Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor
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Fakhrony Sholahudin Rohman
,
Dinie Muhammad
Abstract
A multi-objective optimization (MOO) technique to produce a low-density polyethylene (LDPE) is applied to address these two problems: increasing conversion and reducing operating cost (as the first optimization problem, P1) and increasing productivity and reducing operating cost (as the second optimization problem, P2). ASPEN Plus software was utilized for the model-based optimization by executing the MOO algorithm using the tubular reactor model. The multi-objective optimization of multi-objective Bonobo optimisers (MOBO-I, MOBO-II and MOBO-III) are utilised to solve the optimization problem. The performance matrices, including hypervolume, pure diversity, and distance, are used to decide on the best MOO method. An inequality constraint was introduced on the temperature of the reactor to prevent run-away. According to the findings of the study, the MOBO-II for Problems 1 and 2 was the most effective MOO strategy. The reason is that the solution set found represents the most accurate, diversified, and acceptable distribution points alongside the Pareto Front (PF) in terms of homogeneity. The minimum operating cost, the maximum conversion and productivity obtained by MOBO-II are Mil. RM/year 114.3, 31.45 %, Mil. RM/year 545.3, respectively.
Funding source: Universiti Teknologi Malaysia
Award Identifier / Grant number: Professional Development Research University Grant
Acknowledgments
The financial support from Universiti Teknologi Malaysia through Professional Development Research University Grant (Vote Number: Q.J130000.21A2.07E17) is greatly acknowledged.
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Research ethics: Not applicable.
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Author contributions: Fakhrony S Rohman contributes on Conceptualization, Methodology, Data Curation, Formal analysis, Writing - Original Draft, Writing - Review & Editing. Sharifah Rafidah Wan Alwi contributes on Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition. Dinie Muhammad contributes on Software, Methodology, Formal analysis. Ashraf Azmi contributes on Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition. Khairul Azly Zahan contributes on Writing - Review & Editing. Siti Nor Azreen Ahmad Termizi contributes on Writing - Review & Editing.
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Competing interests: The authors declare no competing interests.
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Research funding: This study was funded by Universiti Teknologi Malaysia Professional Development Research University Grant (UTM-PDRU) with Vote number: Q.J130000.21A2.07E17.
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Data availability: Datasets used and/or analysed during the study are available from the author on reasonable request.
References
1. Tang, Q, Liu, S, Liu, J, Wang, Y, Wang, Y, Wang, S, et al.. Novel cuboid-like crystalline complexes (CLCCs), photon emission, fluorescent fibers, and bright red fabrics of Eu3+ complexes adjusted by amphiphilic molecules. Polymers 2022;14:905. https://doi.org/10.3390/polym14050905.Search in Google Scholar PubMed PubMed Central
2. Saldívar-Guerra, E, Ordaz-Quintero, A, Infante-Martínez, R, Herrera-Ordóñez, J, Villarreal-Cárdenas, L, Ramírez-Wong, D, et al.. Some factors affecting the molecular weight distribution (MWD) in low density polyethylene multizone autoclave polymerization reactors. Macromol React Eng 2016;10:123–39. https://doi.org/10.1002/mren.201500030.Search in Google Scholar
3. Patel, RM. Types and basics of polyethylene. In: Spalding, MA, Chatterjee, AM, editors. Handbook of industrial polyethylene and technology. Beverly, Massachusetts: Scrivener Publishing; 2017:105–38 pp.10.1002/9781119159797.ch4Search in Google Scholar
4. Burdett, ID, Eisinger, RS. Ethylene polymerization processes and manufacture of polyethylene. Beverly, Massachusetts: John Wiley & Sons, Inc; 2017:61–103 pp.10.1002/9781119159797.ch3Search in Google Scholar
5. Rohman, FS, Wan Alwi, SR, Muhammad, D, Idris, I, Zahan, KA, Murat, MN, et al.. Artificial hummingbird-based optimisation with advanced crowding distance of energy reduction in the polyethylene reactors. Process Integr Optim Sustainability 2023;8:271–84. https://doi.org/10.1007/s41660-023-00369-0.Search in Google Scholar
6. Azmi, A, Rohman, FS, Idris, I, Zainol, MM, Sudibyo. Sensitivity study of input parameters in the industrial low density polyethylene tubular reactor. Mater Today Proc 2022;63:S195–202. https://doi.org/10.1016/j.matpr.2022.02.404.Search in Google Scholar
7. Azmi, A, Sata, SA, Rohman, FS, Aziz, N. Optimization studies of low-density polyethylene process: effect of different interval numbers. Chem Prod Process Model 2020:15:20190125. https://doi.org/10.1515/cppm-2019-0125.Search in Google Scholar
8. Boopathy, MBM. A comprehensive dynamic model for high-pressure tubular low-density polyethylene (LDPE) reactors. [PhD Thesis]. Ames, Iowa: Iowa State University; 2006.Search in Google Scholar
9. Agrawal, N, Rangaiah, GP, Ray, AK, Gupta, SK. Multi-objective optimization of the operation of an industrial low-density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptations. Ind Eng Chem Res 2006;45:3182–99. https://doi.org/10.1021/ie050977i.Search in Google Scholar
10. Erdeghem, PMMV, Logist, F, Heughebaert, M, Dittrich, C, Impe, JFV. Conceptual modelling and optimization of jacketed tubular reactors for the production of LDPE. In: Vande, Wouwer A, Smets, I, Kothare, M, Tade, M, Proceedings of the 9th international symposium on dynamics and control of process systems (DYCOPS 2010). Leuven, Belgium: IFAC, Elsevier Ltd; 2010:419–24 pp.Search in Google Scholar
11. Gujarathi, AM, Babu, BV. Multi-objective optimization of low density polyethylene (LDPE) tubular reactor using strategies of differential evolution. In: Handbook of optimization, Zelinka, I, Snášel, V, Abraham, A. Berlin Heidelberg: Springer; 2013:615–39 pp.10.1007/978-3-642-30504-7_25Search in Google Scholar
12. Wolpert, DH, Macready, WG. No free lunch theorems for optimization. IEEE Trans Evol Comput 1997;1:67–82. https://doi.org/10.1109/4235.585893.Search in Google Scholar
13. Abdelghany, RY, Kamel, S, Sultan, HM, Khorasy, A, Elsayed, SK, Ahmed, M. Development of an improved Bonobo optimizer and its application for solar cell parameter estimation. Sustainability 2021;13:3863. https://doi.org/10.3390/su13073863.Search in Google Scholar
14. Asteasuain, M, Tonelli, SM, Brandolin, A, Bandoni, JA. Dynamic simulation and optimisation of tubular polymerisation reactors in gPROMS. Comput Chem Eng 2001;25:509–15. https://doi.org/10.1016/s0098-1354(01)00631-7.Search in Google Scholar
15. Brandolin, A, Lacunza, MH, Ugrin, PE, Capiati, NJ. High pressure polymerization of ethylene. An improved mathematical model for industrial tubular reactors. Polym React Eng 1996;4:193–241. https://doi.org/10.1080/10543414.1996.10744475.Search in Google Scholar
16. Cioffi, M, Hoffmann, AC, Janssen, LPBM. Reducing the gel effect in free radical polymerization. Chem Eng Sci 2001;56:911–15. https://doi.org/10.1016/s0009-2509(00)00305-5.Search in Google Scholar
17. Azmi, A, Sata, SA, Rohman, FS, Aziz, N. Melt flow index of low-density polyethylene determination based on molecular weight and branching properties. J Phys Conf 2019;1349:012094. https://doi.org/10.1088/1742-6596/1349/1/012094.Search in Google Scholar
18. Muhammad, D, Ahmad, Z, Aziz, N. Modeling and nonlinearity studies of low density polyethylene (LDPE) tubular reactor. Mater Today Proc 2018;5:21612–19. https://doi.org/10.1016/j.matpr.2018.07.010.Search in Google Scholar
19. Bokis, CP, Ramanathan, S, Franjione, J, Buchelli, A, Call, ML, Brown, AL. Physical properties, reactor modeling, and polymerization kinetics in the low-density polyethylene tubular reactor process. Ind Eng Chem Res 2002;41:1017–30. https://doi.org/10.1021/ie010308e.Search in Google Scholar
20. Stoiljkovic, D, Jovanović, S. Compression, supramolecular organization and free radical polymerization of ethylene gas. Polyolefins J 2019;6:23–41.Search in Google Scholar
21. Bekri, NL, Idris, I, Som, AM, Murat, MN, Rohman, FS, Ilyas, RA, et al.. Study of input parameter changes toward low density polyethylene’s product properties. Mater Today Proc 2023;74:425–32. https://doi.org/10.1016/j.matpr.2022.11.140.Search in Google Scholar
22. Das, AK, Nikum, AK, Krishnan, SV, Pratihar, DK. Multi-objective Bonobo optimizer (MOBO): an intelligent heuristic for multi-criteria optimization. Knowl Inf Syst 2020;62:4407–44. https://doi.org/10.1007/s10115-020-01503-x.Search in Google Scholar
23. ICIS. Chemicals, polyethylene prices, news and analysis; 2018. https://www.icis.com/chemicals-and-the-economy/2018/05/us-ethylene-prices-near-all-time-lows-as-over-capacity-arrives/ [Accessed 14th March 2022].Search in Google Scholar
24. Zhang, S, Li, S, Harley, RG, Habetler, TG. Performance evaluation and comparison of multi-objective optimization algorithms for the analytical design of switched reluctance machines. CES Trans Electr Mach Syst 2017;1:58–65. https://doi.org/10.23919/tems.2017.7911109.Search in Google Scholar
25. Wang, H, Jin, Y, Yao, X. Diversity assessment in many-objective optimization. IEEE Trans Cybern 2017;47:323–9. https://doi.org/10.1109/tcyb.2016.2550502.Search in Google Scholar PubMed
26. Rohman, FS, Muhammad, D, Zahan, KA, Murat, MN. Operation and design optimisation of industrial low-density polyethylene tubular reactor for multiple objectives using an evolutionary algorithm-based strategy. Process Integr Optim Sustainability 2023;7:655–72. https://doi.org/10.1007/s41660-023-00308-z.Search in Google Scholar
27. Duchateau, J, Castañeda-Zúñiga, D, Neuteboom, P, Toloza, C, Tacx, J, Reynolds, A, et al.., Chapter 7.9: SABIC high-pressure process for LDPE: CTR™ technology. In: Meyers, RA, editor. Handbook of petrochemicals production processes, 2nd ed. New York: McGraw Hill Professional: Access Engineering; 2019.Search in Google Scholar
28. Dhib, R, Al-Nidawy, N. Modelling of free radical polymerisation of ethylene using difunctional initiators. Chem Eng Sci 2002;57:2735–46. https://doi.org/10.1016/s0009-2509(02)00156-2.Search in Google Scholar
29. Rohman, FS, Zahan, KA, Muhammad, D, Murat, MN. Pareto optimisation of low density polyethylene (LDPE) in tubular reactor using a hybrid strategy. Chem Eng Technol 2022;45:2292–303. https://doi.org/10.1002/ceat.202200295.Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Review
- Computational chemistry unveiled: a critical analysis of theoretical coordination chemistry and nanostructured materials
- Research Articles
- Reducing sludge formation by enhancing biological decay of biomass: a mathematical model
- Numerical investigation of discharge pressure effect on steam ejector performance in renewable refrigeration cycle by considering wet steam model and dry gas model
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- Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction
- A parametric study on syngas production by adding CO2 and CH4 on steam gasification of biomass system using ASPEN Plus
- Temperature optimization model to inhibit zero-order kinetic reactions
- Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor
- Assessment the thermal performance of square twisted double tube heat exchanger with Al2O3 nanofluid
- Short Communication
- Layouts and tips for a typical final-year chemical engineering graduation project
Articles in the same Issue
- Frontmatter
- Review
- Computational chemistry unveiled: a critical analysis of theoretical coordination chemistry and nanostructured materials
- Research Articles
- Reducing sludge formation by enhancing biological decay of biomass: a mathematical model
- Numerical investigation of discharge pressure effect on steam ejector performance in renewable refrigeration cycle by considering wet steam model and dry gas model
- Natural pigment indigoidine production: process design, simulation, and techno-economic assessment
- Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction
- A parametric study on syngas production by adding CO2 and CH4 on steam gasification of biomass system using ASPEN Plus
- Temperature optimization model to inhibit zero-order kinetic reactions
- Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor
- Assessment the thermal performance of square twisted double tube heat exchanger with Al2O3 nanofluid
- Short Communication
- Layouts and tips for a typical final-year chemical engineering graduation project
