Optimized on-line control of MMA polymerization using fast multi-objective DE

References

• Chakravarthy, S.S.S.; Saraf, D.N.; Gupta, S.K. Use of genetic algorithms in the optimization of free radical polymerizations exhibiting the Trommsdorff effect. Journal of Applied Polymer Science 1997, 63, 529–548.
   Web of Science ®Google Scholar
• Garg, S.; Gupta, S.K.; Saraf, D.N. On-Line optimization of free radical bulk polymerization reactors in the presence of equipment malfunction. Journal of Applied Polymer Science 1999, 71, 2101–2120.
   Web of Science ®Google Scholar
• Sangwai, J.S.; Saraf, D.N.; Gupta, S.K. On-line optimizing control of free radical bulk polymerization of methyl methacrylate (MMA) in a Rheometer-reactor assembly. Chemical Engineering Science 2007, 62, 2790–2802.
   Web of Science ®Google Scholar
• Bhat, S.A.; Gupta, S.; Saraf, D.N.; Gupta, S.K. OOC of bulk free radical polymerization reactors under temporary loss of temperature regulation: an experimental study on a 1-liter batch reactor. Industrial & Engineering Chemistry Research 2006, 45 (22), 7530–7539.
   Web of Science ®Google Scholar
• Abbaszadeh, M.; Solgi, R. Constrained nonlinear model predictive control of a polymerization process via evolutionary optimization. Journal of Intelligent Learning Systems and Applications 2014, 6 (1), 35–44.
   Google Scholar
• Hashemi, R.; Schilling, R.; Engell, S. Optimizing control of a tubular polymerization reactor: comparison of single shooting and full discretization. IFAC Proceedings Volumes 2015, 48 (8), 556–561.
   Google Scholar
• Pontes, K.V.; Wolf, I.J.; Embirucu, M.; Marquardt, W. Dynamic real-time optimization of industrial polymerization processes with fast dynamics. Industrial & Engineering Chemistry Research 2015, 54 (47), 11881–11893.
   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 (5), 275–297.
   Web of Science ®Google Scholar
• Chakraborti, N. Critical assessment 3: the unique contributions of multi-objective evolutionary and genetic algorithms in materials research. Materials Science and Technology 2014, 30 (11), 1259–1262.
   Web of Science ®Google Scholar
• Chakraborti, N. Promise of multiobjective genetic algorithms in coating performance formulation. Surface Engineering 2014, 30 (2), 79–82.
   Web of Science ®Google Scholar
• Flegiel, F.; Sharma, S.; Rangaiah, G.P. Development and multiobjective optimization of improved cumene production processes. Materials and Manufacturing Processes 2015, 30 (4), 444–457.
   Web of Science ®Google Scholar
• Mogilicharla, A.; Majumdar, S.; Mitra, K. Multiobjective optimization of long-chain branched propylene polymerization. Polymer Engineering and Science 2015, 55 (5), 1067–1076.
   Web of Science ®Google Scholar
• Mogilicharla, A.; Mittal, P.; Majumdar, S.; Mitra, K. Kriging surrogate based multi-objective optimization of bulk vinyl acetate polymerization with branching. Materials and Manufacturing Processes 2015, 30 (4), 394–402.
   Web of Science ®Google Scholar
• Virivinti, N.; Mitra, K. A comparative study of fuzzy techniques to handle uncertainty: an industrial grinding process. Chemical Engineering and Technology 2016, 39, 1031–1039.
   Web of Science ®Google Scholar
• Mittal, P.; Kulkarni, K.; Mitra, K. A novel hybrid optimization methodology to optimize the total number and placement of wind turbines. Renewable Energy 2016, 86, 133–147.
   Web of Science ®Google Scholar
• Das, S.; Suganthan, P.N. Differential evolution: a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 2011, 15 (1), 4–31.
   Web of Science ®Google Scholar
• Tyagi, R.; Pant, M.; Negi, Y.S.; Ali, M. Optimization of the electrical performance of polymeric films. Materials and Manufacturing Processes 2015, 30 (4), 464–473.
   Web of Science ®Google Scholar
• Gujarathi, A.M.; Sadaphal, A.; Bathe, G.A. Multi-objective optimization of solid state fermentation process. Materials and Manufacturing Processes 2015, 30 (4), 511–519.
   Web of Science ®Google Scholar
• Iorio, A.W.; Li, X. Solving rotated multi-objective optimization problems using differential evolution. In Proceedings of 17th Australian Joint Conference on Artificial Intelligence. Webb, G.I. Yu, X. Eds.; Springer-Verlag: Cairns, Australia, 2004; pp. 861–872.
   Google Scholar
• Cheng, J.; Zhang, G. Multi-objective differential evolution: a recent survey. International Journal of Soft Computing with Applications 2013, 1 (1), 1–9.
   Google Scholar
• Bhat, S.A. On-line optimizing control of bulk free radical polymerization of methyl methacrylate in a batch reactor using virtual instrumentation. Ph. D. Dissertation, Indian Institute of Technology Kanpur, India, 2007.
   Google Scholar
• Deb, K. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 2000, 186 (2–4), 311–338.
   Web of Science ®Google Scholar
• Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2002, 6 (2), 182–197.
   Web of Science ®Google Scholar
• Ramteke, M.; Ghune, N.; Trivedi, V. Simulated binary jumping gene: a step towards enhancing the performance of real-coded genetic algorithm. Information Sciences 2015, 325, 429–454.
   Web of Science ®Google Scholar
• Huband, S.; Hingston, P.; Barone, L.; While, L. A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 2006, 10 (5), 477–506.
   Web of Science ®Google Scholar
• Prakash, S.; Trivedi, V.; Ramteke, M. An elitist non-dominated sorting bat algorithm NSBAT-II for multi-objective optimization of phthalic anhydride reactor. International Journal of System Assurance Engineering and Management 2016, 7 (3), 299–315.
   Web of Science ®Google Scholar