Volume 2 , Issue 1 , PP: 08-22, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Mona Mohamed 1 * , Nissreen El Saber 2
Doi: https://doi.org/10.54216/NIF.020101
Present era, several technologies are combining in various industries to strengthen sustainable ecological, economic, and societal. For example, in storage energy industrial where a sophisticated technique for storing thermal energy called thermal energy storage (TES) can lessen the effects on the environment and enable cleaner and more effective energy systems. Particularly, thermochemical energy storage (TES) which is characterized by substantial density of energy. So, selecting suitable material among the set of materials is crucial process. This study emphasized employing durable techniques to elucidate complex interrelationships between criteria and several materials. Thus, this study employs Multi-criteria Decision Making (MCDM) methods. Also, we are supporting these methods with robust theory represents in neutrosohic theory to fortify MCDM methods in uncertainty and non-aligned situations. Moreover, we are utilizing Multi-objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA) assists with Single Value Neutrosophic sets (SVNs). Finally, we applied our constructed framework to a real case study to guarantee that our framework is accurate and valid.
Thermochemical , Material Selection , MULTIMOORA , Neutrosophic Sets , with Single Value Neutrosophic sets (SVNs)
[1] F. Hussain, M. Z. Rahman, A. N. Sivasengaran, and M. Hasanuzzaman, “Energy storage technologies,” in Energy for Sustainable Development, Elsevier, 2020, pp. 125–165.
[2] G. Leonzio, “Solar systems integrated with absorption heat pumps and thermal energy storages: state of art,” Renewable and Sustainable Energy Reviews, vol. 70, no. August 2015, pp. 492–505, 2017, doi: 10.1016/j.rser.2016.11.117.
[3] M. Colak and İ. Kaya, “Multi-criteria evaluation of energy storage technologies based on hesitant fuzzy information: A case study for Turkey,” Journal of Energy Storage, vol. 28, p. 101211, 2020.
[4] J. E. Miller, A. H. McDaniel, and M. D. Allendorf, “Considerations in the design of materials for solar‐driven fuel production using metal‐oxide thermochemical cycles,” Advanced Energy Materials, vol. 4, no. 2, p. 1300469, 2014.
[5] S. P. Casey, J. Elvins, S. Riffat, and A. Robinson, “Salt impregnated desiccant matrices for ‘open’thermochemical energy storage—Selection, synthesis and characterisation of candidate materials,” Energy and buildings, vol. 84, pp. 412–425, 2014.
[6] J. M. Thomas, P. P. Edwards, P. J. Dobson, and G. P. Owen, “Decarbonising energy: The developing international activity in hydrogen technologies and fuel cells,” Journal of Energy Chemistry, vol. 51, pp. 405–415, 2020, doi: 10.1016/j.jechem.2020.03.087.
[7] Y. Wu, L. Liu, J. Gao, H. Chu, and C. Xu, “An extended VIKOR-based approach for pumped hydro energy storage plant site selection with heterogeneous information,” Information (Switzerland), vol. 8, no. 3, pp. 1–19, 2017, doi: 10.3390/info8030106.
[8] J. L. Espinoza-Acosta, P. I. Torres-Chávez, J. L. Olmedo-Martínez, A. Vega-Rios, S. Flores-Gallardo, and E. A. Zaragoza-Contreras, “Lignin in storage and renewable energy applications: A review,” Journal of energy chemistry, vol. 27, no. 5, pp. 1422–1438, 2018.
[9] N. Sinan and E. Unur, “Hydrothermal conversion of lignocellulosic biomass into high-value energy storage materials,” Journal of energy chemistry, vol. 26, no. 4, pp. 783–789, 2017.
[10] J. Lin, Q. Zhao, H. Huang, H. Mao, Y. Liu, and Y. Xiao, “Applications of low-temperature thermochemical energy storage systems for salt hydrates based on material classification: A review,” Solar Energy, vol. 214, no. November 2020, pp. 149–178, 2021, doi: 10.1016/j.solener.2020.11.055.
[11] G. Karagiannakis, C. Pagkoura, E. Halevas, P. Baltzopoulou, and A. G. Konstandopoulos, “Cobalt/cobaltous oxide based honeycombs for thermochemical heat storage in future concentrated solar power installations: Multi-cyclic assessment and semi-quantitative heat effects estimations,” Solar Energy, vol. 133, pp. 394–407, 2016.
[12] R. J. Clark, A. Mehrabadi, and M. Farid, “State of the art on salt hydrate thermochemical energy storage systems for use in building applications,” Journal of Energy Storage, vol. 27, no. July 2019, p. 101145, 2020, doi: 10.1016/j.est.2019.101145.
[13] A. P. Muroyama, A. J. Schrader, and P. G. Loutzenhiser, “Solar electricity via an Air Brayton cycle with an integrated two-step thermochemical cycle for heat storage based on Co3O4/CoO redox reactions II: Kinetic analyses,” Solar Energy, vol. 122, pp. 409–418, 2015.
[14] G. Van de Kaa, “Strategies for the emergence of a dominant design for heat storage systems,” Technology Analysis and Strategic Management, vol. 34, no. 1, pp. 58–70, 2022, doi: 10.1080/09537325.2021.1884851.
[15] A. H. Abedin, “A Critical Review of Thermochemical Energy Storage Systems,” The Open Renewable Energy Journal, vol. 4, no. 1, pp. 42–46, 2011, doi: 10.2174/1876387101004010042.
[16] X. Han et al., “Critical review of thermochemical energy storage systems based on cobalt, manganese, and copper oxides,” Renewable and Sustainable Energy Reviews, vol. 158, no. May 2021, p. 112076, 2022, doi: 10.1016/j.rser.2022.112076.
[17] V. Palomba and A. Frazzica, “Recent advancements in sorption technology for solar thermal energy storage applications,” Solar Energy, vol. 192, pp. 69–105, 2019.
[18] B. Swaraj Kumar, J. Varghese, and J. Jacob, “Optimal thermochemical material selection for a hybrid thermal energy storage system for low temperature applications using multi criteria optimization technique,” Materials Science for Energy Technologies, vol. 5, pp. 452–472, 2022, doi: 10.1016/j.mset.2022.10.005.
[19] S. Hosouli, J. Elvins, J. Searle, S. Boudjabeur, J. Bowyer, and E. Jewell, “A Multi-Criteria decision making (MCDM) methodology for high temperature thermochemical storage material selection using graph theory and matrix approach,” Materials & Design, vol. 227, p. 111685, 2023.
[20] Q. Zhang, J. Hu, J. Feng, and A. Liu, “A novel multiple criteria decision making method for material selection based on GGPFWA operator,” Materials and Design, vol. 195, p. 109038, 2020, doi: 10.1016/j.matdes.2020.109038.
[21] K. Zhang, J. Zhan, and Y. Yao, “TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection,” Information Sciences, vol. 502, pp. 297–329, 2019.
[22] S. Shahinur, A. M. M. S. Ullah, M. Noor-E-Alam, H. Haniu, and A. Kubo, “A decision model for making decisions under epistemic uncertainty and its application to select materials,” AI EDAM, vol. 31, no. 3, pp. 298–312, 2017.
[23] M. Abdel-Basset, A. Gamal, R. K. Chakrabortty, and M. J. Ryan, “Evaluation of sustainable hydrogen production options using an advanced hybrid MCDM approach: A case study,” International Journal of Hydrogen Energy, vol. 46, no. 5, pp. 4567–4591, 2021.
[24] A. M. M. S. Ullah and M. Noor-E-Alam, “Big data driven graphical information based fuzzy multi criteria decision making,” Applied Soft Computing, vol. 63, pp. 23–38, 2018.
[25] K. T. Atanassov, Intuitionistic fuzzy sets: theory and applications, vol. 35. Physica, 1999.
[26] F. Smarandache, “Neutrosophy: neutrosophic probability, set, and logic: analytic synthesis & synthetic analysis,” 1998.
[27] N. A. Nabeeh et al., “A Comparative Analysis for a Novel Hybrid Methodology using Neutrosophic theory with MCDM for Manufacture Selection,” in 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2022, pp. 1–8.
[28] A. Abdel-Monem and A. A. Gawad, “A hybrid Model Using MCDM Methods and Bipolar Neutrosophic Sets for Select Optimal Wind Turbine: Case Study in Egypt,” Neutrosophic Sets and Systems, vol. 42, pp. 1–27, 2021.
[29] N. A. Nabeeh, A. Abdel-Monem, and A. Abdelmouty, A hybrid approach of neutrosophic with multimoora in application of personnel selection. Infinite Study, 2019.
[30] M. Abdel-Basset, M. Mohamed, A. Abdel-Monem, and M. A. Elfattah, “New extension of ordinal priority approach for multiple attribute decision-making problems: design and analysis,” Complex & Intelligent Systems, vol. 8, no. 6, pp. 4955–4970, 2022.
[31] N. A. Nabeeh, A. Abdel-Monem, and A. Abdelmouty, “A novel methodology for assessment of hospital service according to BWM, MABAC, PROMETHEE II,” Neutrosophic Sets and Systems, vol. 31, pp. 63–79, 2020.
[32] K. E. N’tsoukpoe, H. Liu, N. Le Pierrès, and L. Luo, “A review on long-term sorption solar energy storage,” Renewable and Sustainable Energy Reviews, vol. 13, no. 9, pp. 2385–2396, 2009.
[33] D. Mugnier and V. Goetz, “Energy storage comparison of sorption systems for cooling and refrigeration,” Solar Energy, vol. 71, no. 1, pp. 47–55, 2001.
[34] J. Sunku Prasad, P. Muthukumar, F. Desai, D. N. Basu, and M. M. Rahman, “A critical review of high-temperature reversible thermochemical energy storage systems,” Applied Energy, vol. 254, no. October 2018, p. 113733, 2019, doi: 10.1016/j.apenergy.2019.113733.
[35] R.-J. Clark et al., “Experimental screening of salt hydrates for thermochemical energy storage for building heating application,” Journal of Energy Storage, vol. 51, p. 104415, 2022.
[36] M. Ghommem, G. Balasubramanian, M. R. Hajj, W. P. Wong, J. A. Tomlin, and I. K. Puri, “Release of stored thermochemical energy from dehydrated salts,” International journal of heat and mass transfer, vol. 54, no. 23–24, pp. 4856–4863, 2011.
[37] P. Tatsidjodoung, N. Le Pierrès, and L. Luo, “A review of potential materials for thermal energy storage in building applications,” Renewable and Sustainable Energy Reviews, vol. 18, pp. 327–349, 2013, doi: 10.1016/j.rser.2012.10.025.
[38] A. and Shanian and O. Savadogo, “A material selection model based on the concept of multiple attribute decision making,” Materials & Design, vol. 27, no. 4, pp. 329–337, 2006.
[39] A. Hambali, S. M. Sapuan, N. Ismail, and Y. Nukman, “Material selection of polymeric composite automotive bumper beam using analytical hierarchy process,” Journal of Central South University of Technology, vol. 17, pp. 244–256, 2010.
[40] U. M. Gaddala and J. K. Devanuri, “A hybrid decision-making method for the selection of a phase change material for thermal energy storage,” Journal of Thermal Science and Engineering Applications, vol. 12, no. 4, p. 41020, 2020.
[41] H.-C. Liu, J.-X. You, L. Zhen, and X.-J. Fan, “A novel hybrid multiple criteria decision making model for material selection with target-based criteria,” Materials & Design, vol. 60, pp. 380–390, 2014.
[42] A.-H. Peng and X.-M. Xiao, “Material selection using PROMETHEE combined with analytic network process under hybrid environment,” Materials & design, vol. 47, pp. 643–652, 2013.
[43] A. Loganathan and I. Mani, “A fuzzy based hybrid multi criteria decision making methodology for phase change material selection in electronics cooling system,” Ain shams engineering journal, vol. 9, no. 4, pp. 2943–2950, 2018.
[44] G. Tian, H. Zhang, Y. Feng, D. Wang, Y. Peng, and H. Jia, “Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 682–692, 2018.
[45] G. Büyüközkan, “An integrated fuzzy multi-criteria group decision-making approach for green supplier evaluation,” International Journal of Production Research, vol. 50, no. 11, pp. 2892–2909, 2012.
[46] R. J. Girubha and S. Vinodh, “Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component,” Materials & Design, vol. 37, pp. 478–486, 2012.
[47] K. A. Eldrandaly, N. El Saber, M. Mohamed, and M. Abdel-Basset, “Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies,” Sustainability (Switzerland), vol. 14, no. 12, 2022, doi: 10.3390/su14127376.
[48] E. K. Zavadskas, R. Baušys, I. Leščauskienė, and J. Omran, “M-generalised q-neutrosophic MULTIMOORA for decision making,” Studies in Informatics and Control, vol. 29, no. 4, pp. 389–398, 2020.
[49] E. K. Zavadskas, A. Čereška, J. Matijošius, A. Rimkus, and R. Bausys, “Internal combustion engine analysis of energy ecological parameters by neutrosophic MULTIMOORA and SWARA methods,” Energies, vol. 12, no. 8, p. 1415, 2019.
[50] E. K. Zavadskas, R. Bausys, I. Lescauskiene, and A. Usovaite, “MULTIMOORA under interval-valued neutrosophic sets as the basis for the quantitative heuristic evaluation methodology HEBIN,” Mathematics, vol. 9, no. 1, p. 66, 2020.
[51] Z. Tian, J. Wang, J. Wang, and H. Zhang, “An improved MULTIMOORA approach for multi-criteria decision-making based on interdependent inputs of simplified neutrosophic linguistic information,” Neural Computing and Applications, vol. 28, pp. 585–597, 2017.
[52] D. Stanujkic, E. K. Zavadskas, F. Smarandache, W. K. M. Brauers, and D. Karabasevic, “A neutrosophic extension of the MULTIMOORA method,” Informatica, vol. 28, no. 1, pp. 181–192, 2017.
[53] I. Siksnelyte, E. K. Zavadskas, R. Bausys, and D. Streimikiene, “Implementation of EU energy policy priorities in the Baltic Sea Region countries: Sustainability assessment based on neutrosophic MULTIMOORA method,” Energy Policy, vol. 125, pp. 90–102, 2019.
[54] M. Abdel-Basset, A. Gamal, N. Moustafa, A. Abdel-Monem, and N. El-Saber, “A Security-by-Design Decision-Making Model for Risk Management in Autonomous Vehicles,” IEEE Access, 2021.
[55] E. K. Zavadskas, R. Bausys, B. Juodagalviene, and I. Garnyte-Sapranaviciene, “Model for residential house element and material selection by neutrosophic MULTIMOORA method,” Engineering Applications of Artificial Intelligence, vol. 64, pp. 315–324, 2017.