Polypropylene Production Optimization in Fluidized Bed Catalytic Reactor (FBCR): Statistical Modeling and Pilot Scale Experimental Validation

Propylene is one type of plastic that is widely used in our everyday life. This study focuses on the identification and justification of the optimum process parameters for polypropylene production in a novel pilot plant based fluidized bed reactor. This first-of-its-kind statistical modeling with experimental validation for the process parameters of polypropylene production was conducted by applying ANNOVA (Analysis of variance) method to Response Surface Methodology (RSM). Three important process variables i.e., reaction temperature, system pressure and hydrogen percentage were considered as the important input factors for the polypropylene production in the analysis performed. In order to examine the effect of process parameters and their interactions, the ANOVA method was utilized among a range of other statistical diagnostic tools such as the correlation between actual and predicted values, the residuals and predicted response, outlier t plot, 3D response surface and contour analysis plots. The statistical analysis showed that the proposed quadratic model had a good fit with the experimental results. At optimum conditions with temperature of 75°C, system pressure of 25 bar and hydrogen percentage of 2%, the highest polypropylene production obtained is 5.82% per pass. Hence it is concluded that the developed experimental design and proposed model can be successfully employed with over a 95% confidence level for optimum polypropylene production in a fluidized bed catalytic reactor (FBCR).

[1]  Atul Kumar,et al.  Application of Response Surface Methodology for Methylene Blue dye removal from aqueous solution using low cost adsorbent , 2012 .

[2]  D. Bikiaris.  Microstructure and Properties of Polypropylene/Carbon Nanotube Nanocomposites , 2010, Materials.

[3]  T. Gries,et al.  Spinnability and Characteristics of Polyvinylidene Fluoride (PVDF)-based Bicomponent Fibers with a Carbon Nanotube (CNT) Modified Polypropylene Core for Piezoelectric Applications , 2013, Materials.

[4]  J. Soares,et al.  Characterization of the combined molecular weight and composition distribution of industrial ethylene/α-olefin copolymers , 2001 .

[5]  P. P. Kundu,et al.  Optimisation of physical and mechanical properties of rubber compounds by response surface methodology––Two component modelling using vegetable oil and carbon black , 2002 .

[6]  Hydrodynamics of three-phase fluidization of a homogeneous ternary mixture of regular particles—Experimental and statistical analysis , 2013 .

[7]  M. Hussain,et al.  Modified mathematical model for gas phase olefin polymerization in fluidized-bed catalytic reactor , 2009 .

[8]  Ying Liu,et al.  An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes , 2010, Comput. Chem. Eng..

[9]  Guo Chen,et al.  Application of response surface methodology for optimization of the synthesis of synthetic rutile from titania slag , 2012 .

[10]  M. Hussain,et al.  CFD simulation of fluidized bed reactors for polyolefin production – A review , 2014 .

[11]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[12]  P. John.  Statistical Design and Analysis of Experiments , 1971 .

[13]  De-Pan Shi,et al.  Steady-state and dynamic modeling of commercial bulk polypropylene process of Hypol technology , 2009 .

[14]  F. K. Hymore,et al.  Hydrogen peroxide and lime based oxidative pretreatment of wood waste to enhance enzymatic hydrolysis for a biorefinery: Process parameters optimization using response surface methodology , 2013 .

[15]  Reza Zarghami,et al.  Dynamic characteristics of bubbling fluidization through recurrence rate analysis of pressure fluctuations , 2013 .

[16]  Shaw Voon Wong,et al.  A new motorcycle helmet liner material: The finite element simulation and design of experiment optimization , 2007 .

[17]  Diego Martinez Prata,et al.  Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor , 2010 .

[18]  Thomas J. Harris,et al.  A comparison of two-phase and well-mixed models for fluidized-bed polyethylene reactors , 1994 .

[20]  H. Karian,et al.  Handbook Of Polypropylene And Polypropylene Composites , 1999 .

[21]  Kamal M. Sassi,et al.  MINLP based superstructure optimization for boron removal during desalination by reverse osmosis , 2013 .

[22]  V. Ranade,et al.  A computational model for predicting particle size distribution and performance of fluidized bed polypropylene reactor , 2004 .

[23]  Katarina M. Rajković,et al.  Optimization of ultrasound-assisted base-catalyzed methanolysis of sunflower oil using response surface and artifical neural network methodologies , 2013 .

[24]  A. A. Jalil,et al.  Ir/Pt-HZSM5 for n-pentane isomerization: Effect of Si/Al ratio and reaction optimization by response surface methodology , 2013 .

[25]  P. Galli,et al.  Polyolefins: The most promising large‐volume materials for the 21st century , 2004 .

[26]  Zhou Tian,et al.  A model for the structures of impact polypropylene copolymers produced by an atmosphere-switching polymerization process , 2013 .

[27]  U. Giannini,et al.  The influence of hydrogen on the polymerization of propylene and ethylene with an MgCl2 supported catalyst , 1983 .

[28]  Lian X. Liu,et al.  Dynamic model development for residence time distribution control in high-impact polypropylene copolymer process , 1999 .

[29]  I. Mujtaba,et al.  Improving fuel quality by whole crude oil hydrotreating: A kinetic model for hydrodeasphaltenization in a trickle bed reactor , 2012 .

[30]  Navid Mostoufi,et al.  Improved single phase modeling of propylene polymerization in a fluidized bed reactor , 2012, Comput. Chem. Eng..

[31]  F. Lesage,et al.  Dynamic Modelling of a Polypropylene Production Plant , 2012 .

[32]  J. Velasco,et al.  Fracture Toughness of Polypropylene-Based Particulate Composites , 2009, Materials.

[33]  M. Abbasi,et al.  Modeling and optimizing of electrochemical oxidation of C.I. Reactive Orange 7 on the Ti/Sb–SnO2 as anode via response surface methodology , 2013 .

[34]  N. Drouiche,et al.  Study of heavy metal removal from heavy metal mixture using the CCD method , 2014 .

[35]  María G. Villarreal-Marroquín,et al.  Optimisation via simulation: a metamodelling-based method and a case study , 2013 .

[36]  Stephan Freytag,et al.  Statistical Design And Analysis Of Experiments With Applications To Engineering And Science , 2016 .

[37]  F. Mjalli,et al.  Dynamic modeling of gas phase propylene homopolymerization in fluidized bed reactors , 2011 .

[38]  David W. Bacon,et al.  Gas Phase Ethylene Polymerization: Production Processes, Polymer Properties, and Reactor Modeling , 1994 .

[39]  B. Bowerman.  Statistical Design and Analysis of Experiments, with Applications to Engineering and Science , 1989 .

[40]  H. Jang,et al.  Mixing–segregation phenomena of binary system in a fluidized bed , 2010 .

[41]  J. Soares,et al.  Effect of Hydrogen, Electron Donor, and Polymerization Temperature on Poly(propylene) Microstructure , 2012 .

[42]  I. Boyaci,et al.  Optimization of lactose utilization in deproteinated whey by Kluyveromyces marxianus using response surface methodology (RSM). , 2006, Bioresource technology.

[43]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[44]  S. Rostami,et al.  Multicomponent polymer systems , 1992 .

[45]  Yong Li,et al.  Numerical studies of bubble formation dynamics in gas-liquid-solid fluidization at high pressures , 2001 .

[46]  I. Hussein,et al.  Effects of branching characteristics and copolymer composition distribution on non-isothermal crystallization kinetics of metallocene LLDPEs , 2007 .

[47]  Jose M. Framinan,et al.  A response surface methodology for parameter setting in a dynamic Conwip production control system , 2011, Int. J. Manuf. Technol. Manag..

[48]  F. Bamoharram,et al.  Comparison of RSM and ANN for the investigation of linear alkylbenzene synthesis over H14[NaP5W30O110]/SiO2 catalyst , 2013 .