Multiobjective Optimization of Industrial Processes Using Elitist Multiobjective Differential Evolution (Elitist-MODE)

REFERENCES

• Back , T. Evolutionary Algorithms in Theory and Practice ; Oxford University Press : New York , 1996 .
   Google Scholar
• Miettinen , K.M. Nonlinear Multiobjective Optimization ; Kluwer Academic Publishers : Boston , MA , 1999 .
   Google Scholar
• Deb , K. Multiobjective Optimization Using Evolutionary Algorithms ; John Wiley and Sons Ltd. : New York , 2001.
   Google Scholar
• Abbass , H.A. ; Sarker , R. ; Newton , C. PDE: A Pareto-frontier differential evolution approach for multiobjective optimization problems. In Proceedings of the 2001 Congress on Evolutionary Computation (CEC'2001) 2001, 971–978.
   Google Scholar
• Onwubolu , G.C. ; Babu , B.V. New Optimization Techniques in Engineering ; Springer-Verlag : Heidelberg , Germany , 2004 .
   Google Scholar
• Eiben , A.E. ; Smith , J.E. Introduction to Evolutionary Computing ; Springer-Verlag : Berlin , Heidelberg , 2003 .
   Google Scholar
• Coello , C.A.C. ; Lamont , G.B. Applications of Multiobjective Evolutionary Algorithm: Advances in Natural Computation; World Scientific Publishing Co. Pte. Ltd.: Singapore, 2004, Vol. 1.
   Google Scholar
• Tan , K.C. ; Khor , E.F. ; Lee , T.H. Multiobjective Evolutionary Algorithms and Applications ; Springer-Verlag : London , 2005 .
   Google Scholar
• Biswas , A. ; Chakraborti , N. ; Sen , P.K. A genetic algorithms based multi-objective optimization approach applied to a hydrometallurgical circuit for ocean nodules . Mineral Processing and Extractive Metallurgy Review 2009 , 30 , 163 – 189 .
   Web of Science ®Google Scholar
• Biswas , A. ; Chakraborti , N. ; Sen , P.K. Multiobjective optimization of manganese recovery from sea nodules using genetic algorithms . Materials and Manufacturing Processes 2009 , 24 , 22 – 30 .
   Web of Science ®Google Scholar
• Ramteke , M. ; Gupta , S.K. Biomimetic adaptations of genetic algorithms and simulated annealing for the robust multi-objective optimization of an industrial Nylon-6 reactor . Materials and Manufacturing Processes 2009 , 24 , 38 – 46 .
   Web of Science ®Google Scholar
• Mitra , K. Genetic algorithms in polymeric material production, design, processing and other applications: a review . International Materials Reviews 2008 , 53 , 275 – 297 .
   Web of Science ®Google Scholar
• Coello , C.A.A. ; Ricardo , L.B. Evolutionary multi-objective optimization in materials science and engineering . Materials and Manufacturing Processes 2009 , 24 , 119 – 129 .
   Web of Science ®Google Scholar
• Wojciech , P. Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields . Materials and Manufacturing Processes 2009 , 24 , 174 – 197 .
   Google Scholar
• Chakraborti , N. Genetic algorithms in materials design and processing . International Materials Reviews 2004 , 49 , 246 – 260 .
   Web of Science ®Google Scholar
• Poloni , C. ; Giurgevich , A. ; Onesti , L. ; Pediroda , V. Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics . Computational Methods in Applied Mechanical Engineering 2000 , 186 , 403 – 420 .
   Web of Science ®Google Scholar
• Tanaka , M. GA-based decision support system for multi-criterion optimization, In Proceedings of the International Conference on Systems, Man and Cybernetics 1995, Vol. 2, 1556–1561.
   Google Scholar
• Price , K.V. ; Storn , R. Differential evolution – a simple evolution strategy for fast optimization . Dr. Dobb's Journal 1997 , 22 , 18 – 22 .
   Google Scholar
• Babu , B.V. Process Plant Simulation ; Oxford Press : New York , 2004 .
   Google Scholar
• Babu , B.V. ; Angira , R. Optimal design of an auto-thermal ammonia synthesis reactor . Computers and Chemical Engineering 2005 , 29 , 1041 – 1045 .
   Web of Science ®Google Scholar
• Babu , B.V. ; Angira , R. Modified differential evolution (MDE) for optimization of non-linear chemical processes . Computers and Chemical Engineering 2006 , 30 , 989 – 1002 .
   Web of Science ®Google Scholar
• Goldberg , D.E. Genetic Algorithms in Search, Optimization and Machine Learning ; Addission-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 . Chemical Engineering Science 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 . Chemical Engineering Science 2010 , 65 , 2009 – 2026 .
   Web of Science ®Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Optimization of adiabatic styrene reactor: A hybrid multi-objective differential evolution (H-MODE) approach . Industrial and Engineering Chemistry Research 2009 , 48 , 11115 – 11132 .
   Web of Science ®Google Scholar
• Babu , B.V. ; Gujarathi , A.M. Elitist-multi-objective differential evolution (E-MODE) algorithm for multi-objective optimization. Proceedings of 3rd Indian International Conference on Artificial Intelligence (IICAI-2007), Pune 2007, 2007; 441–456.
   Google Scholar
• Gujarathi , A.M. ; Babu , B.V. Improved multi-objective differential evolution (MODE) approach for purified terephthalic acid (PTA) oxidation process . Materials and Manufacturing Processes 2009 , 24 , 303 – 319 .
   Web of Science ®Google Scholar
• Sheel , J.G.P. ; Crowe , C.M. Simulation and optimization of an existing ethyl benzene dehydrogenation reactor . Canadian Journal of Chemical Engineering 1969 , 47 , 183 – 187 .
   Google Scholar
• Clough , D.E. ; Ramirez , W.F. Mathematical modeling and optimization of the dehydrogenation of ethyl benzene to form styrene . American Institute of Chemical Engineering Journal 1976 , 22 , 1097 – 1105 .
   Google Scholar
• Elnashaie , S.S.E.H. ; Abdalla , B.K. ; Hughes , R. Simulation of the industrial fixed bed catalytic reactor for the dehydrogenation of ethyl benzene to styrene: heterogeneous dusty gas model . Industrial and Engineering Chemistry Research 1993 , 32 , 2537 – 2541 .
   Google Scholar
• Abdalla , B.K. ; Elnashaie , S.S.E.H. ; Alkhowaiter , S. ; Elshishini , S.S. Intrinsic kinetics and industrial reactors modeling for the dehydrogenation of ethyl benzene to styrene on promoted iron oxide catalysts . Applied Catalysis A: General 1994 , 113 , 89 – 102 .
   Web of Science ®Google Scholar
• Yee , A.K.Y. ; Ray , A.K. ; Rangiah , G.P. Multi-objective optimization of industrial styrene reactor . Computers and Chemical Engineering 2003 , 27 , 111 – 130 .
   Google Scholar
• ICIS., Home Page of ICIS. (2009). http://www.icis.com/v2/chemicals/9076423/polyethylene-terephthalate.html
   Google Scholar
• Laubriet , C. ; LeCorre , B. ; Choi , K.Y. Two-phase model for continuous final stage melt poly-condensation of poly ethylene terephthalate: 1 . steady-state analysis. Industrial and Engineering Chemistry Research 1991 , 30 , 2 – 12 .
   Google Scholar
• Bhaskar , V. ; Gupta , S.K. ; Ray , A.K. Multi-objective optimization of an industrial wiped film poly(ethylene terephthalate) reactor: some further insights . Computers & Chemical Engineering 2001 , 25 , 391 – 407 .
   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 . International Journal of Bio-Inspired Computation 2010 , 2 , 213 – 221 .
   Web of Science ®Google Scholar
• Babu , B.V. ; Mubeen , J.H.S. ; Chakole , P.G. Modeling, simulation, and optimization of wiped film poly ethylene terephthalate (PET) reactor using multi-objective differential evolution (MODE) . Materials and Manufacturing Processes 2007 , 22 , 541 – 552 .
   Web of Science ®Google Scholar