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Multiobjective thermodynamic and environmental optimization of the small scale LNG cold utilization system
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Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
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www.elsevier.com/locate/procedia
Energy EnergyProcedia Procedia142 00 (2017) (2017) 997–1002 000–000 www.elsevier.com/locate/procedia
9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK
Multiobjective thermodynamic and environmental optimization of The 15th International Symposium on District Heating and Cooling the small scale LNG cold utilization system Assessing feasibility of using thea,heat Baris Burak Kanbura,bthe , Xiang Limingc, Swapnil Dubey Choodemand-outdoor Fook Hoonga, Fei Duanb,* temperature function for a long-term district heat demand forecast Energy Research Institute @NTU, Interdisciplinary Graduate School, Nanyang Technological University, Singapore, 637141 aa
bb School
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798
a,b,c a a Nanyang Technological c 637371 School of Physical and Mathematical Sciences, Singapore, I. Andrić *, A. Pina , P. Ferrão , J. Fournierb., B.University, Lacarrière , O. Le Correc cc
a
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
The present study aims to conduct multiobjective optimization investigations by using the genetic algorithm on the small scale and combined LNG cold utilized micro cogeneration system. The system combines the Stirling engine and the microturbine plant with the related heat exchanger for the hot water production. Before the optimization, thermodynamic and environmental Abstractare performed in ranges of 288.15-313.15K with the constant relative humidity. Four different multiobjective analyses optimization cases are considered which are: maximization of the generated net work/minimization of the CO22 emission rate, District heating networks are commonly in the literature as one ofrate, the most effective of solutions for decreasing the maximization the generated net work/ maximization of the exergetic efficiency/ addressed minimization of the CO22 emission greenhouse gas the building sector. These of systems require high destruction/ investments minimization which are returned the heat maximization of emissions the thermalfrom efficiency, and minimization the overall exergy of thethrough CO22 emission sales. Due to theResults changed climate conditions and building renovation heat demandbyin15% the and future could decrease, rate, respectively. show that the net generated power and exergeticpolicies, efficiency decrease 32%, respectively prolonging the investment return period. while the thermal efficiency and overall exergy destruction increase by 3.3% and 25% with 40K temperature increment. The CO22 The mainfind scope of peak this paper assess the feasibility of using demand – outdoor temperature functionfunction for heat studies demand emissions their valueisatto298.15K. Due to different trendsthe ofheat the investigated parameters, multiobjective forecast. The district of Alvalade, located 308.15 in Lisbon used as a case study. Thecases. district is consisted of 665 infer that the best trade-off region is between and (Portugal), 313.15K forwas the investigated 4 optimization vary in both construction ©buildings 2017 Thethat Authors. Published by Elsevierperiod Ltd. and typology. Three weather scenarios (low, medium, high) and three district ©renovation 2017 The Authors. Published by Elsevier Ltd. intermediate, deep). To estimate the error, obtained heat demand values were scenarios were developed (shallow, Peer-review under responsibility of committee of of the the 9th 9th International International Conference Conference on Applied Applied Energy. Energy. Peer-review under responsibility of the the scientific scientific committee compared with results from a dynamic heat demand model, previously developed and validatedon by the authors. The results showed that when onlygenetic weather changeLNG is considered, margin of error could be acceptable for some applications Keywords: Multiobjective optimization, algorithm, cold energy,the thermodynamic analysis, environmental analysis. (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.The Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Small scale liquefied natural gas (LNG) regasification systems have been gaining importance thanks to their coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and advantages from the points of sustainability, security, and feasibility [1], and they are one of the alternatives for the improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +6567905510; fax: +65 67924062. Cooling. E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.345
Baris Burak Kanbur et al. / Energy Procedia 142 (2017) 997–1002 Baris Burak Kanbur et al./ Energy Procedia 00 (2017) 000–000
998 2
inland and stranded areas in the LNG importer countries to generate power and thermal energy by combining the small scale LNG regasification systems with the micro power generation plants which are known as the small scale LNG cold utilized micro power generation systems [2]. The small scale LNG cold energy integrated power generation systems are freshly new topics for the real engineering applications. Up to now, the LNG regasification processes have been integrated with the large scale power generation systems which include Rankine, combined Rankine/Brayton, or direct expansion cycles [3]. For the small scale LNG cold utilized micro cogeneration systems, the current authors of the presented paper presented two different and original designs [2,4,5]. The first design was based on the vaporization of the LNG by using the exhausted gas of the micro turbine system, and the related energetic/economic/environmental [4], and exergetic analyses [5] were studied. The combined cycle was proposed in the second design that integrated the Stirling engine into the LNG cold utilized micro turbine system, and the vaporization of the LNG was provided by the cold end of the Stirling engine [2]. In that study, it was seen that the energetic, exergetic, environmental and thermoeconomic performances of the combined design had different performance trends so that it was deduced that it was significantly difficult to determine the best operation points by considering all these parameters since they had different maximum and minimum performance points so that the multiobjective optimization studies were proposed. Up to now, various studies [6-10] used the multiobjective genetic algorithm optimization procedures for the power generation systems. This study focuses on the multiobjective optimization strategies of the small scale and combined LNG cold utilized system by using the genetic algorithm in MATLAB software according to thermodynamic and environmental parameters. Four different multiobjective cases are studied, and the best trade-off region is obtained. 2. System description The investigated combined LNG cold utilized micro-cogeneration system is presented in Fig. 1. The LNG is pumped from the LNG tank at atmospheric conditions as stream 1, and the pressurized LNG (stream 2) enters to the LNG vaporizer to be vaporized (stream 3) by using the heat transfer between the streams 16 and 17 as the thermalfluid loop of the cold heat exchanger of the Stirling engine and the LNG vaporizer. The required air for the combustion is provided from the ambient air (stream 4), and it is pressurized in the compressor (stream 5), then it is also heated (stream 6) by using the exhausted gas of the microturbine (stream 8). The turbine inlet temperature of the system is shown with stream 7. Stream 9 is the phase change material (PCM) inlet. PCM is used as thermal energy storage device to stabilize the control the outlet temperature (stream 10) constant. Thanks to the stored energy in the PCM tank, the heat transfer fluid is operated between the hot heat exchanger of the Stirling engine and the PCM tank (streams 12 and 13). The Stirling engine generates the electricity (stream 20) between the hot and cold heat exchanger parts. The heat exchanger generates thermal energy as hot water production (stream 15). The inlet water is city water (stream 14). The exhausted gas of the overall system is shown with stream 11. During the modeling and analyses, some assumptions are required, and they were mentioned in detail in our previous study [2].
Fig. 1. Small scale and combined LNG cold utilized micro-cogeneration system schematic.
Baris Burak Kanbur et al. / Energy Procedia 142 (2017) 997–1002 Baris Burak Kanbur et al./ Energy Procedia 00 (2017) 000–000
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3. Modeling and analyses The thermodynamic and environmental models are constituted for the presented system. It can be simply said that the thermodynamic model is based on four different parameters which are (1) the net generated work, (2) thermal (energetic) efficiency, (3) exergetic efficiency, and (4) exergy destruction rate for the overall system, respectively. The main equations of these parameters are presented in Eqs. (1)-(4), ̇ ̇
̇
̇ ̇
( ̇ ̇
̇
̇
⁄ ̇
̇
̇
(1) (2)
̇ ⁄ ̇ )
(3)
̇
(4)
where ̇ defines the net generated work from the overall system, ̇ is the generated work from the gas turbine, ̇ is the consumed work rate for the compressor, and ̇ is the consumed work rate by the LNG pump. The energetic efficiency ( ) is the ratio of the summation of the generated work rate and the thermal energy rate ( ̇ ) to the total heat input of the system ( ̇ ). The exergetic efficiency ( ) considers the exergy destruction ( ̇ ) and exergy loss ( ̇ ), and the ratio of summarized destruction and loss exergy rates to the total fuel exergy ( ̇ ) gives the exergetic efficiency parameter. For the system, exergy destruction can be produced by the exergy balance equation as shown in Eq. 4. In addition to the thermodynamic model, the environmental model is also prepared by using the generated work rate and the carbon dioxide emission rate from the system to the atmosphere as shown in Eq. (5), ̇ ⁄ ̇ (5) where shows the CO2 emission rate whereas , ̇ , and show the molar fraction of the carbon dioxide in the product gas, molar flow rate of the exhausted (product) gas, and the molar mass of carbon dioxide respectively. More details on the thermodynamic and environmental models can be found from our previous study [2]. 4. Optimization procedure Ten steps are presented for the optimization study as shown in Fig.2. First three steps are related to the analyses part. After that, the objective functions are generated for the multiobjective optimization. Four different multiobjective optimization cases are applied to determine the best operating parameters which are named as the best trade-off region. The net generated work, thermal efficiency, exergetic efficiency, carbon dioxide emission rates, and the overall exergy destruction rates are considered as parameters in the multiobjective optimization study. The maximization of the generated work, thermal efficiency, and exergetic efficiency are purposed while the minimization of the carbon dioxide emission rate and the overall exergy destruction are desired during the optimization study.
Fig. 2. General optimization steps of the proposed system.
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Baris Burak Kanbur et al. / Energy Procedia 142 (2017) 997–1002 Baris Burak Kanbur et al./ Energy Procedia 00 (2017) 000–000
Multiobjective optimization studies are carried out by genetic algorithm in MATLAB software. In the study, only one variable, the ambient air temperature in the ranges of 288.15-313.15K is used. Pareto frontiers are used to observe the optimal points. It is important to remind that all points on the Pareto frontier are feasible and optimal points according to investigated objective functions. Researchers can determine the best optimal point among the Pareto optimal points with respect to the desired purpose. 5. Results and Discussion Thermodynamic and environmental analyses of the designed system are presented in Fig. 3. The analyses show that the power generation rate and the exergetic efficiency decrease by rising of the ambient air temperature while the power generation rate and exergetic efficiency performances of the Stirling engine show opposite trends. The mechanical efficiency of the Stirling engine also increases with the air temperature increment. The main reason for the different trends between the Stirling engine and the overall cycle is the closed cycle operation of the Stirling engine. By rising of the ambient air temperature, the exhausted gas of the micro turbine increases so that the heat transfer rate in the Stirling engine rises while the heat transfer rate in the cold heat exchanger and LNG vaporizer decreases by rising of the ambient air temperature. Therefore, the enthalpy rate difference between the cold and hot heat exchangers grows up, and the generated power increases with the ambient air temperature increment. The thermal efficiency of the overall system and the mechanical efficiency of the Stirling engine grow up by increasing the air temperature. The carbon dioxide emission rate of the overall system reaches its peak at 298.15K and then it goes down, the minimum emission rate is found at the maximum ambient air temperature. Lastly, the exergy destruction shows continuously increment from low air temperature to high air temperature. As shown in Figs. 3a, b, and c, different thermodynamic and environmental parameters have different trends so that it is difficult to determine the best operating parameters for the overall system. Thus, a multiobjective optimization strategy is applied.
Fig. 3. Energetic and environmental performances of the small scale LNG cold utilized systems: (a) the power generation rates for the overall system and Stirling engine, (b) the energetic and exergetic efficiencies for the overall system and the Stirling engine, (c) the carbon dioxide emission rates and the exergy destruction for the overall system.
The Pareto frontier plots of the investigated cases are presented in Fig.4a, b, c, and d for Cases 1, 2, 3, and 4, respectively. The functions of the generated work, thermal efficiency, and the exergetic efficiency are considered with negative signs due to fact that the minimization of the negative functions is equal to the maximization of the real functions [10]. The results show that Cases 1, 2, and 4 have same Pareto frontier trends while Case 4 has a different Pareto frontier trend. As mentioned, Pareto frontier plots show the all optimal points for the investigated objective functions, and the numbers of the optimal points can be decreased by the decision maker. In this study, the determination of the best-trade off region is aimed instead of to determine a single optimal point. Fig. 4a presents 18 optimal points after 109 iterations, and only two of the optimal points are at the ambient air temperature of 288.15K. Apart from these optimal points, four optimal points belong to the air temperatures which are less than 308.15K. The rest of the optimal points are located at the air temperatures which are between 308.15 and 313.15K. Case 2 has 18 optimal points like Case 1, and two of the optimal points are located at the lowest ambient air temperature value.
Baris Burak Kanbur et al. / Energy Procedia 142 (2017) 997–1002 Baris Burak Kanbur et al./ Energy Procedia 00 (2017) 000–000
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However, the optimal points of Case 2 are obtained after 111 iterations. These two figures deduce that the similar performance trends of the net generated work and the exergetic efficiency provide the similar Pareto frontier plots. As known from the analyses, the thermal efficiency and net generated work rate trends completely contradict to each other so that the Pareto frontier plot represent the optimal regions in the very wide area as it can be seen in Fig. 4c with 49 optimal points after 124 iterations. Thus, it can be seen that the decision process becomes difficult when the thermal efficiency and the net generated work are considered as the objective functions. Fig. 4d illustrates the Pareto frontier plot for Case 4, and 18 optimal points are projected after 116 iterations. Similar to Cases 1 and 2, two optimal points are located at the ambient air temperature of 288.15K. Five of the optimal points are located at the air temperatures which are not greater than 308.15K that means the majority of the optimal points are located between 308.15K and 313.15K.
Fig. 4. Pareto frontier plots: (a) case 1, (b) case 2, (c) case 3, and (d) case 4.
Each Pareto frontier presents the optimal points of the different cases. To determine the common region for the optimal points of the all investigated objective functions, the scatter distribution of the cases are presented in Fig. 5a. It can be seen that Cases 1, 2, and 4 have a similar number of iterations (18) while Case 3 has 49 iterations.
Fig. 5. Scatter distributions of the cases for the multiobjective optimization.
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Baris Burak Kanbur et al. / Energy Procedia 142 (2017) 997–1002 Baris Burak Kanbur et al./ Energy Procedia 00 (2017) 000–000
According to the investigated cases, it can be said that 88% of the optimal points are at the ambient temperatures which are higher than 303.15K, and it is also seen that 61% of the optimal points are located between 308.15K and 313.15K for Cases 1, 2, and 4, respectively. Due to performance trends of the net generated work and the thermal efficiency, optimal points have very large distribution area, but it is possible to say that roundly 25% of the optimal points are located between 308.15K and 313.15K. When the optimal points of all cases are observed, it is possible to say that the air temperature range between 308.15K and 313.15K gives the best trade-off region for this study. Moreover, the higher air temperatures give better performances with respect to the performed parameters. 6. Conclusions The study performed multiobjective optimization procedures to different objective functions to determine the best trade-off region of the small scale and combined LNG cold utilized micro cogeneration systems. Five different parameters which were the net generated work, thermal efficiency, exergetic efficiency, overall exergy destruction, and the carbon dioxide emission rate were considered in the multiobjective optimization studies with four different cases. MATLAB based genetic algorithm was used during the multiobjective optimization studies, and it was seen that the ambient air temperature ranges of 308.15-313.15K gave the best trade-off region for the performed four different cases. Moreover, the study showed that the presented design had better-operating conditions at higher air temperatures. The presented study can be extended by considering different objectives from the points of thermoeconomic and exergoenvironmental analyses. Levelized cost, exergoeconomic factor, and the relative product cost difference trends may present smaller or greater best trade-off region for the investigated design. Furthermore, different parameters such as pressure ratio and relative humidity can be applied as variables so that the definitions of the objective functions can be expanded by using these parameters as variables. Acknowledgements The work was funded under the Energy Innovation Research Programme (EIRP, Award No. NRF2013EWTEIRP001-017), administrated by the Energy Market Authority (EMA). The EIRP is a competitive grant call initiative driven by the Energy Innovation Programme Office, and funded by the National Research Foundation (NRF). Besides, the authors gratefully thank to Dr. Kai Wang, Dr. Lu Qiu, Dr. Chenzen Ji, and Mr. Zhen Qin for their help on this original study. References [1] Program Committee D3, International Gas Union, Small scale LNG 2012-2015 triennium work report. World Gas Conference, Paris, 2015. [2] Kanbur BB, Xiang L, Dubey S, Choo FH, Duan F. Thermoeconomic and environmental assessments of a combined cycle for the small scale LNG cold utilization. Appl Energ, in press. [3] Romero Gomez M, Ferreiro Garcia R, Romero Gomez J, Carbia Carril J. Review of thermal cycles exploiting the exergy of liquefied natural gas in the regasification process. Renew Sust Energ Rev 2014;38:781-95. [4] Kanbur BB, Xiang L, Dubey S, Choo FH, Duan F. A micro cogeneration system with LNG cold utilization-part 1: energetic, economic and environmental analyses. Energy Procedia, in press. [5] Kanbur BB, Xiang L, Dubey S, Choo FH, Duan F. A micro cogeneration system with LNG cold utilization-part 2: exergy analyses. Energy Procedia, in press. [6] Khoshgoftar Manesh, Amidpour M. Multi-objective thermoeconomic optimization of coupling MSF desalination with PWR nuclear power plant through evolutionary algorithms. Desalination 2009;249:1332-44. [7] Mahmoudi SMS, Ghavimi AR. Thermoeconomic analysis and multi objective optimization of a molten carbonate fuel cell – Supercritical carbon dioxide – Organic Rankin cycle integrated power system using liquefied natural gas as heat sink. Appl Therm Eng 2016;107:1219-32. [8] Spelling J, Favrat D, Martin A, Augsburger G. Thermoeconomic optimization of a combined-cycle solar tower power plant. Energy 2012;41:113-20. [9] Barzegar Avval H, Ahmadi P, Ghaffarizadeh AR, Saidi MH. Thermo-economic-environmental multiobjective optimization of a gas turbine power plant with preheater using evolutionary algorithm. Int J Energ Res 2011;35:389-403. [10] Dincer I, Rosen MA. Exergy: energy, environment and sustaianable development. 2nd ed. UK: Elsevier Science; 2013.
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Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
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www.elsevier.com/locate/procedia
Energy EnergyProcedia Procedia142 00 (2017) (2017) 997–1002 000–000 www.elsevier.com/locate/procedia
9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK
Multiobjective thermodynamic and environmental optimization of The 15th International Symposium on District Heating and Cooling the small scale LNG cold utilization system Assessing feasibility of using thea,heat Baris Burak Kanbura,bthe , Xiang Limingc, Swapnil Dubey Choodemand-outdoor Fook Hoonga, Fei Duanb,* temperature function for a long-term district heat demand forecast Energy Research Institute @NTU, Interdisciplinary Graduate School, Nanyang Technological University, Singapore, 637141 aa
bb School
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798
a,b,c a a Nanyang Technological c 637371 School of Physical and Mathematical Sciences, Singapore, I. Andrić *, A. Pina , P. Ferrão , J. Fournierb., B.University, Lacarrière , O. Le Correc cc
a
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
The present study aims to conduct multiobjective optimization investigations by using the genetic algorithm on the small scale and combined LNG cold utilized micro cogeneration system. The system combines the Stirling engine and the microturbine plant with the related heat exchanger for the hot water production. Before the optimization, thermodynamic and environmental Abstractare performed in ranges of 288.15-313.15K with the constant relative humidity. Four different multiobjective analyses optimization cases are considered which are: maximization of the generated net work/minimization of the CO22 emission rate, District heating networks are commonly in the literature as one ofrate, the most effective of solutions for decreasing the maximization the generated net work/ maximization of the exergetic efficiency/ addressed minimization of the CO22 emission greenhouse gas the building sector. These of systems require high destruction/ investments minimization which are returned the heat maximization of emissions the thermalfrom efficiency, and minimization the overall exergy of thethrough CO22 emission sales. Due to theResults changed climate conditions and building renovation heat demandbyin15% the and future could decrease, rate, respectively. show that the net generated power and exergeticpolicies, efficiency decrease 32%, respectively prolonging the investment return period. while the thermal efficiency and overall exergy destruction increase by 3.3% and 25% with 40K temperature increment. The CO22 The mainfind scope of peak this paper assess the feasibility of using demand – outdoor temperature functionfunction for heat studies demand emissions their valueisatto298.15K. Due to different trendsthe ofheat the investigated parameters, multiobjective forecast. The district of Alvalade, located 308.15 in Lisbon used as a case study. Thecases. district is consisted of 665 infer that the best trade-off region is between and (Portugal), 313.15K forwas the investigated 4 optimization vary in both construction ©buildings 2017 Thethat Authors. Published by Elsevierperiod Ltd. and typology. Three weather scenarios (low, medium, high) and three district ©renovation 2017 The Authors. Published by Elsevier Ltd. intermediate, deep). To estimate the error, obtained heat demand values were scenarios were developed (shallow, Peer-review under responsibility of committee of of the the 9th 9th International International Conference Conference on Applied Applied Energy. Energy. Peer-review under responsibility of the the scientific scientific committee compared with results from a dynamic heat demand model, previously developed and validatedon by the authors. The results showed that when onlygenetic weather changeLNG is considered, margin of error could be acceptable for some applications Keywords: Multiobjective optimization, algorithm, cold energy,the thermodynamic analysis, environmental analysis. (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.The Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Small scale liquefied natural gas (LNG) regasification systems have been gaining importance thanks to their coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and advantages from the points of sustainability, security, and feasibility [1], and they are one of the alternatives for the improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +6567905510; fax: +65 67924062. Cooling. E-mail address: [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.345
Baris Burak Kanbur et al. / Energy Procedia 142 (2017) 997–1002 Baris Burak Kanbur et al./ Energy Procedia 00 (2017) 000–000
998 2
inland and stranded areas in the LNG importer countries to generate power and thermal energy by combining the small scale LNG regasification systems with the micro power generation plants which are known as the small scale LNG cold utilized micro power generation systems [2]. The small scale LNG cold energy integrated power generation systems are freshly new topics for the real engineering applications. Up to now, the LNG regasification processes have been integrated with the large scale power generation systems which include Rankine, combined Rankine/Brayton, or direct expansion cycles [3]. For the small scale LNG cold utilized micro cogeneration systems, the current authors of the presented paper presented two different and original designs [2,4,5]. The first design was based on the vaporization of the LNG by using the exhausted gas of the micro turbine system, and the related energetic/economic/environmental [4], and exergetic analyses [5] were studied. The combined cycle was proposed in the second design that integrated the Stirling engine into the LNG cold utilized micro turbine system, and the vaporization of the LNG was provided by the cold end of the Stirling engine [2]. In that study, it was seen that the energetic, exergetic, environmental and thermoeconomic performances of the combined design had different performance trends so that it was deduced that it was significantly difficult to determine the best operation points by considering all these parameters since they had different maximum and minimum performance points so that the multiobjective optimization studies were proposed. Up to now, various studies [6-10] used the multiobjective genetic algorithm optimization procedures for the power generation systems. This study focuses on the multiobjective optimization strategies of the small scale and combined LNG cold utilized system by using the genetic algorithm in MATLAB software according to thermodynamic and environmental parameters. Four different multiobjective cases are studied, and the best trade-off region is obtained. 2. System description The investigated combined LNG cold utilized micro-cogeneration system is presented in Fig. 1. The LNG is pumped from the LNG tank at atmospheric conditions as stream 1, and the pressurized LNG (stream 2) enters to the LNG vaporizer to be vaporized (stream 3) by using the heat transfer between the streams 16 and 17 as the thermalfluid loop of the cold heat exchanger of the Stirling engine and the LNG vaporizer. The required air for the combustion is provided from the ambient air (stream 4), and it is pressurized in the compressor (stream 5), then it is also heated (stream 6) by using the exhausted gas of the microturbine (stream 8). The turbine inlet temperature of the system is shown with stream 7. Stream 9 is the phase change material (PCM) inlet. PCM is used as thermal energy storage device to stabilize the control the outlet temperature (stream 10) constant. Thanks to the stored energy in the PCM tank, the heat transfer fluid is operated between the hot heat exchanger of the Stirling engine and the PCM tank (streams 12 and 13). The Stirling engine generates the electricity (stream 20) between the hot and cold heat exchanger parts. The heat exchanger generates thermal energy as hot water production (stream 15). The inlet water is city water (stream 14). The exhausted gas of the overall system is shown with stream 11. During the modeling and analyses, some assumptions are required, and they were mentioned in detail in our previous study [2].
Fig. 1. Small scale and combined LNG cold utilized micro-cogeneration system schematic.
Baris Burak Kanbur et al. / Energy Procedia 142 (2017) 997–1002 Baris Burak Kanbur et al./ Energy Procedia 00 (2017) 000–000
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3. Modeling and analyses The thermodynamic and environmental models are constituted for the presented system. It can be simply said that the thermodynamic model is based on four different parameters which are (1) the net generated work, (2) thermal (energetic) efficiency, (3) exergetic efficiency, and (4) exergy destruction rate for the overall system, respectively. The main equations of these parameters are presented in Eqs. (1)-(4), ̇ ̇
̇
̇ ̇
( ̇ ̇
̇
̇
⁄ ̇
̇
̇
(1) (2)
̇ ⁄ ̇ )
(3)
̇
(4)
where ̇ defines the net generated work from the overall system, ̇ is the generated work from the gas turbine, ̇ is the consumed work rate for the compressor, and ̇ is the consumed work rate by the LNG pump. The energetic efficiency ( ) is the ratio of the summation of the generated work rate and the thermal energy rate ( ̇ ) to the total heat input of the system ( ̇ ). The exergetic efficiency ( ) considers the exergy destruction ( ̇ ) and exergy loss ( ̇ ), and the ratio of summarized destruction and loss exergy rates to the total fuel exergy ( ̇ ) gives the exergetic efficiency parameter. For the system, exergy destruction can be produced by the exergy balance equation as shown in Eq. 4. In addition to the thermodynamic model, the environmental model is also prepared by using the generated work rate and the carbon dioxide emission rate from the system to the atmosphere as shown in Eq. (5), ̇ ⁄ ̇ (5) where shows the CO2 emission rate whereas , ̇ , and show the molar fraction of the carbon dioxide in the product gas, molar flow rate of the exhausted (product) gas, and the molar mass of carbon dioxide respectively. More details on the thermodynamic and environmental models can be found from our previous study [2]. 4. Optimization procedure Ten steps are presented for the optimization study as shown in Fig.2. First three steps are related to the analyses part. After that, the objective functions are generated for the multiobjective optimization. Four different multiobjective optimization cases are applied to determine the best operating parameters which are named as the best trade-off region. The net generated work, thermal efficiency, exergetic efficiency, carbon dioxide emission rates, and the overall exergy destruction rates are considered as parameters in the multiobjective optimization study. The maximization of the generated work, thermal efficiency, and exergetic efficiency are purposed while the minimization of the carbon dioxide emission rate and the overall exergy destruction are desired during the optimization study.
Fig. 2. General optimization steps of the proposed system.
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Multiobjective optimization studies are carried out by genetic algorithm in MATLAB software. In the study, only one variable, the ambient air temperature in the ranges of 288.15-313.15K is used. Pareto frontiers are used to observe the optimal points. It is important to remind that all points on the Pareto frontier are feasible and optimal points according to investigated objective functions. Researchers can determine the best optimal point among the Pareto optimal points with respect to the desired purpose. 5. Results and Discussion Thermodynamic and environmental analyses of the designed system are presented in Fig. 3. The analyses show that the power generation rate and the exergetic efficiency decrease by rising of the ambient air temperature while the power generation rate and exergetic efficiency performances of the Stirling engine show opposite trends. The mechanical efficiency of the Stirling engine also increases with the air temperature increment. The main reason for the different trends between the Stirling engine and the overall cycle is the closed cycle operation of the Stirling engine. By rising of the ambient air temperature, the exhausted gas of the micro turbine increases so that the heat transfer rate in the Stirling engine rises while the heat transfer rate in the cold heat exchanger and LNG vaporizer decreases by rising of the ambient air temperature. Therefore, the enthalpy rate difference between the cold and hot heat exchangers grows up, and the generated power increases with the ambient air temperature increment. The thermal efficiency of the overall system and the mechanical efficiency of the Stirling engine grow up by increasing the air temperature. The carbon dioxide emission rate of the overall system reaches its peak at 298.15K and then it goes down, the minimum emission rate is found at the maximum ambient air temperature. Lastly, the exergy destruction shows continuously increment from low air temperature to high air temperature. As shown in Figs. 3a, b, and c, different thermodynamic and environmental parameters have different trends so that it is difficult to determine the best operating parameters for the overall system. Thus, a multiobjective optimization strategy is applied.
Fig. 3. Energetic and environmental performances of the small scale LNG cold utilized systems: (a) the power generation rates for the overall system and Stirling engine, (b) the energetic and exergetic efficiencies for the overall system and the Stirling engine, (c) the carbon dioxide emission rates and the exergy destruction for the overall system.
The Pareto frontier plots of the investigated cases are presented in Fig.4a, b, c, and d for Cases 1, 2, 3, and 4, respectively. The functions of the generated work, thermal efficiency, and the exergetic efficiency are considered with negative signs due to fact that the minimization of the negative functions is equal to the maximization of the real functions [10]. The results show that Cases 1, 2, and 4 have same Pareto frontier trends while Case 4 has a different Pareto frontier trend. As mentioned, Pareto frontier plots show the all optimal points for the investigated objective functions, and the numbers of the optimal points can be decreased by the decision maker. In this study, the determination of the best-trade off region is aimed instead of to determine a single optimal point. Fig. 4a presents 18 optimal points after 109 iterations, and only two of the optimal points are at the ambient air temperature of 288.15K. Apart from these optimal points, four optimal points belong to the air temperatures which are less than 308.15K. The rest of the optimal points are located at the air temperatures which are between 308.15 and 313.15K. Case 2 has 18 optimal points like Case 1, and two of the optimal points are located at the lowest ambient air temperature value.
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However, the optimal points of Case 2 are obtained after 111 iterations. These two figures deduce that the similar performance trends of the net generated work and the exergetic efficiency provide the similar Pareto frontier plots. As known from the analyses, the thermal efficiency and net generated work rate trends completely contradict to each other so that the Pareto frontier plot represent the optimal regions in the very wide area as it can be seen in Fig. 4c with 49 optimal points after 124 iterations. Thus, it can be seen that the decision process becomes difficult when the thermal efficiency and the net generated work are considered as the objective functions. Fig. 4d illustrates the Pareto frontier plot for Case 4, and 18 optimal points are projected after 116 iterations. Similar to Cases 1 and 2, two optimal points are located at the ambient air temperature of 288.15K. Five of the optimal points are located at the air temperatures which are not greater than 308.15K that means the majority of the optimal points are located between 308.15K and 313.15K.
Fig. 4. Pareto frontier plots: (a) case 1, (b) case 2, (c) case 3, and (d) case 4.
Each Pareto frontier presents the optimal points of the different cases. To determine the common region for the optimal points of the all investigated objective functions, the scatter distribution of the cases are presented in Fig. 5a. It can be seen that Cases 1, 2, and 4 have a similar number of iterations (18) while Case 3 has 49 iterations.
Fig. 5. Scatter distributions of the cases for the multiobjective optimization.
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According to the investigated cases, it can be said that 88% of the optimal points are at the ambient temperatures which are higher than 303.15K, and it is also seen that 61% of the optimal points are located between 308.15K and 313.15K for Cases 1, 2, and 4, respectively. Due to performance trends of the net generated work and the thermal efficiency, optimal points have very large distribution area, but it is possible to say that roundly 25% of the optimal points are located between 308.15K and 313.15K. When the optimal points of all cases are observed, it is possible to say that the air temperature range between 308.15K and 313.15K gives the best trade-off region for this study. Moreover, the higher air temperatures give better performances with respect to the performed parameters. 6. Conclusions The study performed multiobjective optimization procedures to different objective functions to determine the best trade-off region of the small scale and combined LNG cold utilized micro cogeneration systems. Five different parameters which were the net generated work, thermal efficiency, exergetic efficiency, overall exergy destruction, and the carbon dioxide emission rate were considered in the multiobjective optimization studies with four different cases. MATLAB based genetic algorithm was used during the multiobjective optimization studies, and it was seen that the ambient air temperature ranges of 308.15-313.15K gave the best trade-off region for the performed four different cases. Moreover, the study showed that the presented design had better-operating conditions at higher air temperatures. The presented study can be extended by considering different objectives from the points of thermoeconomic and exergoenvironmental analyses. Levelized cost, exergoeconomic factor, and the relative product cost difference trends may present smaller or greater best trade-off region for the investigated design. Furthermore, different parameters such as pressure ratio and relative humidity can be applied as variables so that the definitions of the objective functions can be expanded by using these parameters as variables. Acknowledgements The work was funded under the Energy Innovation Research Programme (EIRP, Award No. NRF2013EWTEIRP001-017), administrated by the Energy Market Authority (EMA). The EIRP is a competitive grant call initiative driven by the Energy Innovation Programme Office, and funded by the National Research Foundation (NRF). Besides, the authors gratefully thank to Dr. Kai Wang, Dr. Lu Qiu, Dr. Chenzen Ji, and Mr. Zhen Qin for their help on this original study. References [1] Program Committee D3, International Gas Union, Small scale LNG 2012-2015 triennium work report. World Gas Conference, Paris, 2015. [2] Kanbur BB, Xiang L, Dubey S, Choo FH, Duan F. Thermoeconomic and environmental assessments of a combined cycle for the small scale LNG cold utilization. Appl Energ, in press. [3] Romero Gomez M, Ferreiro Garcia R, Romero Gomez J, Carbia Carril J. Review of thermal cycles exploiting the exergy of liquefied natural gas in the regasification process. Renew Sust Energ Rev 2014;38:781-95. [4] Kanbur BB, Xiang L, Dubey S, Choo FH, Duan F. A micro cogeneration system with LNG cold utilization-part 1: energetic, economic and environmental analyses. Energy Procedia, in press. [5] Kanbur BB, Xiang L, Dubey S, Choo FH, Duan F. A micro cogeneration system with LNG cold utilization-part 2: exergy analyses. Energy Procedia, in press. [6] Khoshgoftar Manesh, Amidpour M. Multi-objective thermoeconomic optimization of coupling MSF desalination with PWR nuclear power plant through evolutionary algorithms. Desalination 2009;249:1332-44. [7] Mahmoudi SMS, Ghavimi AR. Thermoeconomic analysis and multi objective optimization of a molten carbonate fuel cell – Supercritical carbon dioxide – Organic Rankin cycle integrated power system using liquefied natural gas as heat sink. Appl Therm Eng 2016;107:1219-32. [8] Spelling J, Favrat D, Martin A, Augsburger G. Thermoeconomic optimization of a combined-cycle solar tower power plant. Energy 2012;41:113-20. [9] Barzegar Avval H, Ahmadi P, Ghaffarizadeh AR, Saidi MH. Thermo-economic-environmental multiobjective optimization of a gas turbine power plant with preheater using evolutionary algorithm. Int J Energ Res 2011;35:389-403. [10] Dincer I, Rosen MA. Exergy: energy, environment and sustaianable development. 2nd ed. UK: Elsevier Science; 2013.