Multi-Criteria Decision Support in the Evaluation of Hydrodynamic Cavitation Effects – A Case Study

• Autorzy: Jerzy Montusiewicz , Aleksandra Szaja , Agnieszka Montusiewicz , Magdalena Lebiocka

Identyfikatory

DOI

10.12913/22998624/193789

• Języki publikacji: EN

Abstrakty

EN

The work describes a method for searching for the best variant(s) of cavitation of coffee waste, taking into account the defined five criteria: sCOD/COD and DOC/TOC, which were maximised, and the others, i.e. caffeine concentration, phenols concentration and energy consumption, which were minimised. The method used in the first stage determines non-dominated variants, then a compromise variant, assuming that all criteria used are equally important. Further analysis allows determining further compromise options. The method of determining compromise solutions is based on the use of Chebyshev metric, which has been enriched with a mechanism for normalising individual criteria (which allows for their comparison) and the ability to define the importance of individual criteria. For normalisation, the so-called the ideal point, which is an internal property of the analysed variants, is determined each time for a given subset of non-dominated variants. After determining the first compromise solution, the process of generating new ideal points (their number is equal to the size of the criteria space) begins by associating the components of this solution with the components of the ideal point. Using new reference points, the method determines additional compromise solutions. The five-criteria evaluation of 20 variants obtained in the experiment showed that when adopting different values of the importance of individual criteria, some variants are never selected as a compromise. The subset of compromise variants ranged from 3 to 6. The variants that were repeatedly selected as compromise variants, with different values of the importance of individual criteria, were the variants for which the cavitation process time was: 20 or 30 minutes and the cavitation inlet pressure was 5 bar. The multi-criteria assessment showed that these are the best compromise options and can be recommended for the coffee waste cavitation process.

Słowa kluczowe

EN

coffee waste; hydrodynamic cavitation; multi-criteria decision support; chebyshev metric

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 8
• Strony: 341--350
• Opis fizyczny: Bibliogr. 30 poz., fig., tab.

Twórcy

autor

Jerzy Montusiewicz

• Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38A, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-8571-3354

autor

Aleksandra Szaja

• Faculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-5007-5145

autor

Agnieszka Montusiewicz

• https://orcid.org/0000-0003-4849-3070

autor

Magdalena Lebiocka

• Faculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-2445-9890

Bibliografia

• 1. Yu X., Zhang S., Liao X., Qi X. ELECTRE methods in prioritized MCDM environment. Inf. Sci. 2018. 424. 301–316. https://doi.org/10.1016/j. ins.2017.09.061
• 2. Amirghodsi S., Naeini A.B., Makui A. An Integrated Delphi-DEMATEL-ELECTRE Method on Gray Numbers to Rank Technology Providers. IEEE T ENG MANAGE. 2022 69.(4). 1348–1364. https:// doi.org/10.1109/TEM.2020.2980127
• 3. Carra M., Botticini F., Pavesi F.C., Maternini G., Pezzagno M., Barabino B. A comparative cycling path selection for sustainable tourism in Franciacorta. An integrated AHP-ELECTRE method. Transp. Res. Proc. 2023. 69 . 448–455. https://doi.org/10.1016/j. trpro.2023.02.194
• 4. Brans J.P. Lingenierie de la decision. Elaboration dinstruments daide a la decision. Methode PROMETHEE. In Nadeau. R.. Landry. M. (Eds.). Laide a la Decision: Nature. Instrument set Perspectives Davenir. Presses de Universite Laval. Quebec. Canada. 1982. pp. 183–214.
• 5. Vincke Ph. and Brans J.P. A preference ranking organization method. The PROMETHEE method for MCDM. Manage Sci. 1985. 31(6). 641–656. http:// dx.doi.org/10.1287/mnsc.31.6.647
• 6. Eichfelder G. Adaptive Scalarization Methods in Multiobjective Optimization. Springer-Verlag Berlin Heidelberg. 2008.
• 7. Ulivieri V. Multicriteria optimization: Scalarization tecniques. Master’s Thesis. Sant’Anna School of Advanced Studies. 2014
• 8. Kasimbeyli R., Ozturk Z.K., Kasimbeyli N., Yalcin G.D., Erdem B.I. Comparison of Some Scalarization Methods in Multiobjective Optimization. Bull. Malays. Math. Sci. Soc. 2019. 42. 1875–1905. https://doi.org/10.1007/s40840-017-0579-4
• 9. Saaty T.L. How to make a decision: The analytic hierarchy process. EJOP 1990. 48. 9–26. https://doi. org/10.1016/0377-2217(90)90057-I
• 10. Saaty. T.L. and Sodenkamp. M. The Analytic Hierarchy and Analytic Network Measurement Processes: The Measurement of Intangibles. In: Zopounidis. C.. Pardalos. P. (eds) Handbook of Multicriteria Analysis. Applied Optimization. 103. Springer. Berlin. 2010. Heidelberg. https://doi. org/10.1007/978-3-540-92828-7_4
• 11. Liu P., Zhu B., Wang P. A weighting model based on best–worst method and its application for environmental performance evaluation. Appl. Soft Comput. 2021. 103. 107168. https://doi.org/10.1016/j. asoc.2021.107168
• 12. Ćetković J., Knežević M., Vujadinović R., Tombarević E., Grujić M. Selection of Wastewater Treatment Technology: AHP Method in Multi- Criteria Decision Making. Water 2023. 15. 1645. https://doi.org/10.3390/w15091645
• 13. Malik N., Singh V., Kumar K., Elumalai S.P. VOC source apportionment. reactivity. secondary transformations. and their prioritization using fuzzy‑AHP method in a coal‑mining city in India. Environ. Sci. Pollut. Res. 2024. 31. 25406–25423. https://doi. org/10.1007/s11356-024-32754-8
• 14. Ayhan M.B. A fuzzy AHP approach for supplier selection problem: a case study in a gearmotor company. IJMVSC. 2013. 4(3). https://doi.org/10.5121/ ijmvsc.2013.4302
• 15. Attri R., Grover S. Application of preference selection index method for decision making over the design stage of production system life cycle. J. King Saud Univ. Eng. Sci. 2015. 27. 207–216. http:// dx.doi.org/10.1016/j.jksues.2013.06.003
• 16. Shen S., Dragićević S., Dujmović J. GIS-based Logic Scoring of Preference method for urban densification suitability analysis. Comput Environ Urban Syst. 2021 (89). 101654. https://doi. org/10.1016/j.compenvurbsys.2021.101654
• 17. Hussain A. and Kim H.-M. EV Prioritization and Power Allocation During Outages: A Lexicographic Method-Based Multiobjective Optimization Approach. TTE 2021. 7(4). 2474–2487. https://doi. org/10.1109/TTE.2021.3063085
• 18. Liu C.-H. Solving the bi-objective optimisation problem with periodic delivery operations using a lexicographic method. Int. J. Prod. Res. 2016. 54(8). 2275–2283. https://doi.org/10.1080/00207543.201 5.1070969
• 19. Makwakwa T.A., Moema D.E., Msagati T.A.M. Multi‑criteria decision analysis: technique for order of preference by similarity to ideal solution for selecting greener analytical method in the determination of mifepristone in environmental water samples. Environ Sci Pollut R 2024. 31. 29460–29471. https://doi.org/10.1007/s11356-024-32961-3
• 20. Alpera D., Başdarb C. A Comparison of TOPSIS and ELECTRE Methods: An Application on the Factoring Industry. Int. j. bus. economics res. 2017. 8(3). 627–646. https://doi.org/10.20409/berj.2017.70
• 21. Xu X., Huang Y., Chen K. Method for large group emergency decision making with complex preferences based on emergency similarity and interval consistency. Nat. Hazards 2019. 97. 45–64. https:// doi.org/10.1007/s11069-019-03624-1
• 22. Chunhua F., Shi H., Guozhen B. A group decision making method for sustainable design using intuitionistic fuzzy preference relations in the conceptual design stage. J. Clean. 2020. 243. 118640. https:// doi.org/10.1016/j.jclepro.2019.118640
• 23. Surdacki P. and Montusiewicz J. Approach to multicriterion optimization of quench performance of superconducting winding. IEEE Trans. Magn. 1996. 32. 1266–1269. https://doi.org/10.1109/20.497475
• 24. Pareto V. Cours d’economic politique. Rouge. Lousanne 1896.
• 25. Montusiewicz J. Ranking Pareto optimal solutions in genetic algorithm by using the undifferentiation interval method. In: Burczyński T. (ed.) Evolutionary Methods in Mechanics. 2004. Kluwer Academic Publishers. 265-276.
• 26. Antkiewicz M. and Myszkowski P.B. Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints. Inf. Sci. 2024. 667. 120400. https://doi. org/10.1016/j.ins.2024.120400
• 27. Kesireddy A., Medrano F.A. EliteMulti-Criteria DecisionMaking—Pareto Front Optimization in Multi-Objective Optimization. Algorithms 2024. 17. 206. https://doi.org/10.3390/a17050206
• 28. Roy B. Methodologie MulticritEre d’Aide A la Decisin. Editions Economica, 1985.
• 29. Saaty T. The analytic hierarchy process. McGraw– Hill, 1980.
• 30. Szaja A., Montusiewicz A., Pasieczna-Patkowska S., Grządka E., Montusiewicz J., Lebiocka M. Pre-Treatment of Spent Coffee Grounds Using Hydrodynamic Cavitation. Energies 2024. 17. 2229. https://doi.org/10.3390/en17092229