A Bioreactor Fault Diagnosis Based on Metaheuristics

  • Lídice Camps Echevarría
  • Orestes Llanes-Santiago
  • Antônio José Silva Neto


Fault Diagnosis is a very important issue in the industry. Some essential topics in the industry, e.g. reliability, safety, efficiency, and maintenance, depend on the correct diagnosis of systems. Robustness in relation to external disturbances, which may affect the system, sensible to incipient faults, and a proper diagnosis time are desired characteristics of the diagnosis, in order to prevent propagation of faults. In the particular case of the chemical and biochemical industries, the use of nonlinear bioreactors is common. Therefore, the diagnosis of these systems is of high importance for both industries. This chapter presents the application of three metaheuristics, Ant Colony Optimization with Dispersion (ACO-d), Differential Evolution with Particle Collisions (DEwPC), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), in the diagnosis of a nonlinear bioreactor through a Fault Detection and Isolation (FDI) inverse problem approach. This technique deals with the solution of an optimization problem, which is solved with the help of these three metaheuristics. The analysis of the quality of the diagnosis is based on the robustness and diagnosis time. Furthermore, the results are compared with other reported ones in the literature. The main contributions of this chapter are, at first, a proposal for collecting information regarding the quality of the diagnosis based on the FDI inverse problem approach and the use of metaheuristics, as well as the organization of this information in tables. Furthermore, it is shown how to improve the stopping criteria of the metaheuristics, when they are applied to FDI inverse problems.



The authors acknowledge the Brazilian Research supporting agencies CAPES—Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CNPq—Conselho Nacional de Desenvolvimento Científico e Tecnológico, and FAPERJ—Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, as well as UERJ, Universidade do Estado do Rio de Janeiro and CUJAE, Universidad Tecnológica de La Habana José Antonio Echeverría.


  1. 1.
    Acosta Díaz, C., Camps Echevarría, L., Prieto Moreno, A., Silva Neto, A.J., Llanes Santiago, O.: A model-based fault diagnosis in a nonlinear bioreactor using an inverse problem approach. Chem. Eng. Res. Des. 114, 18–29 (2016). CrossRefGoogle Scholar
  2. 2.
    Becceneri, J.C., Zinober, A.: Extraction of energy in a nuclear reactor. In: XXXIII Brazilian Symposium on Operational Research, Campos do Jordão (2001)Google Scholar
  3. 3.
    Becceneri, J.C., Sandri, S., Luz, E.F.P.: Using ant colony systems with pheromone dispersion in the traveling salesman problem. In: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence, Sant Martí d’Empúries (2008)Google Scholar
  4. 4.
    Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005)CrossRefGoogle Scholar
  5. 5.
    Camps Echevarría, L.: Fault diagnosis based on inverse problems. Ph.D. thesis, Instituto Superior Politécnico José Antonio Echeverría (2012)Google Scholar
  6. 6.
    Camps Echevarría, L., Llanes Santiago, O., Silva Neto, A.J.: A proposal to fault diagnosis in industrial systems using bio-inspired strategies. Ingeniare. Rev. Chil. Ing. 19(2), 240–252 (2011)CrossRefGoogle Scholar
  7. 7.
    Camps Echevarría, L., Llanes Santiago, O., Silva Neto, A.J.: Fault diagnosis based on inverse problem solution. In: 2011 International Conference on Inverse Problems in Engineering (ICIPE), Orlando (2011)Google Scholar
  8. 8.
    Camps Echevarría, L., Llanes Santiago, O., Silva Neto, A.J., Campos Velho, H.F.: An approach of fault diagnosis using meta-heuristics: a new variant of the differential evolution algorithm. Revista Computación y Sistemas (2012)Google Scholar
  9. 9.
    Camps Echevarría, L., Llanes Santiago, O., Silva Neto, A.J., Campos Velho, H.F.: Meta heuristics in the faults diagnosis: modification of the algorithm differential evolution. In: 2nd International Conference on Computational and Informatics Sciences, Havana (2013)Google Scholar
  10. 10.
    Camps Echevarría, L., Silva Neto, A.J., Llanes Santiago, O., Hernández Fajardo, J.A., Saánchez, D.J.: A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation. Eng. Appl. Artif. Intell. 28, 36–51 (2014)CrossRefGoogle Scholar
  11. 11.
    Camps Echevarría, L., Campos Velho, H.F., Becceneri, J.C., Silva Neto, A.J., Llanes Santiago, O.: The fault diagnosis inverse problem with ant colony optimization and fuzzy ant colony optimization. Appl. Math. Comput. 227(15), 687–700 (2014)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Contois, D.: Kinetics of bacteria growth relationship between population density and specific growth rate of continuous cultures. J. Genet. Macrobiol. 21, 40–50 (1959)CrossRefGoogle Scholar
  13. 13.
    Ding, S.X.: Model-Based Fault Diagnosis Techniques. Springer, Berlin (2008)Google Scholar
  14. 14.
    Dorigo, M.: Ottimizzazione, Apprendimento Automatico, Ed Algoritmi Basati su Metafora Naturale. Ph.D. thesis, Politécnico di Milano (1992)Google Scholar
  15. 15.
    Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  17. 17.
    Frank, P.M.: Analytical and qualitative model-based fault diagnosis – a survey and some new results. Eur. J. Control 2(1), 6–28 (1996)CrossRefGoogle Scholar
  18. 18.
    Gauthier, J.P., Hammouri, H., Othman, S.: A simple observer for nonlinear systems, application to bioreactors. IEEE Trans. Autom. Control 37(6), 875–880 (1992)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)CrossRefGoogle Scholar
  20. 20.
    Hansen, N., Mueller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation CMA-ES. Evol. Comput. 11(1), 1–18 (2003)CrossRefGoogle Scholar
  21. 21.
    Isermann, R.: Model based fault detection and diagnosis. status and applications. Annu. Rev. Control 29(1), 71–85 (2005)CrossRefGoogle Scholar
  22. 22.
    Isermann, R., Ballé, P.: Trends in the application of model-based fault detection and diagnosis of technical processes. Control Eng. Pract. 5, 709–719 (1997)CrossRefGoogle Scholar
  23. 23.
    Knupp, D.C., Silva Neto, A.J., Sacco, W.F.: Estimation of radiactive properties with the particle collision algorithm. In: Inverse Problems, Design and Optimization Symposium, Miami (2007)Google Scholar
  24. 24.
    Mezura-Montes, E., Velázquez-Reyes, J., Coello-Coello, C.A.: A comparative study of differential evolution variants for global optimization. In: GECCO 06, Seattle, Washington (2006)Google Scholar
  25. 25.
    Pavlidis, N.G., Parsopoulos, K.E., Vrahatis, M.N.: Computing Nash equilibria through computational intelligence methods. J. Comput. Appl. Math. 175(1), 113–136 (2005)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution – A Practical Approach to Global Optimization. Springer, Berlin (2005)zbMATHGoogle Scholar
  27. 27.
    Sacco, W.F., Oliveira, C.R.E.: A new stochastic optimization algorithm based on particle collisions. In: 2005 ANS Annual Meeting, Transactions of the American Nuclear Society (2005)Google Scholar
  28. 28.
    Sacco, W.F., Oliveira, C.R.E., Pereira, C.M.N.A.: Two stochastic optimization algorithms applied to nuclear reactor core design. Prog. Nucl. Energy 48(6), 525–539 (2006)CrossRefGoogle Scholar
  29. 29.
    Silva Neto, A.J., Llanes Santiago, O., Silva, G.N. (eds.): Mathematical Modelling and Computational Intelligence in Engineering Applications. Springer, Basel (2016)zbMATHGoogle Scholar
  30. 30.
    Simani, S., Patton, R.J.: Fault diagnosis of an industrial gas turbine prototype using a system identification approach. Control. Eng. Pract. 16(7), 769–786 (2008)CrossRefGoogle Scholar
  31. 31.
    Simani, S., Fantuzzi, C., Patton, R.J.: Model-Based Fault Diagnosis in Dynamics Systems Using Identifications Techniques. Springer, London (2002)Google Scholar
  32. 32.
    Socha, K.: Ant colony optimization for continuous and mixed-variable domains. Ph.D. thesis, Universite Libre de Bruxelles (2008)Google Scholar
  33. 33.
    Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155–1173 (2008)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Stephany, S., Becceneri, J.C., Souto, R.P., Campos Velho, H.F., Silva Neto, A.J.: A pre-regularization scheme for the reconstruction of a spatial dependent scattering albedo using a hybrid ant colony optimization implementation. Appl. Math. Model. 34(3), 561–572 (2010)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Storn, R., Price, K.: Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute (1995)Google Scholar
  36. 36.
    Storn, R., Price, K.: Differential evolution – a simple and efficient adaptive heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)CrossRefGoogle Scholar
  37. 37.
    Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real parameter optimization. Technical Report, Nanyang Technological University (2005)Google Scholar
  38. 38.
    Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis. Part 1: quantitative model-based methods. Comput. Chem. Eng. 27, 293–311 (2002)CrossRefGoogle Scholar
  39. 39.
    Xu, A., Zhang, Q.: Nonlinear system fault diagnosis based on adaptive estimation. Automatica 40, 1181–1193 (2004)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lídice Camps Echevarría
    • 1
  • Orestes Llanes-Santiago
    • 2
  • Antônio José Silva Neto
    • 3
  1. 1.Mathematics DepartmentUniversidad Tecnológica de La Habana (Cujae)La HabanaCuba
  2. 2.Automatic and Computing DepartmentUniversidad Tecnológica de La Habana (Cujae)La HabanaCuba
  3. 3.Department of Mechanical Engineering and EnergyPolytechnic Institute, IPRJ-UERJNova FriburgoBrazil

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