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Research Papers

Robust Multi-Objective Optimization of Solid Oxide Fuel Cell–Gas Turbine Hybrid Cycle and Uncertainty Analysis

[+] Author and Article Information
Shivom Sharma

Industrial Process and Energy Systems
Engineering,
École Polytechnique Fédérale de Lausanne,
EPFL,
Sion CH-1951, Switzerland
e-mail: shivom.sharma@epfl.ch

François Maréchal

Industrial Process and Energy Systems
Engineering,
École Polytechnique Fédérale de Lausanne,
EPFL,
Sion CH-1951, Switzerland
e-mail: francois.marechal@epfl.ch

Manuscript received August 22, 2016; final manuscript received April 9, 2018; published online May 7, 2018. Editor: Wilson K. S. Chiu.

J. Electrochem. En. Conv. Stor. 15(4), 041007 (May 07, 2018) (9 pages) Paper No: JEECS-16-1113; doi: 10.1115/1.4039944 History: Received August 22, 2016; Revised April 09, 2018

Chemical process optimization problems often have multiple and conflicting objectives, such as capital cost, operating cost, production cost, profit, energy consumptions, and environmental impacts. In such cases, multi-objective optimization (MOO) is suitable in finding many Pareto optimal solutions, to understand the quantitative tradeoffs among the objectives, and also to obtain the optimal values of decision variables. Gaseous fuel can be converted into heat, power, and electricity, using combustion engine, gas turbine (GT), or solid oxide fuel cell (SOFC). Of these, SOFC with GT has shown higher thermodynamic performance. This hybrid conversion system leads to a better utilization of natural resource, reduced environmental impacts, and more profit. This study optimizes performance of SOFC–GT system for maximization of annual profit and minimization of annualized capital cost, simultaneously. For optimal SOFC–GT designs, the composite curves for maximum amount of possible heat recovery indicate good performance of the hybrid system. Further, first law energy and exergy efficiencies of optimal SOFC–GT designs are significantly better compared to traditional conversion systems. In order to obtain flexible design in the presence of uncertain parameters, robust MOO of SOFC–GT system was also performed. Finally, Pareto solutions obtained via normal and robust MOO approaches are considered for parametric uncertainty analysis with respect to market and operating conditions, and solution obtained via robust MOO found to be less sensitive.

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References

Sharma, S. , and Rangaiah, G. P. , 2013, “Multi-Objective Optimization Applications in Chemical Engineering,” Multi-Objective Optimization in Chemical Engineering: Developments and Applications, G. P., Rangaiah and A., Bonilla-Petriciolet , eds., Wiley, Chichester, UK. [CrossRef]
Facchinetti, E. , Favrat, D. , and Maréchal, F. , 2014, “Design and Optimization of an Innovative Solid Oxide Fuel Cell–Gas Turbine Hybrid Cycle for Small Scale Distributed Generation,” Fuel Cells, 14(4), pp. 595–606. [CrossRef]
Ramadhani, F. , Hussain, M. A. , Mokhlis, H. , and Hajimolana, S. , 2017, “Optimization Strategies for Solid Oxide Fuel Cell (SOFC) Application: A Literature Survey,” Renewable Sustainable Energy Rev., 76, pp. 460–484. [CrossRef]
Marchetti, A. , Gopalkrishnan, A. , Chachuat, B. , Bonvin, D. , Tsikonis, L. , Nakajo, A. , Wuillemin, Z. , and Van herle, J. , 2011, “Robust Real-Time Optimization of a Solid Oxide Fuel Cell Stack,” ASME J. Fuel Cell Sci. Technol., 8(5), p. 051001.
Hajabdollahi, Z. , and Fu, P. F. , 2017, “Multi-Objective Based Configuration Optimization of SOFC-GT Cogeneration Plant,” Appl. Therm. Eng., 112, pp. 549–559. [CrossRef]
Tock, L. , and Maréchal, F. , 2015, “Decision Support for Ranking Pareto Optimal Process Designs Under Uncertain Market Conditions,” Comput. Chem. Eng., 83, pp. 165–175. [CrossRef]
Mitra, K. , 2013, “Chance Constrained Programming to Handle Uncertainty in Nonlinear Process Models,” Multi-Objective Optimization in Chemical Engineering: Developments and Applications, G. P., Rangaiah and A., Bonilla-Petriciolet , eds., Wiley, Chichester, UK. [CrossRef]
Palazzi, F. , Autissier, N. , Maréchal, F. , and Favrat, D. , 2007, “A Methodology for Thermo-Economic Modeling and Optimization of Solid Oxide Fuel Cell Systems,” Appl. Therm. Eng., 27(16), pp. 2703–2712. [CrossRef]
Caliandro, P. , Tock, L. , Ensinas, A. V. , and Maréchal, F. , 2014, “Thermo-Economic Optimization of a Solid Oxide Fuel Cell—Gas Turbine System Fueled With Gasified Lignocellulosic Biomass,” Energy Convers. Manage., 85, pp. 764–773. [CrossRef]
Deb, K. , and Gupta, H. , 2006, “Introducing Robustness in Multi-Objective Optimization,” Evol. Computation, 14(4), pp. 463–494. [CrossRef]
Sharma, S. , Celebi, A. D. , and Maréchal, F. , 2017, “Robust Multi-Objective Optimization of Gasifier and Solid Oxide Fuel Cell Plant for Electricity Production Using Wood,” Energy, 137, pp. 811–822. [CrossRef]
Van herle, J. , Maréchal, M. , Leuenberger, S. , and Favrat, D. , 2003, “Energy Balance Model of a SOFC Cogenerator Operated With Biogas,” J. Power Sources, 118(1–2), pp. 375–383. [CrossRef]
Pelster, S. , 1998, “Environomic Modeling and Optimization of Advanced Combined Cycle Cogeneration Power Plants Including CO2 Separation Options,” Ph.D. thesis, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, pp. 89–90.
Maréchal, F. , Palazzi, F. , Godat, J. , and Favrat, D. , 2004, “Thermo-Economic Modelling and Optimization of Fuel Cell Systems,” Fuel Cells, 5(1), pp. 5–24. [CrossRef]
Turton, R. , Bailie, R. C. , Whiting, W. B. , and Shaeiwitz, J. A. , 2012, Analysis, Synthesis and Design of Chemical Processes, 4th ed., Prentice Hall, Upper Saddle River, NJ.
Rangaiah, G. P. , Sharma, S. , and Sreepathi, B. K. , 2015, “Multi-Objective Optimization for the Design and Operation of Energy Efficient Chemical Processes and Power Generation,” Curr. Opin. Chem. Eng., 10, pp. 49–62. [CrossRef]
Wang, Z. , and Rangaiah, G. P. , 2017, “Application and Analysis of Methods for Selecting an Optimal Solution From the Pareto-Optimal Front Obtained by Multi-Objective Optimization,” Ind. Eng. Chem. Res., 56(2), pp. 560–574. [CrossRef]
Kirschner, M. , 2012, Oxygen. In: Ullmann's Encyclopedia of Industrial Chemistry, Wiley-VCH, Weinheim, Germany.

Figures

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Fig. 1

A simplified schematic of solid oxide fuel cell with gas turbine (1—SR, 2—SOFC, 3—CGT, 4—AGT, and 5—CO2 compression): stream data correspond to Pareto solution 25 in Fig.4

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Fig. 2

(a) Multi-objective optimization of SOFC–GT system using OSMOSE program, which has four main parts: MOO program, plant simulation, energy integration, and performance evaluation and (b) parametric uncertainty analysis methodology

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Fig. 3

Pareto solutions obtained for SOFC–GT system from normal MOO

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Fig. 4

Uncertainty analysis of chosen SOFC–GT designs obtained from normal MOO

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Fig. 5

Composite curves for Pareto solutions 25 (normal MOO, Fig. 4) and 11 (robust MOO, Fig. 7)

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Fig. 6

All solutions obtained for SOFC–GT system from robust MOO

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Fig. 7

Uncertainty analysis of chosen SOFC–GT designs obtained from robust MOO

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Fig. 8

Ranking of chosen SOFC–GT designs obtained from normal and robust MOO approaches

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Fig. 9

Relative change in the annual profit for Pareto solutions 25 (normal MOO, Fig. 4) and 11 (robust MOO, Fig. 7)

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Fig. 10

Effect of SOFC–GT plant size on the levelized electricity cost (box plot)

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