Multiobjective Optimization of Industrial Naphtha Cracker for Production of Ethylene and Propylene

REFERENCES

• Deb , K. Multi-Objective Optimization using Evolutionary Algorithms ; John Wiley & Sons Ltd. : New York , 2001 .
   Google Scholar
• Ethylene; Wikipedia , 2010 . Available online at http://en.wikipedia.org/wiki/Ethylene. Last accessed October 2010.
   Google Scholar
• Price , K. ; Storn , R. Differential evolution-a simple evolution strategy for fast optimization . Dr. Dobb's J. 1997 , 24 , 18 – 24 , 78.
   Google Scholar
• Goldberg , D.E. Genetic Algorithms in Search, Optimization, and Machine Learning ; Addison-Wesley : Reading , MA , 1989 .
   Google Scholar
• Babu , B.V. ; Chakole , P.G. ; Mubeen , J.H.S. Multi-objective differential evolution (MODE) for optimization of adiabatic styrene reactor . Chem. Eng. Sci. 2005 , 60 , 4822 – 4837 .
   Web of Science ®Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Multi-objective optimization of industrial styrene reactor: Adiabatic and pseudo-isothermal operation . Chem. Eng. Sci. 2010 , 65 , 2009 – 2026 .
   Web of Science ®Google Scholar
• Angira , R. Evolutionary computation for optimization of selected non-linear chemical processes. Ph.D. thesis, BITS-Pilani, India, 2005.
   Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Optimization of adiabatic styrene reactor: A hybrid multi-objective differential evolution (H–MODE) approach . Ind. Eng. Chem. Res. 2009 , 48 , 11115 – 11132 .
   Web of Science ®Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Hybrid multi-objective differential evolution (h-mode) for optimization of polyethylene terephthalate (PET) reactor . Int. J. Bio-insp. Compu. 2010 , 2 , 213 – 221 .
   Web of Science ®Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Improved multi-objective differential evolution (mode) approach for purified terephthalic acid oxidation process . Mater. Manuf. Processes 2009 , 24 , 303 – 309 .
   Web of Science ®Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Multi-objective optimization of industrial processes using elitist multi-objective differential evolution . Mater. Manuf. Processes 2011 , 26 , 455 – 463 .
   Web of Science ®Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Multi-objective differential evolution algorithm for multi-objective optimization of industrial styrene reactor . App. Comp. Intel. Soft. Comp. 2011 , 11 , 463 – 468 .
   Google Scholar
• Kumar , P. ; Kunzru , D. Modelling of naphtha pyrolysis . Ind. Eng. Chem. Process Des. Dev. 1985 , 24 , 774 – 782 .
   Web of Science ®Google Scholar
• Othmer , K. Ethylene . In: Kirk-Othmer Encyclopedia of Chemical Technology ; Ed. 5 . Vol. 10 , John Wiley & Sons : New Jersey , 2007 .
   Google Scholar
• Kumar , P. ; Kunzru , D. Kinetics of coke deposition in naphtha pyrolysis . Can. J. Chem. Eng. 1985 , 63 , 598 – 604 .
   Web of Science ®Google Scholar
• Hirato , M. ; Yoshioka , S. Simulation of the pyrolysis of naphtha, kerosene and gas oil with a tubular reactor . Int. Chem. Eng. 1973 , 13 , 347 – 355 .
   Google Scholar
• Sundaram , K.M. ; Froment , G.F. Modeling of thermal cracking kinetics. 3. Radical mechanisms for the pyrolysis of simple paraffins, olefins, and their mixtures . Ind. Eng. Chem. Fundam. 1978 , 17 , 174 – 182 .
   Web of Science ®Google Scholar
• Van Damme , P.S. ; Froment , G.F. ; Balthasar , W.B. Scaling up of naphtha cracking coils . Ind. Eng. Chem. Process. Des. Dev. 1981 , 20 , 366 – 376 .
   Google Scholar
• Biswas , A. ; Chakraborti , N. ; Sen , P.K. Multiobjective optimization of manganese recovery from sea nodules using genetic algorithms . Mater. Manuf. Process 2009 , 24 , 22 – 30 .
   Web of Science ®Google Scholar
• Pettersson , F. ; Biswas , A. ; Sen , P.K. ; Saxén , H. ; Chakraborti , N. Analyzing leaching data for low-grade manganese ore using neural nets and multiobjective genetic algorithms . Mater. Manuf. Process. 2009 , 24 , 320 – 330 .
   Web of Science ®Google Scholar
• Deb , K. ; Mitra , K. ; Dewri , R. ; Majumdar , S. Towards a better understanding of the epoxy-polymerization process using multi-objective evolutionary computation . Chem. Eng. Sci. 2004 , 59 , 4261 – 4277 .
   Web of Science ®Google Scholar
• Sharma , S. ; Chua , Y.C. ; Rangaiah , G.P. Economic and environmental criteria and trade-offs for recovery processes . Mater. Manuf. Process. 2011 , 26 , 431 – 445 .
   Web of Science ®Google Scholar
• Mitra , K. Handling uncertainty in kinetic parameters in optimal operation of a polymerization reactor . Mater. Manuf. Process. 2011 , 26 , 446 – 454 .
   Web of Science ®Google Scholar
• Li , Y. ; Yu-Hsiang , T. Hybrid differential evolution and particle swarm optimization approach to surface-potential-based model parameter extraction for nanoscale MOSFETs . Mater. Manuf. Process. 2011 , 26 , 388 – 397 .
   Web of Science ®Google Scholar
• Chiou , J.P. ; Wang , F.S. Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed batch fermentation process . Comput. Chem. Eng. 1999 , 23 , 1277 – 1299 .
   Web of Science ®Google Scholar