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Metaheuristics in Process Engineering: A Historical Perspective

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Applications of Metaheuristics in Process Engineering

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Abstract

This chapter presents an overview of applications of metaheuristics to solve different real-world chemical process engineering problems over the last 30 years. The first part of this chapter describes some fundamental characteristics of metaheuristics, a class of global stochastic methods and also provides the standard description of some of the most widely used metaheuristics such as simulated annealing, tabu search, genetic algorithms, and ant colony optimization (ACO). In the second part, different practical applications of these metaheuristics related to chemical process industry are covered such as heat exchanger networks (HENs), short-term scheduling of batch processes, dynamic optimization of chemical and biochemical processes, parameter estimation, and multiobjective optimization with extensive list of references.

Whenever you’re called on to make up your mind, and you’re hampered by not having any, the best way to solve the dilemma, you’ll find, is simply by spinning a penny. No-not so that chance shall decide the affair while you’re passively standing there moping; but the moment the penny is up in the air, you suddenly know what you’re hoping.

— “A Psychological Tip” in Grooks by Piet Hein (1982)

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References

  1. Aarts, E.H.L., Korst, J.H.M., van Laarhoven, P.J.M.: Simulated annealing. In: Aarts, E.H.L., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization, pp. 91–120. Wiley-Interscience, Chichester (1997)

    Google Scholar 

  2. Aghalayam, P., Park, Y.K., Vlachos, D.G.: Construction and optimization of complex surface-reaction mechanisms. AIChE J. 46(10), 2017–2029 (2000)

    Google Scholar 

  3. Ahmad, M.I., Zhang, N., Jobson, M., Chen, L.: Multi-period design of heat exchanger networks. Chem. Eng. Res. Des. 90(11), 1883–1895 (2012)

    Google Scholar 

  4. Alberton, A.L., Schwaab, M., Biscaia Jr., E.C., Pinto, J.C.: Sequential experimental design based on multiobjective optimization procedures. Chem. Eng. Sci. 65(20), 5482–5494 (2010)

    Google Scholar 

  5. Allen, B., Savard-Goguen, M., Gosselin, L.: Optimizing heat exchanger networks with genetic algorithms for designing each heat exchanger including condensers. Appl. Therm. Eng. 29(16), 3437–3444 (2009)

    Google Scholar 

  6. Altinten, A.: Generalized predictive control applied to a pH neutralization process. Comput. Chem. Eng. 31(10), 1199–1204 (2007)

    Google Scholar 

  7. Anderson, S., Kadirkamanathan, V., Chipperfield, A., Sharifi, V., Swithenbank, J.: Multi-objective optimization of operational variables in a waste incineration plant. Comput. Chem. Eng. 29(5), 1121–1130 (2005)

    Google Scholar 

  8. Androulakis, I.P., Venkatasubramanian, V.: A genetic algorithmic framework for process design and optimization. Comput. Chem. Eng. 15(4), 217–228 (1991)

    Google Scholar 

  9. Angira, R., Babu, B.V.: Optimization of process synthesis and design problems: A modified differential evolution approach. Chem. Eng. Sci. 61(14), 4707–4721 (2006)

    Google Scholar 

  10. Aras, O., Bayramoglu, M., Hasiloglu, A.S.: Optimization of scaled parameters and setting minimum rule base for a fuzzy controller in a lab-scale pH process. Ind. Eng. Chem. Res. 50(6), 3335–3344 (2011)

    Google Scholar 

  11. Athier, G., Floquet, P., Pibouleau, L., Domenech, S.: Optimization of heat exchanger networks by coupled simulated annealing and NLP procedures. Comput. Chem. Eng. 20(Suppl. 1), S13–S18 (1996)

    Google Scholar 

  12. Athier, G., Floquet, P., Pibouleau, L., Domenech, S.: Process optimization by simulated annealing and NLP procedures. Application to heat exchanger network synthesis. Comput. Chem. Eng. 21(Suppl. 1), S475–S480 (1997)

    Google Scholar 

  13. Athier, G., Floquet, P., Pibouleau, L., Domenech, S.: Synthesis of heat-exchanger network by simulated annealing and NLP procedures. AIChE J. 43(11), 3007–3020 (1997)

    Google Scholar 

  14. Babu, B.V., Sastry, K.K.N.: Estimation of heat transfer parameters in a trickle-bed reactor using differential evolution and orthogonal collocation. Comput. Chem. Eng. 23(3), 327–339 (1999)

    Google Scholar 

  15. Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  16. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. IOP Publishing Ltd., Bristol (1997)

    MATH  Google Scholar 

  17. Balasubramanian, P., Bettina, S.J., Pushpavanam, S., Balaraman, K.S.: Kinetic parameter estimation in hydrocracking using a combination of genetic algorithm and sequential quadratic programming. Ind. Eng. Chem. Res. 42(20), 4723–4731 (2003)

    Google Scholar 

  18. Banzhaf, W., Francone, F.D., Keller, R.E., Nordin, P.: Genetic programming: An introduction. On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, San Francisco (1998)

    MATH  Google Scholar 

  19. Barreto, A., Rodriguez-Donis, I., Gerbaud, V., Joulia, X.: Optimization of heterogeneous batch extractive distillation. Ind. Eng. Chem. Res. 50(9), 5204–5217 (2011)

    Google Scholar 

  20. Battiti, R., Protasi, M.: Reactive search, a history-based heuristic for MAX-SAT. ACM J. Exp. Algorithmics 2 (1996)

    Google Scholar 

  21. Beghi, A., Cecchinato, L., Cosi, G., Rampazzo, M.: A pso-based algorithm for optimal multiple chiller systems operation. Appl. Therm. Eng. 32, 31–40 (2012)

    Google Scholar 

  22. Behroozsarand, A., Ebrahimi, H., Zamaniyan, A.: Multiobjective optimization of industrial autothermal reformer for syngas production using nonsorting genetic algorithm II. Ind. Eng. Chem. Res. 48(16), 7529–7539 (2009)

    Google Scholar 

  23. Bernal-Haro, L., Azzaro-Pantel, C., Pibouleau, L., Domenech, S.: Multiobjective batch plant design: A two-stage methodology, 2. Development of a genetic algorithm and result analysis. Ind. Eng. Chem. Res. 41(23), 5743–5758 (2002)

    Google Scholar 

  24. Bhaskar, V., Gupta, S.K., Ray, A.K.: Multiobjective optimization of an industrial wiped-film pet reactor. AIChE J. 46(5), 1046–1058 (2000)

    Google Scholar 

  25. Bhat, S.A., Huang, B.: Preferential crystallization: Multi-objective optimization framework. AIChE J. 55(2), 383–395 (2009)

    Google Scholar 

  26. Bhushan, S., Karimi, I.A.: Heuristic algorithms for scheduling an automated wet-etch station. Comput. Chem. Eng. 28(3), 363–379 (2004)

    Google Scholar 

  27. Biegler, L.T., Grossmann, I.E.: Retrospective on optimization. Comput. Chem. Eng. 28(8), 1169–1192 (2004)

    Google Scholar 

  28. Bjork, K.M., Nordman, R.: Solving large-scale retrofit heat exchanger network synthesis problems with mathematical optimization methods. Chem. Eng. Process. Process Intensif. 44(8), 869–876 (2005)

    Google Scholar 

  29. Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank based version of the ant system: A computational study. Cent. Eur. J. Oper. Res. 7, 25–38 (1999)

    MATH  MathSciNet  Google Scholar 

  30. Buzzi-Ferraris, G.: Planning of experiments and kinetic analysis. Catal. Today 52(2–3), 125–132 (1999)

    Google Scholar 

  31. Calonder, M., Bleuler, S., Zitzler, E.: Module identification from heterogeneous biological data using multiobjective evolutionary algorithms. In: Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN’06), pp. 573–582. Springer, Berlin (2006)

    Google Scholar 

  32. de Canete, J.F., del Saz-Orozco, P., Gonzalez, S., Garcia-Moral, I.: Dual composition control and soft estimation for a pilot distillation column using a neurogenetic design. Comput. Chem. Eng. 40(0), 157–170 (2012)

    Google Scholar 

  33. Cao, H., Yu, J., Kang, L., Chen, Y., Chen, Y.: The kinetic evolutionary modeling of complex systems of chemical reactions. Comput. Chem. 23(2), 143–151 (1999)

    Google Scholar 

  34. Cao, K., Feng, X., Ma, H.: Pinch multi-agent genetic algorithm for optimizing water-using networks. Comput. Chem. Eng. 31(12), 1565–1575 (2007)

    Google Scholar 

  35. Capon-Garcia, E., Bojarski, A.D., Espuna, A., Puigjaner, L.: Multiobjective evolutionary optimization of batch process scheduling under environmental and economic concerns. AIChE J. 59(2), 429–444 (2013)

    Google Scholar 

  36. Cardoso, M., Salcedo, R., de Azevedo, S., Barbosa, D.: Optimization of reactive distillation processes with simulated annealing. Chem. Eng. Sci. 55(21), 5059–5078 (2000)

    Google Scholar 

  37. Cauley, F.G., Xie, Y., Wang, N.H.L.: Optimization of SMB systems with linear adsorption isotherms by the standing wave annealing technique. Ind. Eng. Chem. Res. 43(23), 7588–7599 (2004)

    Google Scholar 

  38. Causa, J., Karer, G., Nunez, A., Saez, D., Skrjanc, I., Zupanc̄ic̄, B.: Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor. Comput. Chem. Eng. 32(12), 3254–3263 (2008)

    Google Scholar 

  39. Chakravarthy, S.S.S., Vohra, A.K., Gill, B.S.: Predictive emission monitors (pems) for NOx generation in process heaters. Comput. Chem. Eng. 23(11–12), 1649–1659 (2000)

    Google Scholar 

  40. Chaudhuri, P.D., Diwekar, U.M., Logsdon, J.S.: An automated approach for the optimal design of heat exchangers. Ind. Eng. Chem. Res. 36(9), 3685–3693 (1997)

    Google Scholar 

  41. Chen, C., Yang, B., Yuan, J., Wang, Z., Wang, L.: Establishment and solution of eight-lump kinetic model for FCC gasoline secondary reaction using particle swarm optimization. Fuel 86(15), 2325–2332 (2007)

    Google Scholar 

  42. Chen, L., Wu, L., Wang, R., Wang, Y., Zhang, S., Zhang, X.: Comparison of protein structures by multi-objective optimization. Genome Inform. 16(2), 114–24 (2005)

    Google Scholar 

  43. Chen, X., Li, Z., Yang, J., Shao, Z., Zhu, L.: Nested tabu search (TS) and sequential quadratic programming (SQP) method, combined with adaptive model reformulation for heat exchanger network synthesis (HENS). Ind. Eng. Chem. Res. 47(7), 2320–2330 (2008)

    Google Scholar 

  44. Cheng, L.H., Wu, P.C., Chen, J.: Numerical simulation and optimal design of AGMD-based hollow fiber modules for desalination. Ind. Eng. Chem. Res. 48(10), 4948–4959 (2009)

    Google Scholar 

  45. 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. 23(9), 1277–1291 (1999)

    Google Scholar 

  46. Chu, Y., Hahn, J.: Parameter set selection for estimation of nonlinear dynamic systems. AIChE J. 53(11), 2858–2870 (2007)

    Google Scholar 

  47. Coello, C., van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems, vol. 5. Kluwer Academic, Dordrecht (2002)

    MATH  Google Scholar 

  48. Cotta, C., van Hemert, J. (eds.): Recent Advances in Evolutionary Computation for Combinatorial Optimization. Springer, Berlin (2008)

    MATH  Google Scholar 

  49. Csukas, B., Lakner, R., Varga, K., Balogh, S.: Combining generated structural models with genetic programming in evolutionary synthesis. Comput. Chem. Eng. 20(Suppl. 1)(1), S61–S66 (1996)

    Google Scholar 

  50. Cui, X., Zhang, X., Zhang, Y., Feng, T.: Batch distillation in a batch stripper with a side withdrawal for purification of heat-unstable compounds. Ind. Eng. Chem. Res. 49(14), 6521–6529 (2010)

    Google Scholar 

  51. Dai, K., Wang, N.: A hybrid DNA based genetic algorithm for parameter estimation of dynamic systems. Chem. Eng. Res. Des. 90(12), 2235–2246 (2012)

    MathSciNet  Google Scholar 

  52. Dasgupta, D., Nino, F.: Immunological Computation: Theory and Applications. Auerbach, Boston (2008)

    Google Scholar 

  53. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  54. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Google Scholar 

  55. Dedieu, S., Pibouleau, L., Azzaro-Pantel, C., Domenech, S.: Design and retrofit of multiobjective batch plants via a multicriteria genetic algorithm. Comput. Chem. Eng. 27(12), 1723–1740 (2003)

    Google Scholar 

  56. Dey, F., Caflisch, A.: Fragment-based de Novo ligand design by multiobjective evolutionary optimization. J. Chem. Inf. Model. 48(3), 679–690 (2008)

    Google Scholar 

  57. Dietz, A., Azzaro-Pantel, C., Pibouleau, L., Domenech, S.: Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations. Comput. Chem. Eng. 30(4), 599–613 (2006)

    Google Scholar 

  58. Dipama, J., Teyssedou, A., Sorin, M.: Synthesis of heat exchanger networks using genetic algorithms. Appl. Therm. Eng. 28(14–15), 1763–1773 (2008)

    Google Scholar 

  59. Dolan, W.B., Cummings, P.T., Van, M.D.L.: Heat exchanger network design by simulated annealing. In: Proceedings of the First International Conference on Foundations of Computer Aided Process Operations (1987)

    Google Scholar 

  60. Dolan, W.B., Cummings, P.T., Van, M.D.L.: Process optimization via simulated annealing: Application to network design. AIChE J. 35(5), 725–736 (1989)

    Google Scholar 

  61. Doma, M.J., Taylor, P.A., Vermeer, P.J.: Closed loop identification of MPC models for MIMO processes using genetic algorithms and dithering one variable at a time: Application to an industrial distillation tower. Comput. Chem. Eng. 20(Suppl. 2)(8), S1035–S1040 (1996)

    Google Scholar 

  62. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Milan, Italy (1992) [in Italian]

    Google Scholar 

  63. Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life 5, 137–172 (1999)

    Google Scholar 

  64. Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1, 53–66 (1997)

    Google Scholar 

  65. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26, 29–41 (1996)

    Google Scholar 

  66. Dorigo, M., St utzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Google Scholar 

  67. van Dyk, B., Nieuwoudt, I.: Design of solvents for extractive distillation. Ind. Eng. Chem. Res. 39(5), 1423–1429 (2000)

    Google Scholar 

  68. Eftaxias, A., Font, J., Fortuny, A., Fabregat, A., Stüber, F.: Nonlinear kinetic parameter estimation using simulated annealing. Comput. Chem. Eng. 26(12), 1725–1733 (2002)

    Google Scholar 

  69. Egea, J.A., Balsa-Canto, E., Garcia, M.S.G., Banga, J.R.: Dynamic optimization of nonlinear processes with an enhanced scatter search method. Ind. Eng. Chem. Res. 48(9), 4388–4401 (2009)

    Google Scholar 

  70. El-Halwagi, M.M., Manousiouthakis, V.: Synthesis of mass exchange networks. AIChE J. 35(8), 1233–1244 (1989)

    Google Scholar 

  71. Faber, R., Jockenhövel, T., Tsatsaronis, G.: Dynamic optimization with simulated annealing. Comput. Chem. Eng. 29(2), 273–290 (2005)

    Google Scholar 

  72. Fabro, J.A., Arruda, L., Neves Jr., F.: Startup of a distillation column using intelligent control techniques. Comput. Chem. Eng. 30(2), 309–320 (2005)

    Google Scholar 

  73. Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation, and machine learning. Phys. D Nonlinear Phenom. 22(1–3), 187–204 (1986)

    MathSciNet  Google Scholar 

  74. Fieg, G., Luo, X., Jezowski, J.: A monogenetic algorithm for optimal design of large-scale heat exchanger networks. Chem. Eng. Process. Process Intensif. 48(11–12), 1506–1516 (2009)

    Google Scholar 

  75. Fogel, D.B.: Applying evolutionary programming to selected traveling salesman problems. Cybern. Syst. 24(1), 27–36 (1993)

    MathSciNet  Google Scholar 

  76. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley, New York (1966)

    MATH  Google Scholar 

  77. Fonseca, C., Fleming, P.: Genetic algorithms for multiobjective optimization: Formulation, discussion, generalization. In: Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)

    Google Scholar 

  78. Fraga, E.S., Matias, T.R.S.: Synthesis and optimization of a nonideal distillation system using a parallel genetic algorithm. Comput. Chem. Eng. 20(Suppl. 1)(1), S79–S84 (1996)

    Google Scholar 

  79. Freitas, A.: A review of evolutionary algorithms for data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 371–400. Springer, New York (2010)

    Google Scholar 

  80. Frewen, T.A., Sinno, T., Haeckl, W., von Ammon, W.: A systems-based approach for generating quantitative models of microstructural evolution in silicon materials processing. Comput. Chem. Eng. 29(4), 713–730 (2005)

    Google Scholar 

  81. Furman, K.C., Sahinidis, N.V.: A critical review and annotated bibliography for heat exchanger network synthesis in the 20th century. Ind. Eng. Chem. Res. 41(10), 2335–2370 (2002)

    Google Scholar 

  82. Garrard, A., Fraga, E.S.: Mass exchange network synthesis using genetic algorithms. Comput. Chem. Eng. 22(12), 1837–1850 (1998)

    Google Scholar 

  83. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: Harmony search. Simulation 76(2), 60–68 (2001)

    Google Scholar 

  84. Glover, F.: Heuristics for integer programming using surrogate constraints. Decis. Sci. 8(1), 156–166 (1977)

    Google Scholar 

  85. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)

    MATH  MathSciNet  Google Scholar 

  86. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic, Norwell (1997)

    MATH  Google Scholar 

  87. Glover, F., Laguna, M., Marti, R.: Fundamentals of scatter search and path relinking. Control Cybern. 39(3), 653–684 (2000)

    MathSciNet  Google Scholar 

  88. Goggos, V., King, R.: Evolutionary predictive control (epc). Comput. Chem. Eng. 20(Suppl. 2)(6–7), S817–S822 (1996)

    Google Scholar 

  89. Gomez-Castro, F.I., Rodriguez-Angeles, M.A., Segovia-Hernandez, J.G., Gutierrez-Antonio, C., Briones-Ramirez, A.: Optimal designs of multiple dividing wall columns. Chem. Eng. Technol. 34(12), 2051–2058 (2011)

    Google Scholar 

  90. Gomez-Castro, F.I., Segovia-Hernandez, J.G., Hernandez, S., Gutierrez-Antonio, C., Briones-Ramirez, A.: Dividing wall distillation columns: Optimization and control properties. Chem. Eng. Technol. 31(9), 1246–1260 (2008)

    Google Scholar 

  91. Gordon, P.A.: Statistical associating fluid theory, 2. Estimation of parameters to predict lube-ranged isoparaffin properties. Ind. Eng. Chem. Res. 40(13), 2956–2965 (2001)

    Google Scholar 

  92. Gorji-Bandpy, M., Yahyazadeh-Jelodar, H., Khalili, M.: Optimization of heat exchanger network. Appl. Therm. Eng. 31(5), 779–784 (2011)

    Google Scholar 

  93. Graells, M., Cantón, J., Peschaud, B., Puigjaner, L.: General approach and tool for the scheduling of complex production systems. Comput. Chem. Eng. 22, S395–S402 (1998)

    Google Scholar 

  94. Grosman, B., Lewin, D.R.: Automated nonlinear model predictive control using genetic programming. Comput. Chem. Eng. 26(4–5), 631–640 (2002)

    Google Scholar 

  95. Handl, J., Kell, D., Knowle, J.: Multiobjective optimization in bioinformatics and computational biology. IEEE/ACM Trans. Comput. Biol. Bioinforma. 4(2), 279–292 (2007)

    Google Scholar 

  96. Hanke, M., Li, P.: Simulated annealing for the optimization of batch distillation processes. Comput. Chem. Eng. 24(1), 1–8 (2000)

    Google Scholar 

  97. He, S., Wu, Q.H., Saunders, J.R.: Group search optimizer: An optimization algorithm inspired by animal searching behavior. IEEE Trans. Evol. Comput. 13(5), 973–990 (2009)

    Google Scholar 

  98. He, Y., Hui, C.W.: Genetic algorithm for large-size multi-stage batch plant scheduling. Chem. Eng. Sci. 62(5), 1504–1523 (2007)

    Google Scholar 

  99. He, Y., Hui, C.W.: A novel search framework for multi-stage process scheduling with tight due dates. AIChE J. 56(8), 2103–2121 (2010)

    Google Scholar 

  100. Hiden, H.G., Willis, M.J., Tham, M.T., Montague, G.A.: Non-linear principal components analysis using genetic programming. Comput. Chem. Eng. 23(3), 413–425 (1999)

    Google Scholar 

  101. Hinchliffe, M.P., Willis, M.J.: Dynamic systems modelling using genetic programming. Comput. Chem. Eng. 27(12), 1841–1854 (2003)

    Google Scholar 

  102. Holland, J.H.: Adaption in Natural and Artificial Systems. The University of Michigan Press, Ann Harbor (1975)

    MATH  Google Scholar 

  103. von Homeyer, A.: Evolutionary algorithms and their applications in chemistry. In: Handbook of Chemoinformatics, pp. 1239–1280. Wiley-VCH Verlag GmbH, Weinheim, Germany (2008)

    Google Scholar 

  104. Hudebine, D., Verstraete, J.J.: Molecular reconstruction of LCO gasoils from overall petroleum analyses. Chem. Eng. Sci. 59(22–23), 4755–4763 (2004)

    Google Scholar 

  105. Huo, Z., Zhao, L., Yin, H., Ye, J.: Simultaneous synthesis of structural-constrained heat exchanger networks with and without stream splits. Can. J. Chem. Eng. 91(5), 830–842 (2013)

    Google Scholar 

  106. Iancu, P., Plesu, V., Lavric, V.: Regeneration of internal streams as an effective tool for wastewater network optimisation. Comput. Chem. Eng. 33(3), 731–742 (2009)

    Google Scholar 

  107. Immanuel, C.D., Doyle, F.J.: Open-loop control of particle size distribution in semi-batch emulsion copolymerization using a genetic algorithm. Chem. Eng. Sci. 57(20), 4415–4427 (2002)

    Google Scholar 

  108. Immanuel, C.D., Doyle, F.J.: Hierarchical multiobjective strategy for particle-size distribution control. AIChE J. 49(9), 2383–2399 (2003)

    Google Scholar 

  109. Ingber, L.: Adaptive simulated annealing (ASA): Lessons learned. Control Cybern. 25, 33–54 (1996)

    MATH  Google Scholar 

  110. Irizarry, R.: LARES: An artificial chemical process approach for optimization. Evol. Comput. 12(4), 435–459 (2004)

    MathSciNet  Google Scholar 

  111. Istadi, I., Amin, N.A.S.: Hybrid artificial neural networ-genetic algorithm technique for modeling and optimization of plasma reactor. Ind. Eng. Chem. Res. 45(20), 6655–6664 (2006)

    Google Scholar 

  112. Jabri, K., Dumur, D., Godoy, E., Mouchette, A., Bale, B.: Particle swarm optimization based tuning of a modified smith predictor for mould level control in continuous casting. J. Process Control 21(2), 263–270 (2011)

    Google Scholar 

  113. Jaimes, A.L., Coello, C.: Multi-objective evolutionary algorithms: A review of the state-of-the-art and some of their applications in chemical engineering. In: Rangaiah, G. (ed.) Multi-Objective Optimization: Techniques and Applications in Chemical Engineering, pp. 61–86. World Scientific, Singapore (2008)

    Google Scholar 

  114. Jain, S., Kim, J.K., Smith, R.: Process synthesis of batch distillation systems. Ind. Eng. Chem. Res. 52(24), 8272–8288 (2013)

    Google Scholar 

  115. Jayaraman, V.K., Kulkarni, B.D., Karale, S., Shelokar, P.: Ant colony framework for optimal design and scheduling of batch plants. Comput. Chem. Eng. 24, 1901–1912 (2000)

    Google Scholar 

  116. Jezowski, J., Bochenek, R., Poplewski, G.: On application of stochastic optimization techniques to designing heat exchanger- and water networks. Chem. Eng. Process. Process Intensif. 46(11), 1160–1174 (2007)

    Google Scholar 

  117. Jiang, D., Chang, C.T.: An algorithmic revamp strategy for improving operational flexibility of multi-contaminant water networks. Chem. Eng. Sci. 102(0), 289–299 (2013)

    Google Scholar 

  118. Kasat, R.B., Kunzru, D., Saraf, D.N., Gupta, S.K.: Multiobjective optimization of industrial FCC units using elitist non-dominated sorting genetic algorithm. Ind. Eng. Chem. Res. 41(19), 4765–4776 (2002)

    Google Scholar 

  119. Kasat, R.B., Ray, A.K., Gupta, S.K.: Applications of genetic algorithm in polymer science and engineering. Mater. Manuf. Process. 18(3), 523–532 (2003)

    Google Scholar 

  120. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  121. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    MATH  MathSciNet  Google Scholar 

  122. Kishore, M., Jhansi, L., Kumar, A.: Kinetic study of oxidation of cyclohexane using complex catalyst. AIChE J. 53(6), 1550–1561 (2007)

    Google Scholar 

  123. Klemeś, J., Stehlík, P.: Recent advances on heat, chemical and process integration, multiobjective and structural optimisation. Appl. Therm. Eng. 26(13), 1339–1344 (2006)

    Google Scholar 

  124. Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  125. Ku, H.M., Karimi, I.: An evaluation of simulated annealing for batch process scheduling. Ind. Eng. Chem. Res. 30(1), 163–169 (1991)

    Google Scholar 

  126. Kundu, M., Mandal, B.P., Bandyopadhyay, S.S.: Vapor-liquid equilibrium of CO2 in aqueous solutions of 2-amino-2-methyl-1-propanol. J. Chem. Eng. Data 48(4), 789–796 (2003)

    Google Scholar 

  127. Lavric, V., Iancu, P., Plesu, V.: Genetic algorithm optimisation of water consumption and wastewater network topology. J. Clean. Prod. 13(15), 1405–1415 (2005)

    Google Scholar 

  128. Leboreiro, J., Acevedo, J.: Processes synthesis and design of distillation sequences using modular simulators: A genetic algorithm framework. Comput. Chem. Eng. 28(8), 1223–1236 (2004)

    Google Scholar 

  129. Lee, C.J., Prasad, V., Lee, J.M.: Stochastic nonlinear optimization for robust design of catalysts. Ind. Eng. Chem. Res. 50(7), 3938–3946 (2011)

    Google Scholar 

  130. Lee, K.B., Kasat, R.B., Cox, G.B., Wang, N.H.L.: Simulated moving bed multiobjective optimization using standing wave design and genetic algorithm. AIChE J. 54(11), 2852–2871 (2008)

    Google Scholar 

  131. Lee, M.H., Han, C., Chang, K.S.: Hierarchical time-optimal control of a continuous copolymerization reactor during start-up or grade change operation using genetic algorithms. Comput. Chem. Eng. 21(9), 1037–1042 (1997)

    Google Scholar 

  132. Lee, Y.G., Malone, M.F.: Flexible batch process planning. Ind. Eng. Chem. Res. 39(6), 2045–2055 (2000)

    Google Scholar 

  133. Lee, Y.G., Malone, M.F.: A general treatment of uncertainties in batch process planning. Ind. Eng. Chem. Res. 40(6), 1507–1515 (2001)

    Google Scholar 

  134. Lewin, D.R.: Feedforward control design for distillation systems aided by disturbance cost contour maps. Comput. Chem. Eng. 18(5), 421–426 (1994)

    Google Scholar 

  135. Lewin, D.R.: A generalized method for HEN synthesis using stochastic optimization II. The synthesis of cost-optimal networks. Comput. Chem. Eng. 22(10), 1387–1405 (1998)

    Google Scholar 

  136. Lewin, D.R., Wang, H., Shalev, O.: A generalized method for HEN synthesis using stochastic optimization I. General framework and MER optimal synthesis. Comput. Chem. Eng. 22(10), 1503–1513 (1998)

    Google Scholar 

  137. Li, C., Zhu, Q., Geng, Z.: Multi-objective particle swarm optimization hybrid algorithm: An application on industrial cracking furnace. Ind. Eng. Chem. Res. 46(11), 3602–3609 (2007)

    Google Scholar 

  138. Li, L., Wang, C., Song, B., Mi, L., Hu, J.: Kinetic parameters estimation in the polymerase chain reaction process using the genetic algorithm. Ind. Eng. Chem. Res. 51(40), 13,268–13,273 (2012)

    Google Scholar 

  139. Lim, E., Wee, C.: Application of particle swarm optimization to fourier series regression of non-periodic data. Ind. Eng. Chem. Res. 50(4), 2307–2322 (2011)

    Google Scholar 

  140. Lima, R.M., Francois, G., Srinivasan, B., Salcedo, R.L.: Dynamic optimization of batch emulsion polymerization using MSIMPSA, a simulated-annealing-based algorithm. Ind. Eng. Chem. Res. 43(24), 7796–7806 (2004)

    Google Scholar 

  141. Lin, B., Miller, D.: Solving heat exchanger network synthesis problems with tabu search. Comput. Chem. Eng. 28(8), 1451–1464 (2004)

    Google Scholar 

  142. Liu, B., Wang, L., Liu, Y., Qian, B., Jin, Y.H.: An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes. Comput. Chem. Eng. 34(4), 518–528 (2010)

    Google Scholar 

  143. Liu, F., Xu, X.T., Li, L.J., Wu, Q.H.: The group search optimizer and its application on truss structure design. In: Fourth International Conference on Natural Computation, vol. 7, pp. 688–692 (2008)

    Google Scholar 

  144. Liu, L., Du, J., El-Halwagi, M.M., Ponce-Ortega, J.M., Yao, P.: A systematic approach for synthesizing combined mass and heat exchange networks. Comput. Chem. Eng. 53(0), 1–13 (2013)

    Google Scholar 

  145. Löhl, T., Schulz, C., Engell, S.: Sequencing of batch operations for a highly coupled production process: Genetic algorithms versus mathematical programming. Comput. Chem. Eng. 22, S579–S585 (1998)

    Google Scholar 

  146. Lotfi, R., Boozarjomehry, R.B.: Superstructure optimization in heat exchanger network (hen) synthesis using modular simulators and a genetic algorithm framework. Ind. Eng. Chem. Res. 49(10), 4731–4737 (2010)

    Google Scholar 

  147. Low, K.H., Sorensen, E.: Simultaneous optimal configuration, design and operation of batch distillation. AIChE J. 51(6), 1700–1713 (2005)

    Google Scholar 

  148. Lu, X., Huang, M., Li, Y., Chen, M.: Subspace-modeling-based nonlinear measurement for process design. Ind. Eng. Chem. Res. 50(23), 13457–13465 (2011)

    Google Scholar 

  149. Lu, X.J., Li, H.X., Chen, C.L.P.: Robust optimal design with consideration of robust eigenvalue assignment. Ind. Eng. Chem. Res. 49(7), 3306–3315 (2010)

    Google Scholar 

  150. Luo, X., Wen, Q.Y., Fieg, G.: A hybrid genetic algorithm for synthesis of heat exchanger networks. Comput. Chem. Eng. 33(6), 1169–1181 (2009)

    Google Scholar 

  151. Ma, X., Yao, P., Luo, X., Roetzel, W.: Synthesis of multi-stream heat exchanger network for multi-period operation with genetic/simulated annealing algorithms. Appl. Therm. Eng. 28(8–9), 809–823 (2008)

    Google Scholar 

  152. Majdalani, S., Fahs, M., Carrayrou, J., Ackerer, P.: Reactive transport parameter estimation: Genetic algorithm vs. Monte Carlo approach. AIChE J. 55(8), 1959–1968 (2009)

    Google Scholar 

  153. Mani, T., Murugan, P., Mahinpey, N.: Determination of distributed activation energy model kinetic parameters using simulated annealing optimization method for nonisothermal pyrolysis of lignin. Ind. Eng. Chem. Res. 48(3), 1464–1467 (2009)

    Google Scholar 

  154. Mansoornejad, B., Mostoufi, N., Jalali-Farahani, F.: A hybrid GA-SQP optimization technique for determination of kinetic parameters of hydrogenation reactions. Comput. Chem. Eng. 32(7), 1447–1455 (2008)

    Google Scholar 

  155. Mariano, A.P., Costa, C.B.B., de Toledo, E.C.V., Melo, D.N.C., Filho, R.M.: Analysis of the particle swarm algorithm in the optimization of a three-phase slurry catalytic reactor. Comput. Chem. Eng. 35(12), 2741–2749 (2011)

    Google Scholar 

  156. Marti, R., Laguna, M., Glover, F.: Principles of scatter search. Eur. J. Oper. Res. 169(2), 359–372 (2006)

    MATH  MathSciNet  Google Scholar 

  157. Matsuura, K., Shiba, H., Nunokawa, Y., Shimizu, H., Shioya, S., Suga, K.: Calculation of optimal trajectories for fermentation processes by genetic algorithm. J. Ferment. Bioeng. 75(6), 474– (1993)

    Google Scholar 

  158. McKay, B., Willis, M., Barton, G.: Steady-state modelling of chemical process systems using genetic programming. Comput. Chem. Eng. 21(9), 981–996 (1997)

    Google Scholar 

  159. Méndez, C.A., Cerdá, J., Grossmann, I.E., Harjunkoski, I., Fahl, M.: State-of-the-art review of optimization methods for short-term scheduling of batch processes. Comput. Chem. Eng. 30(6–7), 913–946 (2006)

    Google Scholar 

  160. Michalewicz, Z., Janikow, C.Z., Krawczyk, J.B.: A modified genetic algorithm for optimal control problems. Comput. Math. Appl. 23(12), 83–94 (1992)

    MATH  Google Scholar 

  161. Miladi, M., Mujtaba, I.: Optimisation of design and operation policies of binary batch distillation with fixed product demand. Comput. Chem. Eng. 28(11), 2377–2390 (2004)

    Google Scholar 

  162. Miranda-Galindo, E.Y., Segovia-Hernandez, J.G., Hernandez, S., Gutierrez-Antonio, C., Briones-Ramirez, A.: Reactive thermally coupled distillation sequences: Pareto front. Ind. Eng. Chem. Res. 50(2), 926–938 (2011)

    Google Scholar 

  163. Mitra, K.: Genetic algorithms in polymeric material production, design, processing and other applications: A review. Int. Mater. Rev. 53(5), 275–297 (2008)

    Google Scholar 

  164. Mitra, K., Deb, K., Gupta, S.K.: Multiobjective dynamic optimization of an industrial nylon 6 semibatch reactor using genetic algorithm. J. Appl. Polym. Sci. 69(1), 69–87 (1998)

    Google Scholar 

  165. Mitra, K., Majumdar, S., Raha, S.: Multiobjective optimization of a semibatch epoxy polymerization process using the elitist genetic algorithm. Ind. Eng. Chem. Res. 43(19), 6055–6063 (2004)

    Google Scholar 

  166. Modla, G., Lang, P.: Removal and recovery of organic solvents from aqueous waste mixtures by extractive and pressure swing distillation. Ind. Eng. Chem. Res. 51(35), 11473–11481 (2012)

    Google Scholar 

  167. Moros, R., Kalies, H., Rex, H., Schaffarczyk, S.: A genetic algorithm for generating initial parameter estimations for kinetic models of catalytic processes. Comput. Chem. Eng. 20(10), 1257–1270 (1996)

    Google Scholar 

  168. Mośat, A., Cavin, L., Fischer, U., Hungerbühler, K.: Multiobjective optimization of multipurpose batch plants using superequipment class concept. Comput. Chem. Eng. 32(3), 512–529 (2008)

    Google Scholar 

  169. Mośat, A., Fischer, U., Hungerbühler, K.: Multiobjective batch process design aiming at robust performances. Chem. Eng. Sci. 62(21), 6015–6031 (2007)

    Google Scholar 

  170. Nakrani, S., Tovey, C.: On honey bees and dynamic server allocation in internet hosting centers. Adapt. Behav. 12(3–4), 223–240 (2004)

    Google Scholar 

  171. Nielsen, J.S., Hansen, M.W., bay Joergensen, S.: Heat exchanger network modelling framework for optimal design and retrofitting. Comput. Chem. Eng. 20(Suppl. 1)(3), S249–S254 (1996)

    Google Scholar 

  172. Niu, D., Jia, M., Wang, F., He, D.: Optimization of nosiheptide fed-batch fermentation process based on hybrid model. Ind. Eng. Chem. Res. 52(9), 3373–3380 (2013)

    Google Scholar 

  173. Okur, H., Eymir, C.: Dehydration kinetics of ulexite by thermogravimetric data using the coats-redfern and genetic algorithm method. Ind. Eng. Chem. Res. 42(15), 3642–3646 (2003)

    Google Scholar 

  174. Omer, S., Mustafa, O., Mehmet, A., Gurboz, B.U.: Calcination kinetics of ammonium pentaborate using the coats-redfern and genetic algorithm method by thermal analysis. Ind. Eng. Chem. Res. 40(6), 1465–1470 (2001)

    Google Scholar 

  175. Ourique, C.O., Biscaia Jr., E.C., Pinto, J.C.: The use of particle swarm optimization for dynamical analysis in chemical processes. Comput. Chem. Eng. 26(12), 1783–1793 (2002)

    Google Scholar 

  176. Pal, S., Bandyopadhyay, S., Ray, S.: Evolutionary computation in bioinformatics: A review. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 36(5), 601–615 (2006)

    Google Scholar 

  177. Park, S.J., Bhargava, S., Chase, G.G.: Fitting of kinetic parameters of NO reduction by CO in fibrous media using a genetic algorithm. Comput. Chem. Eng. 34(4), 485–490 (2010)

    Google Scholar 

  178. Park, T.Y., Froment, G.F.: A hybrid genetic algorithm for the estimation of parameters in detailed kinetic models. Comput. Chem. Eng. 22(Suppl. 1) S103–S110 (1998)

    Google Scholar 

  179. Passino, K.M.: Distributed optimization and control using only a germ of intelligence. In: Proceedings of the IEEE International Symposium on Intelligent Control, pp. 5–13 (2000)

    Google Scholar 

  180. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)

    MathSciNet  Google Scholar 

  181. Patel, A.N., Mah, R.S.H., Karimi, I.: Preliminary design of multiproduct non-continuous plants using simulating annealing. Comput. Chem. Eng. 15, 451 (1991)

    Google Scholar 

  182. Peng, H., Ling, X., Wu, E.: An improved particle swarm algorithm for optimal design of plate-fin heat exchangers. Ind. Eng. Chem. Res. 49(13), 6144–6149 (2010)

    Google Scholar 

  183. Pham, Q.T.: Dynamic optimization of chemical engineering processes by an evolutionary method. Comput. Chem. Eng. 22(7–8), 1089–1097 (1998)

    Google Scholar 

  184. Ponce-Ortega, J.M., Serna-Gonzalez, M., Jimenez-Gutierrez, A.: Synthesis of multipass heat exchanger networks using genetic algorithms. Comput. Chem. Eng. 32(10), 2320–2332 (2008)

    Google Scholar 

  185. Prakotpol, D., Srinophakun, T.: Gapinch: Genetic algorithm toolbox for water pinch technology. Chem. Eng. Process. Process Intensif. 43(2), 203–217 (2004)

    Google Scholar 

  186. Prata, D.M., Schwaab, M., Lima, E.L., Pinto, J.C.: Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: Application for an industrial polypropylene reactor. Chem. Eng. Sci. 64(18), 3953–3967 (2009)

    Google Scholar 

  187. Qian, F., Kong, X., Cheng, H., Du, W., Zhong, W.: Development of a kinetic model for industrial entrained flow coal gasifiers. Ind. Eng. Chem. Res. 52(5), 1819–1828 (2013)

    Google Scholar 

  188. Qian, F., Sun, F., Du, W., Zhong, W.: Novel hybrid evolutionary algorithm for dynamic optimization problems and its application in an ethylene oxide hydration reactor. Ind. Eng. Chem. Res. 51(49), 15974–15985 (2012)

    Google Scholar 

  189. Rahimpour, M., Behjati, H.E.: Dynamic optimization of membrane dual-type methanol reactor in the presence of catalyst deactivation using genetic algorithm. Fuel Process. Technol. 90(2), 279–291 (2009)

    Google Scholar 

  190. Rajesh, J., Gupta, K., Kusumakar, H.S., Jayaraman, V., Kulkarni, B.: Dynamic optimization of chemical processes using ant colony framework. Comput. Chem. 25(6), 583–595 (2001)

    Google Scholar 

  191. Ramanathan, S., Mukherjee, S., Dahule, R., Ghosh, S., Rahman, I., Tambe, S., Ravetkar, D., Kulkarni, B.: Optimization of continuous distillation columns using stochastic optimization approaches. Chem. Eng. Res. Des. 79(3), 310–322 (2001)

    Google Scholar 

  192. Ramteke, M., Gupta, S.K.: Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm. Ind. Eng. Chem. Res. 48(21), 9671–9685 (2009)

    Google Scholar 

  193. Ramteke, M., Srinivasan, R.: Large-scale refinery crude oil scheduling by integrating graph representation and genetic algorithm. Ind. Eng. Chem. Res. 51(14), 5256–5272 (2012)

    Google Scholar 

  194. Ravagnani, M.A.S.S., Silva, A.P., Arroyo, P.A., Constantino, A.A.: Heat exchanger network synthesis and optimization using genetic algorithm. Appl. Therm. Eng. 25, 1003–1017 (2005)

    Google Scholar 

  195. Ravagnani, M.A.S.S., Silva, A.P., Biscaia, E.C., Caballero, J.A.: Optimal design of shell-and-tube heat exchangers using particle swarm optimization. Ind. Eng. Chem. Res. 48(6), 2927–2935 (2009)

    Google Scholar 

  196. Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (Ph.D. Thesis). Fromman-Holzboog Verlag, Stutgart, Germany (1973)

    Google Scholar 

  197. Reeves, C., Rowe, J.: Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory. Kluwer Academic, Norwell (2002)

    Google Scholar 

  198. Rezaei, E., Shafiei, S.: Heat exchanger networks retrofit by coupling genetic algorithm with NLP and ILP methods. Comput. Chem. Eng. 33(9), 1451–1459 (2009)

    Google Scholar 

  199. Roubos, J., van Straten, G., van Boxtel, A.: An evolutionary strategy for fed-batch bioreactor optimization; concepts and performance. J. Biotechnol. 67(2), 173–187 (1999)

    Google Scholar 

  200. Routray, K., Deo, G.: Kinetic parameter estimation for a multiresponse nonlinear reaction model. AIChE J. 51(6), 1733–1746 (2005)

    Google Scholar 

  201. Ryu, J.H., Lee, H.K., Lee, I.B.: Optimal scheduling for a multiproduct batch process with minimization of penalty on due date period. Ind. Eng. Chem. Res. 40(1), 228–233 (2001)

    Google Scholar 

  202. S. Raimondeau, Aghalayam, P., Mhadeshwar, A.B., Vlachos, D.G.: Parameter optimization of molecular models: Application to surface kinetics. Ind. Eng. Chem. Res. 42(6), 1174–1183 (2003)

    Google Scholar 

  203. Sankararao, B., Gupta, S.K.: Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using two jumping gene adaptations of simulated annealing. Comput. Chem. Eng. 31(11), 1496–1515 (2007)

    Google Scholar 

  204. Sankararao, B., Yoo, C.K.: Development of a robust multiobjective simulated annealing algorithm for solving multiobjective optimization problems. Ind. Eng. Chem. Res. 50(11), 6728–6742 (2011)

    Google Scholar 

  205. Sarkar, D., Modak, J.M.: Optimisation of fed-batch bioreactors using genetic algorithms. Chem. Eng. Sci. 58(11), 2283–2296 (2003)

    Google Scholar 

  206. Sarkar, D., Modak, J.M.: Optimization of fed-batch bioreactors using genetic algorithm: Multiple control variables. Comput. Chem. Eng. 28(5), 789–798 (2004)

    Google Scholar 

  207. Sarkar, D., Rohani, S., Jutan, A.: Multiobjective optimization of semibatch reactive crystallization processes. AIChE J. 53(5), 1164–1177 (2007)

    Google Scholar 

  208. Schwaab, M., Biscaia Jr., E.C., Monteiro, J.L., Pinto, J.C.: Nonlinear parameter estimation through particle swarm optimization. Chem. Eng. Sci. 63(6), 1542–1552 (2008)

    Google Scholar 

  209. Schwefel, H.P.: Numerische Optimierung von Computer-Modellen (Ph.D. thesis). Birkhäuser, Basel (1977) [English edition: Numerical Optimization of Computer Models. Wiley, Chichester (1981)]

    Google Scholar 

  210. Schwefel, H.P.P.: Evolution and Optimum Seeking: The Sixth Generation. Wiley, New York (1993)

    Google Scholar 

  211. Senties, O.B., Azzaro-Pantel, C., Pibouleau, L., Domenech, S.: A neural network and a genetic algorithm for multiobjective scheduling of semiconductor manufacturing plants. Ind. Eng. Chem. Res. 48(21), 9546–9555 (2009)

    Google Scholar 

  212. Shafiei, S., Davin, A., Pibouleau, L., Domenech, S., Floquet, P.: Mass exchange network synthesis by coupling a genetic algorithm and a SQP procedure. In: Pierucci, S. (ed.) European Symposium on Computer Aided Process Engineering-10. Computer Aided Chemical Engineering, vol. 8, pp. 973–978 (2000)

    Google Scholar 

  213. Shafiei, S., Domenech, S., Koteles, R., Paris, J.: System closure in pulp and paper mills: Network analysis by genetic algorithm. J. Clean. Prod. 12(2), 131–135 (2004)

    Google Scholar 

  214. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: Multiobjective optimization of reactor-regenerator system using ant algorithm. Pet. Sci. Technol. 21(7–8), 1167–1184 (2003)

    Google Scholar 

  215. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Anal. Chim. Acta 509(2), 187–195 (2004)

    Google Scholar 

  216. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony classifier system: Application to some process engineering problems. Comput. Chem. Eng. 28(9), 1577–1584 (2004)

    Google Scholar 

  217. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: Multicanonical jump walk annealing assisted by tabu for dynamic optimization of chemical engineering processes. Eur. J. Oper. Res. 185(3), 1213–1229 (2008)

    MATH  MathSciNet  Google Scholar 

  218. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998)

    Google Scholar 

  219. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Google Scholar 

  220. Simon, H.A. (ed.): Models of Man: Social and Rational. Wiley, New York (1997)

    Google Scholar 

  221. Singh, A.K., Hahn, J.: Sensor location for stable nonlinear dynamic systems: multiple sensor case. Ind. Eng. Chem. Res. 45(10), 3615–3623 (2006)

    Google Scholar 

  222. Srinivasan, B., Palanki, S., Bonvin, D.: Dynamic optimization of batch processes: I. Characterization of the nominal solution. Comput. Chem. Eng. 27(1), 1–26 (2003)

    Google Scholar 

  223. Storn, R.: On the usage of differential evolution for function optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society, pp. 519–523 (1996)

    Google Scholar 

  224. Storn, R., Price, K.: Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    MATH  MathSciNet  Google Scholar 

  225. Stützle, T., Hoos, H.H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16, 889–914 (2000)

    Google Scholar 

  226. Sumana, C., Venkateswarlu, C.: Genetically tuned decentralized proportional-integral controllers for composition control of reactive distillation. Ind. Eng. Chem. Res. 49(3), 1297–1311 (2010)

    Google Scholar 

  227. Taillard, E.: Robust taboo search for the quadratic assignment problem. Parallel Comput. 17, 443–455 (1991)

    MathSciNet  Google Scholar 

  228. Tang, L., Yan, P.: Particle swarm optimization algorithm for a campaign planning problem in process industries. Ind. Eng. Chem. Res. 47(22), 8775–8784 (2008)

    Google Scholar 

  229. Tang, L., Yan, P.: Particle swarm optimization algorithm for a batching problem in the process industry. Ind. Eng. Chem. Res. 48(20), 9186–9194 (2009)

    Google Scholar 

  230. Tarafder, A., Lee, B.C.S., Ray, A.K., Rangaiah, G.P.: Multiobjective optimization of an industrial ethylene reactor using a non-dominated sorting genetic algorithm. Ind. Eng. Chem. Res. 44(1), 124–141 (2005)

    Google Scholar 

  231. Tayal, M.C., Fu, Y., Diwekar, U.M.: Optimal design of heat exchangers: A genetic algorithm framework. Ind. Eng. Chem. Res. 38(2), 456–467 (1999)

    Google Scholar 

  232. Thornhill, N.F., Manela, M., Campbell, J.A., Stone, K.M.: Two methods of selecting smoothing splines applied to fermentation process data. AIChE J. 40(4), 716–725 (1994)

    Google Scholar 

  233. Tian, X., Zhang, X., Zeng, S., Xu, Y., Yao, Y., Chen, Y., Huang, L., Zhao, Y., Zhang, S.: Process analysis and multi-objective optimization of ionic liquid-containing acetonitrile process to produce 1,3-butadiene. Chem. Eng. Technol. 34(6), 927–936 (2011)

    Google Scholar 

  234. Tsai, M.J., Chang, C.T.: Water usage and treatment network design using genetic algorithms. Ind. Eng. Chem. Res. 40(22), 4874–4888 (2001)

    Google Scholar 

  235. Upreti, S.R.: A new robust technique for optimal control of chemical engineering processes. Comput. Chem. Eng. 28(8), 1325–1336 (2004)

    Google Scholar 

  236. Venkateswarlu, C., Reddy, A.D.: Nonlinear model predictive control of reactive distillation based on stochastic optimization. Ind. Eng. Chem. Res. 47(18), 6949–6960 (2008)

    Google Scholar 

  237. Verheyen, W., Zhang, N.: Design of flexible heat exchanger network for multi-period operation. Chem. Eng. Sci. 61(23), 7730–7753 (2006)

    Google Scholar 

  238. Wang, C., Quan, H., Xu, X.: Optimal design of multiproduct batch chemical process using genetic algorithms. Ind. Eng. Chem. Res. 35(10), 3560–3566 (1996)

    Google Scholar 

  239. Wang, C., Quan, H., Xu, X.: Optimal design of multiproduct batch chemical processes using tabu search. Comput. Chem. Eng. 23(3), 427–437 (1999)

    Google Scholar 

  240. Wang, C., Zhao, X.: Ants foraging mechanism in the design of multiproduct batch chemical process. Ind. Eng. Chem. Res. 41(26), 6678–6686 (2002)

    Google Scholar 

  241. Wang, F.S., Sheu, J.W.: Multiobjective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast. Chem. Eng. Sci. 55(18), 3685–3695 (2000)

    Google Scholar 

  242. Wang, J., Smith, R.: Synthesis and optimization of low-temperature gas separation processes. Ind. Eng. Chem. Res. 44(8), 2856–2870 (2005)

    Google Scholar 

  243. Wang, K., Löhl, T., Stobbe, M., Engell, S.: A genetic algorithm for online-scheduling of a multiproduct polymer batch plant. Comput. Chem. Eng. 24, 393–400 (2000)

    Google Scholar 

  244. Wang, K., Qian, Y., Yuan, Y., Yao, P.: Synthesis and optimization of heat integrated distillation systems using an improved genetic algorithm. Comput. Chem. Eng. 23(1), 125–136 (1998)

    Google Scholar 

  245. Wang, K., Wang, N.: A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil. Chem. Eng. J. 167(1), 228–239 (2011)

    Google Scholar 

  246. Wang, Y., Smith, R.: Retrofit of a heat-exchanger network by considering heat-transfer enhancement and fouling. Ind. Eng. Chem. Res. 52(25), 8527–8537 (2013)

    Google Scholar 

  247. Wang, Y., Smith, R., Kim, J.K.: Heat exchanger network retrofit optimization involving heat transfer enhancement. Appl. Therm. Eng. 43, 7–13 (2012)

    Google Scholar 

  248. Wang, Y., Xiao, Q., Yang, N., Li, J.: In-depth exploration of the dual-bubble-size model for bubble columns. Ind. Eng. Chem. Res. 51(4), 2077–2083 (2012)

    Google Scholar 

  249. Wei-zhong, A., Xi-Gang, Y.: A simulated annealing-based approach to the optimal synthesis of heat-integrated distillation sequences. Comput. Chem. Eng. 33(1), 199–212 (2009)

    Google Scholar 

  250. Wolf, D., Moros, R.: Estimating rate constants of heterogeneous catalytic reactions without supposition of rate determining surface steps: An application of a genetic algorithm. Chem. Eng. Sci. 52(7), 1189–1199 (1997)

    Google Scholar 

  251. Wu, L., Chang, W.X., Guan, G.F.: Extractants design based on an improved genetic algorithm. Ind. Eng. Chem. Res. 46(4), 1254–1258 (2007)

    Google Scholar 

  252. Wu, L.Y., Hu, Y.D., Xu, D.M., Hua, B.: Solving batch production scheduling using genetic algorithm. In: Chen, B., Westerberg, A.W. (eds.) 8th International Symposium on Process Systems Engineering 2003. Computer Aided Chemical Engineering, vol. 15, pp. 648–653 (2003)

    Google Scholar 

  253. Xiao, J., Li, J., Xu, Q., Huang, Y., Lou, H.H.: ACS-based dynamic optimization for curing of polymeric coating. AIChE J. 52(4), 1410–1422 (2006)

    Google Scholar 

  254. Xue, D., Li, S., Li, Y.Y., Yao, P.: Synthesis of waste interception and allocation networks using genetic-alopex algorithm. Comput. Chem. Eng. 24(2), 1455–1460 (2000)

    Google Scholar 

  255. Bar Yam, Y.: Dynamics of Complex Systems. Addison-Wesley, Reading (1997)

    MATH  Google Scholar 

  256. Yamashita, Y., Shima, M.: Numerical computational method using genetic algorithm for the optimal control problem with terminal constraints and free parameters. Nonlinear Anal. Theory Methods Appl. 30(4), 2285–2290 (1997)

    MATH  MathSciNet  Google Scholar 

  257. Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome (2008)

    Google Scholar 

  258. Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) Stochastic Algorithms: Foundations and Applications. Lecture Notes in Computer Science, vol. 5792, pp. 169–178. Springer, Berlin (2009)

    Google Scholar 

  259. Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, Berlin (2010)

    Google Scholar 

  260. Yang, X.S., Deb, S.: Cuckoo search via lévy flights. In: World Congress on Nature and Biologically Inspired Computing, pp. 210–214 (2009)

    Google Scholar 

  261. Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)

    MATH  Google Scholar 

  262. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)

    Google Scholar 

  263. Yee, A.K.Y., Ray, A.K., Rangaiah, G.P.: Multiobjective optimization of an industrial styrene reactor. Comput. Chem. Eng. 27(1), 111–130 (2003)

    Google Scholar 

  264. Yiqing, L., Xigang, Y., Yongjian, L.: An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints. Comput. Chem. Eng. 31(3), 153–162 (2007)

    Google Scholar 

  265. Young, C.T., Zheng, Y., Yeh, C.W., Jang, S.S.: Information-guided genetic algorithm approach to the solution of MINLP problems. Ind. Eng. Chem. Res. 46(5), 1527–1537 (2007)

    Google Scholar 

  266. Yu, H., Fang, H., Yao, P., Yuan, Y.: A combined genetic algorithm/simulated annealing algorithm for large scale system energy integration. Comput. Chem. Eng. 24(8), 2023–2035 (2000)

    Google Scholar 

  267. Zhang, B., Chen, D., Zhao, W.: Iterative ant-colony algorithm and its application to dynamic optimization of chemical process. Comput. Chem. Eng. 29(10), 2078–2086 (2005)

    Google Scholar 

  268. Zhang, H., Rangaiah, G., Bonilla-Petriciolet, A.: Integrated differential evolution for global optimization and its performance for modeling vapor-liquid equilibrium data. Ind. Eng. Chem. Res. 50(17), 10047–10061 (2011)

    Google Scholar 

  269. Zhang, L., Linninger, A.A.: Towards computer-aided separation synthesis. AIChE J. 52(4), 1392–1409 (2006)

    Google Scholar 

  270. Zhang, Y., Fan, Y., Zhang, P.: Combining kernel partial least-squares modeling and iterative learning control for the batch-to-batch optimization of constrained nonlinear processes. Ind. Eng. Chem. Res. 49(16), 7470–7477 (2010)

    Google Scholar 

  271. Zhang, Y., Zhang, Y.: Fault detection of non-gaussian processes based on modified independent component analysis. Chem. Eng. Sci. 65(16), 4630–4639 (2010)

    Google Scholar 

  272. Zhao, C., Xu, Q., An, A.: Application of the parallel adaptive genetic simulated annealing algorithm for the synthesis of heat exchanger networks. Asia Pac. J. Chem. Eng. 7(5), 660–669 (2012)

    Google Scholar 

  273. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K.C., Tsahalis, D.T., Périaux, J., Papailiou, K.D., Fogarty, T. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100. Athens, Greece (2001)

    Google Scholar 

  274. Zuo, K., Wu, W.: Semi-realtime optimization and control of a fed-batch fermentation system. Comput. Chem. Eng. 24(2), 1105–1109 (2000)

    Google Scholar 

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Acknowledgment

Prakash Shelokar acknowledges the partial support received from MICINN under the Juan de la Cierva programme JCI-2010-07626.

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Authors and Affiliations

  1. European Center for Soft Computing, Mieres, Spain

    Prakash Shelokar

  2. SAS R&D (I) Pvt Ltd., Magarpatta city, Pune, India

    Abhijit Kulkarni

  3. Shiv Nadar University, Tehsil Dadri, District Gautam Buddha Nagar, UP, 201314, India

    Valadi K. Jayaraman

  4. Center for Development of Advanced Computing (CDAC), Pune, India

    Valadi K. Jayaraman

  5. Université Paris-Est Créteil Val-de-Marne, Paris, France

    Patrick Siarry

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  1. Prakash Shelokar
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  2. Abhijit Kulkarni
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  3. Valadi K. Jayaraman
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  4. Patrick Siarry
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Correspondence to Prakash Shelokar , Abhijit Kulkarni , Valadi K. Jayaraman or Patrick Siarry .

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  1. Evol. Comput. & Image Proc. Group, Center for Develop. of Adv. Computing, Pune, India

    Jayaraman Valadi

  2. Lab. LiSSi (EA 3956), Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

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Shelokar, P., Kulkarni, A., Jayaraman, V.K., Siarry, P. (2014). Metaheuristics in Process Engineering: A Historical Perspective. In: Valadi, J., Siarry, P. (eds) Applications of Metaheuristics in Process Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06508-3_1

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