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L-Lactide ring-opening polymerization: a multi-objective optimization approach through mathematical modeling

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Abstract

As industries move towards sustainable product development, biopolymers such as polylactide are gaining significant attention owing to their self-degradability and eco-friendliness. Therefore, a multi-objective optimization problem (MOOP) formulation to obtain high-performance polylactide concerning physicochemical properties is designed through mathematical modeling and solved using the Elitist Non-dominated Sorting Genetic Algorithm (NSGA II). The current work is focused on improving the polymer growth mechanisms with stannous octoate (catalyst) and 1-dodecanol (co-catalyst) by analyzing three different case studies using optimization approach. In the first study, the Pareto front for batch L-lactide ring-opening polymerization (L-ROP) with objective functions of average molecular weight, polydispersity index, and time is obtained. Further investigations on esterification, chain propagation and the ratio of monomer–catalyst and cocatalyst–catalyst is carried out. The optimized result using certain range of initial reagent concentrations is determined and one of the suitable Pareto optimal solution for case study 1 gives Mw = 610 kDa, PDI = 1.8, time = 100 s; case study 2 is Mw = 560 kDa, λ1/λ0 = 4300, λ0 = 70; case study 3 is Mw = 500 kDa, M/C = 33,800, ROH/C = 8.5. The neighboring optimal solutions in the Pareto front have been classified into 3 groups and the corresponding process parameters for the particular outcome are tabulated. Process modeling and optimization in close vicinity with appropriate experimental data are distinct aspects of this work to apply in industrial plant level.

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Abbreviations

Sn(Oct)2 :

Stannous octoate

ROH:

Alcohol

PL:

Polylactide

GA:

Genetic algorithm

MOOP:

Multi-objective optimization problem

NSGA II:

Elitist non-dominated sorted genetic algorithm

PO:

Pareto optimal

Ki:

Kinetic rate constant

Mw:

Weight average molecular weight

Mn:

Number average molecular weight

MWD:

Molecular weight distribution

PDI:

Polydispersity index

t:

Polymerization time

M/C :

Monomer/catalyst ratio

ROH/C:

Co-catalyst/catalyst ratio

λ0:

Zeroth moment equation for active chain

λ1:

First moment equation for active chain

λ2:

Second moment equation for active chain

µ0:

Zeroth moment equation for dormant chain

µ1:

First moment equation for dormant chain

µ2:

Second moment equation for dormant chain

γ0:

Zeroth moment equation for dead chain

γ1:

First moment equation for dead chain

γ2:

Second moment equation for dead chain

References

  1. Vert M, Li SM, Spenlehauer G, Guerin P (1992) Bioresorbability and biocompatibility of aliphatic polyesters. J Mater Sci Mater Med 3:432–446

    Article  CAS  Google Scholar 

  2. Gilding DK, Reed AM (1979) Biodegradable polymers for use in surgery-polyglycolic/poly(actic acid) homo- and copolymers: Polymer 20:1459–1464

  3. Mainil-Varlet P, Rahn B, Gogolewski S (1997) Long-term in vivo degradation and bone reaction to various polylactides: 1. One-year results Biomaterials 18:257–266

    CAS  PubMed  Google Scholar 

  4. Hartmann MH (1998) High molecular weight polylactic acid polymers. Springer, Berlin

    Book  Google Scholar 

  5. Auras R, Harte B, Selke S (2004) An overview of polylactides as packaging materials. Macromol Biosci 4:835–864

    Article  CAS  PubMed  Google Scholar 

  6. Paul GP, Virivinti N (2022) An outlook on recent progress in poly(lactic acid): polymerization, modeling, and optimization. Iran Polym J 31:59–81

    Article  CAS  Google Scholar 

  7. Desai H, Mehta T, Shah N (2023) Azeotropic dehydrative (solution) polycondensation of lactic acid to polylactic acid (PLA): A in-depth review of an overlooked method for manufacturing PLA. Polym Technol Mater 62:1394–1402

    CAS  Google Scholar 

  8. Singla P, Mehta R, Berek D, Upadhyay SN (2014) Ring opening polymerization of lactide in a monomode microwave using stannous octoate and dibutyltin dimethoxide catalysts. J Macromol Sci 51:350–361

    Article  CAS  Google Scholar 

  9. Jacobsen S, Fritz HG, Jerome R (1999) Polylactide (PLA)-A New Way of Production. Polym Eng Sci 39:1311–1319

    Article  CAS  Google Scholar 

  10. Datta R, Henry M (2006) Lactic acid: recent advances in products, processes and technologies: a review. J Chem Technol Biotechnol 81:1119–1129

    Article  CAS  Google Scholar 

  11. Carothers WH, Dorough GL, Natta FJV (1932) Studies of polymerization and ring formation. X. The reversible polymerization of six-membered cyclic esters. J Am Chem Soc 54:761–772

    Article  CAS  Google Scholar 

  12. Kricheldorf HR, Lee SR (1995) Polylactones: 32. High-molecular-weight polylactides by ring-opening polymerization with dibutylmagnesium or butylmagnesium chloride. Polymer 36:2995–3003

    Article  CAS  Google Scholar 

  13. Kricheldorf HR, Kreiser-Saunders I, Stricker A (2000) Polylactones 48. SnOct2-initiated polymerizations of lactide: a mechanistic study. Macromolecules 33:702–709

    Article  CAS  Google Scholar 

  14. Penczek S, Duda A, Kowalski A, Libiszowski J, Majerska K, Biela T (2000) On the mechanism of polymerization of cyclic esters induced by tin(II) octoate. Macromol Symp 157:61–70

    Article  CAS  Google Scholar 

  15. Awd Allah MM, Abdel-Aziem W, Abd El-baky MA (2023) Collapse behavior and energy absorbing characteristics of 3D-printed tubes with different infill pattern structures: an experimental study. Fibers Polym 24:2609–2622

    Article  Google Scholar 

  16. Abd-Elaziem W, Khedr M, Abd-Elaziem AE, Awd allah MM, Mousa AA, Yehia HM, Daoush WM, Abd El-baky MA (2023) Particle-reinforced polymer matrix composites (PMC) fabricated by 3D printing. J Inorg Organomet Polym Mater 2023:1-18

  17. Awd Allah MM, Abd El-baky MA, Alshahrani H, Sebaey TA, Hegazy DA (2023) Multi attribute decision making through COPRAS on tensile properties of hybrid fiber metal laminate sandwich structures for aerospace and automotive industries. J Compos Mater 57:3757–3773

    Article  CAS  Google Scholar 

  18. Destro F, Barolo M (2022) A review on the modernization of pharmaceutical development and manufacturing: trends, perspectives, and the role of mathematical modeling. Int J Pharm 620:121715

    Article  CAS  PubMed  Google Scholar 

  19. Zambaldi E, Magalhães RR, Dias MC, Mendes LM, Tonoli GHD (2022) Numerical simulation of poly(lactic acid) polymeric composites reinforced with nanofibrillated cellulose for industrial applications. Polym Eng Sci 62:4043–4054

    Article  CAS  Google Scholar 

  20. Long T, Zhang C, Liu H, chen X, Zhao S, Zhou C (2017) Molecular weight distribution simulation in equilibrium ring-opening polymerization: a new macroscopic model. Macromol Theory Simulations 26:1-7

  21. Liu Y, Wei H, Wang J, Li Q (2018) Numerical simulation of the crack formation in the quenched poly(l-lactic acid) spherulites. Macromol Theory Simul 27:1–6

    Article  Google Scholar 

  22. Eenink MJD, Feijen J, Olijslager J, Albers JHM, Rieke JC, Greidanus PJ (1987) Biodegradable hollow fibres for the controlled release of hormones. J Control Release 6:225–247

    Article  CAS  Google Scholar 

  23. Zhang X, MacDonald DA, Goosen MFA, McAuley KB (1994) Mechanism of lactide polymerization in the presence of stannous octoate: The effect of hydroxy and carboxylic acid substances. J Polym Sci Part A 32:2965–2970

    Article  CAS  Google Scholar 

  24. Puaux JP, Banu I, Nagy I, Bozga G (2007) A study of L-lactide ring-opening polymerization kinetics. Macromol Symp 259:318–326

    Article  CAS  Google Scholar 

  25. Mehta R, Kumar V, Upadhyay SN (2007) Mathematical modeling of the poly(lactic acid) ring-opening polymerization using stannous octoate as a catalyst. Polym Plast Technol Eng 46:933–937

    Article  CAS  Google Scholar 

  26. Mehta R, Kumar V, Upadhyay SN (2007) Mathematical modeling of the poly(lactic acid) ring-opening polymerization kinetics. Polym Plast Technol Eng 46:257–264

    Article  CAS  Google Scholar 

  27. Witzke DR, Narayan R, Kolstad JJ (1997) Reversible kinetics and thermodynamics of the homopolymerization of L-lactide with 2-ethylhexanoic acid Tin(II) salt. Macromolecules 30:7075–7085

    Article  CAS  Google Scholar 

  28. Yu Y, Storti G, Morbidelli M (2009) Ring-opening polymerization of L, L-lactide: Kinetic and modeling study. Macromolecules 42:8187–8197

    Article  CAS  Google Scholar 

  29. Yu Y, Storti G, Morbidelli M (2011) Kinetics of ring-opening polymerization of l, l -lactide. Ind Eng Chem Res 50:7927–7940

    Article  CAS  Google Scholar 

  30. Alshahrani H, Sebaey TA, Awd Allah MM, Abd El-baky MA (2023) Multi-response optimization of crashworthy performance of perforated thin walled tubes. J Compos Mater 57:1579–1597

    Article  Google Scholar 

  31. Coello CAC (2000) Treating constraints as objectives for single-objective evolutionary optimization. Eng Optim 32:275–308

    Article  Google Scholar 

  32. Deb K (2011) Multi-objective evolutionary optimisation for product design and manufacturing. Springer, London

    Google Scholar 

  33. Horn J, Nafpliotis N, Goldberg DE (1994) A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE world congress on computational intelligence 1:82–87

  34. Virivinti N, Mitra K (2016) A comparative study of fuzzy techniques to handle uncertainty: an industrial grinding process. Chem Eng Technoiogy 2016:1–14

    Google Scholar 

  35. Virivinti N, Mitra K (2015) Intuitionistic fuzzy chance constrained programming for handling parametric uncertainty: an industrial grinding case study. Ind Eng Chem Res 54:6291–6304

    Article  CAS  Google Scholar 

  36. Virivinti N, Mitra K (2014) Fuzzy expected value analysis of an industrial grinding process. Powder Technol 268:9–18

    Article  CAS  Google Scholar 

  37. Wang H, Ji C, Shi C, Yang J, Wang S, Ge Y, Chang K, Meng H, Wang X (2023) Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm. Energy 263:125961

    Article  CAS  Google Scholar 

  38. Alizadeh S, Mahdavian M, Ganji E (2023) Optimal placement and sizing of photovoltaic power plants in power grid considering multi-objective optimization using evolutionary algorithms. J Electr Syst Inf Technol 10:7

    Article  Google Scholar 

  39. Gupta RR, Gupta SK (1999) Multiobjective optimization of an industrial nylon-6 semibatch reactor system using genetic algorithm. J Appl Polym Sci 73:729–739

    Article  CAS  Google Scholar 

  40. Raha S, Majumdar S, Mitra K (2004) Effect of caustic addition in epoxy polymerization process: a single and multi-objective evolutionary approach. Macromol Theory Simulations 13:152–161

    Article  CAS  Google Scholar 

  41. Mitra K, Majumdar S, Raha S (2004) Multiobjective dynamic optimization of a semi-batch epoxy polymerization process. Comput Chem Eng 28:2583–2594

    Article  CAS  Google Scholar 

  42. Torki MM, Hassanajili S, Jalisi MM (2020) Design optimizations of PLA stent structure by FEM and investigating its function in a simulated plaque artery. Math Comput Simul 169:103–116

    Article  Google Scholar 

  43. Hosseinzadeh M, Ghoreishi M, Narooei K (2023) 4D printing of shape memory polylactic acid beams: An experimental investigation into FDM additive manufacturing process parameters, mathematical modeling, and optimization. J Manuf Process 85:774–782

    Article  Google Scholar 

  44. Mitra K (2008) Genetic algorithms in polymeric material production, design, processing and other applications: a review. Int Mater Rev 53:275–297

    Article  CAS  Google Scholar 

  45. Ramteke M, Gupta SK (2012) Kinetic modeling and reactor simulation and optimization of industrially important polymerization processes: a perspective. Int J Chem React Eng 9:1–56

    Google Scholar 

  46. Paul GP, Nagajyothi V (2022) Kinetic Analysis and Multi Objective Optimization of L-Lactide Polymerization. Proc 8th World Congr Mech Chem Mater Eng 2022:1–7

  47. Cvetkovic D, Parmee IC (1999) Genetic algorithm-based multi-objective optimisation and conceptual engineering design. Proc 1999 Congr Evol Comput CEC 1999 1:29–36

  48. Coello CAC, Lamont GB, Veldhuizen DAV (2007) Evolutionary Algorithms for Solving Multi-Objective Problems. Springer

    Google Scholar 

  49. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

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Acknowledgements

Geetu P Paul acknowledges that the Ministry of Human Resource Development (MHRD)-India supports a research grant through the Prime Minister’s Research Fellows (PMRF) Scheme -December 2020 cycle.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Contributions

Designed research, methodology, performed research, analyzed data, writing—original draft, writing—review and editing was done by gpp. Conceptualization, methodology, writing—review and editing, supervision, project administration was done by VN. All authors read and approved the final manuscript.

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Correspondence to Virivinti Nagajyothi.

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Paul, G.P., Nagajyothi, V. L-Lactide ring-opening polymerization: a multi-objective optimization approach through mathematical modeling. Iran Polym J 33, 815–826 (2024). https://doi.org/10.1007/s13726-024-01291-z

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