Skip to main content
SpringerLink
Log in

Multi-Objective Genetic Algorithms for Chemical Engineering Applications

  • Chapter
  • First Online:
Applications of Metaheuristics in Process Engineering

  • 1467 Accesses

Abstract

The current environmental and social awareness leads optimization researchers to be more and more concerned with multi-objective optimization problems (MOOPs). Evolutionary methods and particularly genetic algorithms are commonly used in chemical engineering, where the problems generally involve complex models embedded in an outer optimization loop. This paper first presents two multi-objective genetic algorithms to tackle continuous and mixed-integer chemical engineering problems. The algorithms are then illustrated by classical chemical engineering benchmark problems often used in the literature for mono-objective optimization studies: three bi-objective ones (ammonia synthesis reactor, alkylation plant, natural gas transportation network), a structural mixed-integer design problem and three multi-objective problems (Williams–Otto process, new product development in the pharmaceutical industry and economic and environmental study of the HDA process). The results are compared with the ones reported in the literature, and the analysis highlights the efficiency of the proposed algorithms either in the continuous case or in the mixed-integer one.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

AC:

Annual Cost (M$/y)

AP:

Acidification Potential (t SO 2 equivalent/y)

DES:

Discrete Event Simulation

EP:

Eutrophication Potential (t \(\mathit{PO}_{4}^{3_{-}}\) equivalent/y)

FUCA:

Faire Un Choix Adéquat (Making an Adequate Choice)

GA:

Genetic Algorithm

GWP:

Global Warming potential (t CO 2 equivalent/y)

HDA:

HydroDealkylation of toluene

HTP:

Human Toxicity Potential (t C 6 H 6 equivalent/y)

MAOP:

Maximum Allowable Operational Pressure (bar)

MCDM:

Multiple Choice Decision Making

MGA:

Multiobjective Genetic Algorithm

MMS:

Mixed-integer Multiobjective Structural

MOGA:

MultiObjective Genetic Algorithm

MOOP:

MultiObjective Optimization Problem

MOSA:

MultiObjective Simulated Annealing

NG:

Natural Gas

NLP:

NonLinear Programming

NPD:

New Product Development

NPGA:

Niched Pareto Genetic Algorithm

NPV:

Net Present Value (M$)

NPW:

Net Present Worth (M$)

NSGA:

Non-dominated Sorting Genetic Algorithm

PBT:

Profit Before Taxes (M$)

POCP:

Photochemical Ozone Creation Potential (t \(C_{2}H_{4}\) equivalent/y)

TOPSIS:

Technique for Order Preference by Similarity to Ideal Solution

WOP:

Williams Otto Process

References

  1. Abbaspour, M., Chapman, K., Krishnaswami, P.: Nonisothermal compressor station optimization. J. Energy Resour. Tech. 127, 131–141 (2005)

    Article  Google Scholar 

  2. Ang, J., Goh, C., Teoh, E., Mamun, A.: Multi-objective evolutionary recurrent neural networks for system identification. In: Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pp. 1586–1592 (2007)

    Google Scholar 

  3. Azapagic, A., Perdan, S., Clift, R. (eds.): Sustainable Development in Practice: Case Studies for Engineers and Scientists. Wiley, New York (2005)

    Google Scholar 

  4. Babu, B., Angira, R.: Optimal design of an auto-thermal ammonia synthesis reactor. Comput. Chem. Eng. 29(5), 1041–1045 (2005)

    Article  Google Scholar 

  5. Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multiobjective optimization algorithm: Amosa. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)

    Article  Google Scholar 

  6. Blau, G.E., Pekny, J.F., Varma, V.A., Bunch, P.R.: Managing a portfolio of interdependent new product candidates in the pharmaceutical industry. J. Prod. Innovat. Manag. 21(4), 227–245 (2004)

    Article  Google Scholar 

  7. Branke, J., Deb, K., Dierolf, H., Osswald, M.: Finding knees in multi-objective optimization. In: In the Eighth Conference on Parallel Problem Solving from Nature (PPSN VIII). Lecture Notes in Computer Science, pp. 722–731. Springer, Berlin (2004)

    Google Scholar 

  8. Chafekar, D., Xuan, J., Rasheed, K.: Constrained multi-objective optimization using steady state genetic algorithms. In: Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI, GECCO’03, pp. 813–824. Springer, Berlin, Heidelberg (2003)

    Google Scholar 

  9. Chakraborti, N., Mishra, P., Aggarwal, A., Banerjee, A., Mukherjee, S.: The williams and otto chemical plant re-evaluated using a pareto-optimal formulation aided by genetic algorithms. Appl. Soft Comput. 6(2), 189–197 (2006)

    Article  Google Scholar 

  10. Chen, Y., Li, K., Xu, H., Liu, S.: A dea-topsis method for multiple criteria decision analysis in emergency management. J. Syst. Sci. Syst. Eng. 18(4), 489–507 (2009)

    Article  Google Scholar 

  11. Coello, C.A.C., Becerra, R.L.: Evolutionary multi-objective optimization in materials science and engineering. Mater. Manuf. Process. 24(2), 119–129 (2009)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  13. Deb, K., Agrawal, S.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)

    MATH  MathSciNet  Google Scholar 

  14. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  15. Di Bella, C.W., Stevens, W.F.: Process optimization by nonlinear programming. Ind. Eng. Chem. Process Design Dev. 4(1), 16–20 (1965)

    Article  Google Scholar 

  16. Dorigo M., Stützle T.: Ant Colony Optimization. MIT Press, 305 pp., ISBN 0-262-04219-3

    Google Scholar 

  17. Douglas, J.: Conceptual Design of Chemical Process, McGraw-Hill Chemical Engineering Series (1988)

    Google Scholar 

  18. Edgar, T., Himmelblau, D.: Optimization of Chemical Processes. McGraw-Hill, New York (1988)

    Google Scholar 

  19. Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation, and machine learning. Phys. D 2(1–3), 187–204 (1986)

    Article  MathSciNet  Google Scholar 

  20. Fonseca, C.M., Fleming, P.J.: Genetic algortihm for multiobjective optimization: formulation, discusion and generalisation. Proc. 5th Int. Conf. Genetic Algorithm 00, 416–423 (1993)

    Google Scholar 

  21. Gomez, A., Pibouleau, L., Azzaro-Pantel, C., Domenech, S., Latge, C., Haubensack, D.: Multiobjective genetic algorithm strategies for electricity production from generation {IV} nuclear technology. Energy Convers. Manag. 51(4), 859–871 (2010)

    Article  Google Scholar 

  22. Hernandez-Rodriguez, G.: Multiobjective optimization of natural gas transportation networks. Ph.D. thesis, University of Toulouse, INPT, France (2011)

    Google Scholar 

  23. Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)

    Google Scholar 

  24. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, pp. 82–87 (1994)

    Google Scholar 

  25. Jones, D.: Elements of Petroleum Processing (Chapter 14). Wiley, New York (1996)

    Google Scholar 

  26. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  27. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Readings in computer vision: issues, problems, principles, and paradigms. Optimization by Simulated Annealing, pp. 606–615. Morgan Kaufmann, San Francisco (1987)

    Google Scholar 

  28. Konac, A., Coit, D., Smith, A.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91, 992–1007 (2006)

    Article  Google Scholar 

  29. Ksasy, M., Areed, F., Saraya, S., Khalik, M.: Optimal reactor length of an auto-thermal ammonia synthesis reactor. Int. J. Electr. Comput. Sci. 10, 6–15 (2010)

    Google Scholar 

  30. Lee, E., Rangaiah, G., Agrawal, N.: Advances in Process Systems Engineering Vol. 1. Multi-Objective Optimization. Techniques and Applications in Chemical Engineering: Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives, pp. 301–338. World Scientific Publishing, Singapore (2009)

    Google Scholar 

  31. Mansouri, S., Hendizadeh, S., Salmasi, N.: Bicriteria two-machine flowshop scheduling using metaheuristics. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO ’07, pp. 909–909. ACM, New York (2007)

    Google Scholar 

  32. Mendoza, L.F.M., Escobedo, J.L.P., Azzaro-Pantel, C., Pibouleau, L., Domenech, S., Aguilar-Lasserre, A.: Selecting the best portfolio alternative from a hybrid multiobjective ga-mcdm approach for new product development in the pharmaceutical industry. In: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM), pp. 159–166 (2011)

    Google Scholar 

  33. Murase, A., Roberts, H.L., Converse, A.O.: Optimal thermal design of an autothermal ammonia synthesis reactor. Ind. Eng. Chem. Process Des. Dev. 9(4), 503–513 (1970)

    Article  Google Scholar 

  34. Nakrani, S., Tovey, C.: On honey bees and dynamic server allocation in internet hosting centers. Adapt. Behav. Animals Animats Software Agents Robots Adapt. Syst. 12(3–4), 223–240 (2004)

    Google Scholar 

  35. Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.): EMO’07: Proceedings of the 4th International Conference on Evolutionary Multi-criterion Optimization. Springer, Berlin, Heidelberg (2007)

    Google Scholar 

  36. Ouattara, A., Pibouleau, L., Azzaro-Pantel, C., Domenech, S., Baudet, P., Yao, B.: Economic and environmental strategies for process design. Comput. Chem. Eng. 36(0), 174–188 (2012)

    Google Scholar 

  37. Papalexandri, K., Dimkou, T.: A parametric mixed-integer optimization algorithm for multiobjective engineering problems involving discrete decisions. Ind. Eng. Chem. Res. 37(5), 1866–1882 (1998)

    Article  Google Scholar 

  38. Perez-Escobedo, J., Azzaro-Pantel, C., Pibouleau, L.: New product development with discrete event simulation: Application to portfolio management for the pharmaceutical industry. Ind. Eng. Chem. Res. 50(18), 10,615–10,629 (2011)

    Google Scholar 

  39. Perez-Escobedo, J.L., Azzaro-Pantel, C., Pibouleau, L.: Multiobjective strategies for new product development in the pharmaceutical industry. Comput. Chem. Eng. 37(0), 278–296 (2012)

    Google Scholar 

  40. Pettersson, F., Biswas, A., Kumar, P.K.P., Saxen, H., Chakraborti, N.: Analyzing leaching data for low-grade manganese ore using neural nets and multiobjective genetic algorithms. Mater. Manuf. Process. 24(3), 320–330 (2009)

    Article  Google Scholar 

  41. Pettersson, F., Chakraborti, N., Saxen, H.: A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Appl. Soft Comput. 7(1), 387–397 (2007)

    Article  Google Scholar 

  42. Pintaric, Z.N., Kravanja, Z.: Selection of the economic objective function for the optimization of process flow sheets. Ind. Eng. Chem. Res. 45(12), 4222–4232 (2006)

    Article  Google Scholar 

  43. Rangaiah, G.: Advances in Process Systems Engineering. vol. 1., Multi-Objective Optimization, Techniques and Applications in Chemical Engineering. World Scientific Publishing, Singapore (2009)

    Google Scholar 

  44. Rangaiah, G.P.: Studies in constrained optimization of chemical process problems. Comput. Chem. Eng. 9(4), 395–404 (1985)

    Article  Google Scholar 

  45. Rey, T., Isaacs, A., Smith, W.: Advances in Process Systems Engineering, vol. 1. Multi-Objective Optimization. Techniques and Applications in Chemical Engineering: Surrogate Assisted Evolutionary Algorithm for Multi-objective Optimization, pp. 131–151. World Scientific Publishing, Singapore (2009)

    Google Scholar 

  46. Shopova, E.G., Vaklieva-Bancheva, N.G.: Basic - a genetic algorithm for engineering problems solution. Comput. Chem. Eng. 30(8), 1293–1309 (2006)

    Article  Google Scholar 

  47. Shu, L.S., Ho, S.J.H.S.Y., Chen, J.H., Hung, M.H.: A novel multi-objective orthogonal simulated annealing algorithm for solving multi-objective optimization problems with a large number of parameters. In: Deb, K. (ed.) Genetic and Evolutionary Computation GECCO 2004, Lecture Notes in Computer Science, vol. 3102, pp. 737–747. Springer, Berlin, Heidelberg (2004)

    Chapter  Google Scholar 

  48. Smith, K., Everson, R., Fieldsend, J.: Dominance measures for multi-objective simulated annealing. In: Evolutionary Computation, 2004. CEC2004. Congress on, vol. 1, pp. 23–30 (2004)

    Google Scholar 

  49. Srinivas, N., Deb, K.: Multiobjective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2, 221–248 (1994)

    Article  Google Scholar 

  50. Sriram, M., Stevens, W.: An example of the application on nonlinear programming to chemical process optimization. Oper. Res. 21, 269–304 (1973)

    Article  MathSciNet  Google Scholar 

  51. Tabkhi, F., Pibouleau, L., Hernandez-Rodriguez, G., Azzaro-Pantel, C., Domenech, S.: Improving the performance of natural gas pipeline networks fuel consumption minimization problems. AIChE J. 56(4), 946–964 (2010)

    Google Scholar 

  52. Turinsky, P., Keller, P., Abdel-Khalik, H.: Evolution of nuclear fuel management and reactor operational aid tools. Nuclear Eng. Tech. 37, 79–90 (2005)

    Google Scholar 

  53. Upreti, S.R., Deb, K.: Optimal design of an ammonia synthesis reactor using genetic algorithms. Comput. Chem. Eng. 21(1), 87–92 (1996)

    Article  Google Scholar 

  54. Veldhuizen, D.A.V., Lamont, G.B.: Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evol. Comput. 8(2), 125–147 (2000)

    Article  Google Scholar 

  55. de Weck, O.: Multiobjective optimization: History and promise. In: Invited Keynote Paper, GL2-2, The Third China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems, Kanazawa, Japan, pp. 1–14 (2004)

    Google Scholar 

  56. Williams, T., Otto, R.: A generalized chemical processing model for the investigation of computer control. Trans. Am. Inst. Electr. Eng. 709, 451–473 (1960)

    Google Scholar 

  57. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  58. Wu, S., Rios-Mercado, R., Boyd, E., Scott, L.: Model relaxations for the fuel cost minimization of steady-state gas pipeline networks. Math. Comput. Model. 31(2–3), 197–220 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire de Génie Chimique UMR 5503 CNRS/INPT/UPS, Université de Toulouse, ENSIACET; 4, Allée Emile Monso, BP 84234, F-31432, Toulouse, France

    Guillermo Hernandez-Rodriguez, Fernando Morales-Mendoza, Luc Pibouleau, Catherine Azzaro-Pantel & Serge Domenech

  2. Département de Génie Chimique et Agro-Alimentaire, Institut National Polytechnique Houphouet-Boigny, BP 1093, Yamoussoukro, Côte d’Ivoire

    Adama Ouattara

Authors
  1. Guillermo Hernandez-Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Morales-Mendoza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luc Pibouleau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Catherine Azzaro-Pantel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Serge Domenech
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adama Ouattara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Catherine Azzaro-Pantel .

Editor information

Editors and Affiliations

  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

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Hernandez-Rodriguez, G., Morales-Mendoza, F., Pibouleau, L., Azzaro-Pantel, C., Domenech, S., Ouattara, A. (2014). Multi-Objective Genetic Algorithms for Chemical Engineering Applications. In: Valadi, J., Siarry, P. (eds) Applications of Metaheuristics in Process Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06508-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06508-3_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06507-6

  • Online ISBN: 978-3-319-06508-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation