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A Bioreactor Fault Diagnosis Based on Metaheuristics

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Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering

Abstract

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.

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References

  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). https://doi.org/10.1016/j.cherd.2016.08.005

    Article  Google Scholar 

  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. 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. Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005)

    Article  Google Scholar 

  5. Camps Echevarría, L.: Fault diagnosis based on inverse problems. Ph.D. thesis, Instituto Superior Politécnico José Antonio Echeverría (2012)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  13. Ding, S.X.: Model-Based Fault Diagnosis Techniques. Springer, Berlin (2008)

    Google Scholar 

  14. Dorigo, M.: Ottimizzazione, Apprendimento Automatico, Ed Algoritmi Basati su Metafora Naturale. Ph.D. thesis, Politécnico di Milano (1992)

    Google Scholar 

  15. Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  19. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  21. Isermann, R.: Model based fault detection and diagnosis. status and applications. Annu. Rev. Control 29(1), 71–85 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  MathSciNet  Google Scholar 

  26. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution – A Practical Approach to Global Optimization. Springer, Berlin (2005)

    MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  29. Silva Neto, A.J., Llanes Santiago, O., Silva, G.N. (eds.): Mathematical Modelling and Computational Intelligence in Engineering Applications. Springer, Basel (2016)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  31. Simani, S., Fantuzzi, C., Patton, R.J.: Model-Based Fault Diagnosis in Dynamics Systems Using Identifications Techniques. Springer, London (2002)

    Google Scholar 

  32. Socha, K.: Ant colony optimization for continuous and mixed-variable domains. Ph.D. thesis, Universite Libre de Bruxelles (2008)

    Google Scholar 

  33. Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155–1173 (2008)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  39. Xu, A., Zhang, Q.: Nonlinear system fault diagnosis based on adaptive estimation. Automatica 40, 1181–1193 (2004)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Mathematics Department, Universidad Tecnológica de La Habana (Cujae), La Habana, Cuba

    Lídice Camps Echevarría

  2. Automatic and Computing Department, Universidad Tecnológica de La Habana (Cujae), La Habana, Cuba

    Orestes Llanes-Santiago

  3. Department of Mechanical Engineering and Energy, Polytechnic Institute, IPRJ-UERJ, Nova Friburgo, Brazil

    Antônio José Silva Neto

Authors
  1. Lídice Camps Echevarría
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  2. Orestes Llanes-Santiago
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  3. Antônio José Silva Neto
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Editors and Affiliations

  1. Universidade Federal do Rio Grande, Santo Antônio da Patrulha, Rio Grande do Sul, Brazil

    Gustavo Mendes Platt

  2. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  3. Polytechnic Institute, Universidade do Estado do Rio de Janeiro, Nova Friburgo, Rio de Janeiro, Brazil

    Antônio José Silva Neto

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Echevarría, L.C., Llanes-Santiago, O., Silva Neto, A.J. (2019). A Bioreactor Fault Diagnosis Based on Metaheuristics. In: Platt, G., Yang, XS., Silva Neto, A. (eds) Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-96433-1_7

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