Operation and Design Optimisation of Industrial Low-Density Polyethylene Tubular Reactor for Multiple Objectives Using an Evolutionary Algorithm-Based Strategy

Springer Science and Business Media LLC - Tập 7 - Trang 655-672 - 2023
F. S. Rohman1, D. Muhammad1,2, K. A. Zahan3,4, M. N. Murat1
1School of Chemical Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia
2Chemical Engineering Department, Faculty of Chemical and Energy Engineering, Universiti Tekniligi Malaysia, Johor, Malaysia
3Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, Japan
4Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

Tóm tắt

Multi-objective optimisation (MOO) of a low-density polyethylene (LDPE) production in a tubular reactor is performed for two problems with three different objectives: maximisation of monomer conversion and minimisation of operating cost for problem 1; maximisation of productivity and minimisation of operating cost for problem 2. As a precaution against a run-away in the tubular reactor, an inequality constraint for the reactor temperature is also imposed. The multi-objective evolutionary optimisation algorithms (MOEA), namely the Pareto envelope-based selection algorithm II (PESA-II), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the strength Pareto evolutionary algorithm II (SPEA-II), are used to execute the optimisation problem with the Aspen simulator as a model-based optimisation for LDPE in a tubular reactor. Prior to that, model validation and a variables selection methodology based on the Pearson correlation coefficient (PCC) are devised for the selection of the appropriate decision variables for the MOO. The final inputs for MOO’s decision variables are the jacket flowrate of zone 5, initiator 2, and the length of zone 5. Performance matrices including hyper volume, spacing, and pure diversity are employed to select the most effective MOEA method. Based on the results of the comparison study, the most effective MOO strategies were SPEA-II for problem 1 and MOEA/D for problem 2. This is due to the fact that the discovered solution set provided the most precise, diverse, and appropriate in homogeneity allocation points along the Pareto front (PF).

Tài liệu tham khảo

Agrawal N, Rangaiah GP, Ray AK, Gupta SK (2006) 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 45:3182–3199

Agrawal N (2008) Modeling, Simulation and Multi-Objective Optimization of an Industrial, Low-Density Polyethylene Reactor. National University of Singapore, PhD Thesis

Al-Malah K (2017) Aspen Plus®: chemical engineering applications. John Wiley & Sons Inc, Hoboken, New Jersey

Asteasuain M, Tonelli SM, Brandolin A, Bandoni JA (2001a) Dynamic simulation and optimisation of tubular polymerisation reactors in gPROMS. Comput Chem Eng 25:509–515

Asteasuain M, Tonelli SM, Brandolin A, Bandoni JA (2001b) Dynamic simulation and optimization of tubular polymerization reactors in gPROMS. Comput Chem Eng 25:509–515

Asteasuain M, Brandolin A (2008) Modeling and optimization of a high-pressure ethylene polymerization reactor using gPROMS. Comput Chem Eng 32:396–408

Azmi A, Aziz N (2016) Low density polyethylene tubular reactor modeling: overview of the model developments and future directions. Int J Appl Eng Res 11:9906–9913

Azmi A, Sata SA, Rohman FS, Aziz N (2019) Melt flow index of low-density polyethylene determination based on molecular weight and branching properties. J Phys: Conf Ser 1349:012094

Azmi A, Rohman FS, Idris I, Zainol MM (2022) Sensitivity study of input parameters in the industrial low density polyethylene tubular reactor. Mater Today: Proc 63:S195–S202

Azmi A, Sata SA, Rohman FS, Aziz N (2020a) Optimization studies of low-density polyethylene process: effect of different interval numbers. Chemical Product and Process Modeling 15(4):20190125. https://doi.org/10.1515/cppm-2019-0125

Azmi A, Sata SA, Rohman FS, Aziz,N (2020b) Dynamic optimization of low-density polyethylene production in tubular reactor under thermal safety constraint. Chemical Industry and Chemical Engineering Quarterly 27(1):85–97. https://doi.org/10.2298/CICEQ190108027A

Bezzo F, Bernardi R, Cremonese G, Finco M, Barolo M (2004) Using process simulators for steady-state and dynamic plant analysis: an industrial case study. Chem Eng Res Des 82:499–512

Bokis CP, Ramanathan S, Franjione J, Buchelli A, Call ML, Brown AL (2002) Physical properties, reactor modeling, and polymerization kinetics in the low-density polyethylene tubular reactor process. Ind Eng Chem Res 41:1017–1030

Boopathy MBM (2006) A comprehensive dynamic model for high-pressure tubular low-density polyethylene (LDPE) reactors. Iowa State University

Brandolin A, Lacunza MH, Ugrin PE, Capiati NJ (1996) High pressure polymerization of ethylene. An improved mathematical model for industrial tubular reactors. Polym React Eng 4:193–241

Burdett ID, Eisinger RS (2017) Ethylene polymerization processes and manufacture of polyethylene. John Wiley & Sons, Inc. , pp 61–103

Cao L, Li D, Zhang C, Wu H (2007) Control and modeling of temperature distribution in a tubular polymerization process. Comput Chem Eng 31:1516–1524

Chen CC (2002) An industry perspective on polymer process modeling. CAST Communications

Cioffi M, Hoffmann AC, Janssen LPBM (2001) Reducing the gel effect in free radical polymerization. Chem Eng Sci 56:911–915

Corne DW, Knowles JD, Oates MJ (2000) The Pareto envelope-based selection algorithm for multiobjective optimization". PPSN 2000: Parallel Problem Solving from Nature PPSN VI. France, Paris, pp 839–848

Curreri F, Graziani S, Xibilia MG (2020) Input selection methods for data-driven Soft sensors design: application to an industrial process. Inf Sci 537:1–17

Dhib R, Al-Nidawy N (2002) Modelling of free radical polymerisation of ethylene using difunctional initiators. Chem Eng Sci 57:2735–2746

Duchateau J, Castañeda-Zúñiga D, Neuteboom P, Toloza C, Tacx J, Reynolds A, and Allemand C (2019) Chapter 7.9: SABIC high-pressure process for LDPE: CTR™ technology. In: Meyers RA (ed) Handbook of Petrochemicals Production Processes (2nd ed) McGraw Hill Professional: Access. Engineering. https://www.accessengineeringlibrary.com/content/book/9781259643132/chapter/chapter54

Erdeghem PMMV, Logist F, Heughebaert M, Dittrich C and Impe JFV (2010) Conceptual modelling and optimization of jacketed tubular reactors for the production of LDPE. Proceedings of the 9th International Symposium on Dynamics and Control of Process Systems (DYCOPS 2010) 43(5):433–438

Gujarathi AM, Babu BV (2013) Multi-objective optimization of low density polyethylene (LDPE) tubular reactor using strategies of differential evolution. Handbook of Optimization. Springer, Berlin Heidelberg, pp 615–639

Heris MK (2015) Pareto envelope-based selection algorithm II (PESA-II), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and strength Pareto evolutionary algorithm 2 (SPEA2) in MATLAB, https://yarpiz.com/category/multiobjective-optimization

Ibrahim A (2017) Toward enhancement of evolutionary multi- and many-objective optimization: algorithms, performance metrics, and visualization techniques. University of Ontario Institute of Technology, Thesis

Jayaweera CD, Aziz N (2018) Reliability of principal component analysis and Pearson correlation coefficient, for application in artificial neural network model development, for water treatment plants. In IOP Conf Series: Mater Sci Eng 458:012076

Kiparissides C, Mavridis H (1986) Mathematical Modelling and Sensitivity Analysis of High Pressure Polyethylene Reactors. In: de Lasa HI (ed) Chemical Reactor Design and Technology. NATO ASI Series, vol 110. Springer, Dordrecht, pp759–777. https://doi.org/10.1007/978-94-009-4400-8_21

Krallis A, Pladis P, Kanellopoulos V, Kiparissides C (2010) Development of advanced software tools for computer-aided design, simulation, and optimization of polymerization processes. Macromol React Eng 4:303–318

May R, Dandy G, Maier H (2011) Review of input variable selection methods for artificial neural networks. Artif Neural Networks-Methodol Adv Biomed Appl 10:16004

Muhammad D, Ahmad Z, Aziz N (2018) Modeling and nonlinearity studies of low density polyethylene (LDPE) tubular reactor. Mater Today: Proc 5(10):21612–21619

Muhammad D, Rohman FS, Ahmad Z, Aziz N (2021) Low-density polyethylene tubular reactor control using neural Wiener model predictive control. Asia-Pacific J Chem Eng 16:e2699

Nogueira I, Fontes C, Sartori I, Pontes K, Embiruçu M (2017) A model-based approach to quality monitoring of a polymerization process without online measurement of product specifications. Comput Ind Eng 106:123–136

Patel RM (2017) Types and basics of polyethylene. In: Spalding MA, Chatterjee AM (eds) Handbook of Industrial Polyethylene and Technology, pp 105–138.

Peikert P, Pflug KM, Busch M (2019) Modeling of high-pressure ethene homo-and copolymerization. Chem Ing Tec 19:673–677

Rangaiah GP (2009) Multi-objective optimization: techniques and applications in chemical engineering. Advances in Process Systems Engineering Vol 1

Rohman FS, MuhammadSudibyo Murat DMN, Azmi A (2022) Application of feed forward neural network for fouling thickness estimation in low density polyethylene tubular reactor. Materials Today: Proceedings 63:S95–S100

Rohman FS, Zahan KA, Aziz N (2022) Dynamic multi-objective optimization of autocatalytic esterification in a semi-batch reactor. Chem Eng Technol 45:1795–1802

Saldívar-Guerra E, Ordaz-Quintero A, Infante-Martínez R, Herrera-Ordóñez J, Villarreal-Cárdenas L, Ramírez-Wong D, Rivera-Rodríguez E, Flores-Flores R, Miramontes-Vidal L (2016) Some factors affecting the molecular weight distribution (MWD) in low density polyethylene multizone autoclave polymerization reactors. Macromol React Eng 10:123–139

Shenoy AV, Saini DR (1986) Melt flow index: more than just a quality control rheological parameter. Part i Adv Polymer Technol 6:1–58

Stoiljkovic D, Jovanović S (2019) Compression, supramolecular organization and free radical polymerization of ethylene gas. Polyolefins Journal 6:23–41

Vallerio M, Logist F, Van Erdeghem P, Dittrich C, Van Impe J (2013) Model-based optimization of the cooling system of an industrial tubular LDPE reactor. Ind Eng Chem Res 52:1656–1666

Wang H, Jin Y, Yao X (2017) Diversity assessment in many-objective optimization. IEEE Transactions on Cybernetics 47:323–329

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

Zhang Q, Li H (2007) MOEA/D: A Multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11:712–731

Zhang S, Li S, Harley RG, Habetler TG (2017) Performance evaluation and comparison of multi-objective optimization algorithms for the analytical design of switched reluctance machines. CES Trans Electr Mach Syst 1:58–65

Zitzler E, Laumanns M, and L Thiele L (2001) SPEA-II: Improving the strength pareto evolutionary algorithm, Evolutionary Methods for Design, Optimisation and Control, 1–6. https://doi.org/10.3929/ETHZ-A-004284029

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