taking place recently. This helps to improve the existing technology. An example is solar-based water desalination [1]. A lot of attention has been focused on photovoltaic panels for producing electricity. There is also a significant potential for utilising solar irradiation as heat. Generally, thermal solar capture offers a higher efficiency compared to photovoltaic panels. The integration of renewables into a process system needs a specific approach due to the variations in energy supply availability from renewable sources, and fluctuations in the users’ energy demands. Two approaches can be used for integrating renewables and accounting for this variability: (i) A dynamic model formulation, followed by dynamic optimisation (ii) A multi-period model involving steady-states, associated with time intervals. 297
The advantage of dynamic models is that they accurately describe the system behaviour. They are usually employed to solve servo- and regulatory tasks during process control. There are dynamic models that describe plants using solar thermal-energy as a utility [2]. Several other models estimate solar irradiation [3, 4], and just a few models estimating the available solar thermal-energy and available electricity [5]. Typically, such models only evaluate a part of the whole capture system, for example, just thermal-energy storage [6]. The main drawback of dynamic models is that they are not favourable for design or long-horizon operational optimisation since these models are complex and presently computationally intensive. Models assuming steady-states are simpler and yet still capable of describing the systems with acceptable accuracy. As some of the variables are discretised, the computational time becomes much shorter. During batch processes, energy demands vary over time. In order to account for these variations, Batch Process Integration was formulated by Kemp and Deakin [7] who developed two models: (i) The Time Average Model, where the heat-loads are averaged throughout the time horizon, and (ii) The Time Slice Model, where the Time Slices are obtained by combining the starting and ending time points of the involved process streams. During each Time Slices Heat Integration is performed in the same manner as the continuous processes. A detailed description can be found in [8]. The batch process scheduling method using the MILP formulation with heat integration was another step in exploiting batch process heat integration [9]. A similar formulation of the problem was also used to design a HEN [10]. In addition, a different, more combinatorial approach has been developed for batch process scheduling based on the S graph, [11] where also scheduling influence is presented on the HEN synthesis. To enable the integration of heat-storage into the system design, combined with pinch analysis, another combinatorial approach was subsequently introduced using time decompositions of the processes [12]. Majozi [13] developed a mathematical model for optimising energy usage for a multi-purpose batch plant. The evolution of a batch heat exchanger network was described by Foo et al. [14]. The methodology of time decomposition was recently extended by Varbanov and Klemeš [15] for analysing Total Sites using the integration of renewables. A Comprehensive review of Process Integration, including batch, has been presented by Friedler [16, 17]. Muster-Slawitsch et al. [18] presented the annual load curve for a renewable energy source. While adequate for that work, the approach is not appropriate for the integration of solar thermal energy as developed in this work. The reason is that clustering of the loads leaves the temporal sequence out of consideration, accounting only for the load horizon. It lumps loads from different time intervals within the overall horizon (one year) at certain temperature and load levels. For the current problem accounting for the temporal sequence is essential, as the heat supply and demand streams may be active during different time intervals, since a batch process is considered. Ludig et al. [19] modelled a power system, investigating 14 different technologies for producing electricity. They evaluated optimal technology-mix from the viewpoint of cost. An interesting part of this work was how they dealt with the variations of renewable energy sources e.g. wind, hydro, solar. They created equal-length time slices and averaged the load of supply within each time slice. In contrast, in the present work the time durations of the TSs and the supply load are the result of a two-stage optimisation. It is a systematic approach compared to the heuristic used previously. The focus of previous work in the field of varying heat supply and demand was either on a variation of the process demand or the energy availability from renewable sources. This current work accounts for both aspects. An analogy from batch process integration is used. TSs, with loads assumed to be constant, was developed for varying the availability of solar thermal-energy. The procedure for integrating solar thermal-energy covers several steps: Heat recovery within batch processes Identifying the number of TSs and the values of the TS boundaries for solar irradiation Estimation of the supply loads 298
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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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» 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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Page 281 and 282: Tabel 1. Characteristics quantities
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atio, as well as the shift-syngas f
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Fig. 4. Effect of operating tempera
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SDG has slight effect on the syngas
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[26.] Castle WF. Air separation and
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1. Introduction - Firenze University Press

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