Soft Sensors to Monitoring a Multivariate Nonlinear Process Using Neural Networks

  • Nathalia Arthur Brunet Monteiro
  • Jaidilson Jó da Silva
  • José Sérgio da Rocha Neto


In general, industrial processes have a multivariable nature, with multiple inputs and multiple outputs. Such systems are more difficult to monitor and control due to interactions between the input and output variables. Focusing on these issues, the development of soft sensors to monitor multivariate nonlinear processes using neural networks is proposed. Experiments were performed to monitor the pressure and flow values on an experimental platform (fluid transport system) using developed soft sensors. With the monitoring using soft sensor, it is possible to make processes more reliable, with better performance and with less difficulty in detecting and solving possible failures.


Monitoring Modeling Neural networks Soft sensor System identification 



The authors would like to thank CNPq and Copele-DEE for financial support.


  1. Anthony, E. J., Talbot, R. E., Jia, L., & Granatstein, D. L. (2000). Agglomeration and fouling in three industrial petroleum coke- red cfbc boilers due to carbonation and sulfation. Energy & Fuels, 14, 1021–1027.CrossRefGoogle Scholar
  2. Arsie, I., Cricchio, A., Cesare, M. D., Lazzarini, F., Pianese, C., & Sorrentino, M. (2017). Neural net-work models for virtual sensing of NOx emissions in automotive diesel engines with least square-based adaptation. Control Engineering Practice, 61, 11–20.CrossRefGoogle Scholar
  3. Buondonno, G. & Luca, A. D. (2016), Combining real and virtual sensors for measuring interaction forces and moments acting on a robot. In International conference on intelligent robots and systems (IROS). Google Scholar
  4. Corriou, J.-P. (2018). Process control: Theory and applications (2nd ed.). Cham: Springer.CrossRefGoogle Scholar
  5. Cybenko, G. (1989). Approximation by superpositions of a sigmoid function. Mathematics of Control, Signals, and Systems, 2(4), 303–314.MathSciNetCrossRefzbMATHGoogle Scholar
  6. Ell, S. M., & Trabachini, A. (2011). Loss of charge in forced conduits. Retrieved January 18, 2018 from
  7. Engelbrecht, A. P. (2007). Computational intelligence—An introduction (2nd ed.). Pretoria: Wiley, University of Pretoria South Africa.CrossRefGoogle Scholar
  8. Fortuna, L., Graziani, S., & Xibilia, M. G. (2007). Soft sensor for monitoring and control of industrial processes. London: Editora Springer.zbMATHGoogle Scholar
  9. Fox, R. W., McDonald, A. T., & Pritchard, P. J. (2017). Introduction to Fluid Mechanics, 8 edn. India: Wiley.zbMATHGoogle Scholar
  10. Haykin, S. (2009). Neural networks and learning machines (3rd ed.). Ontario: Prentice Hall.Google Scholar
  11. Joseph, B., & Brosilow, C. (1978). Inferential control of processes: Part i, ii e iii. American Institute of Chemical Engineers (AIChE Journal), 24, 485–509.CrossRefGoogle Scholar
  12. Kadlec, P., Gabrys, B., & Strandt, S. (2008). Data-driven soft sensors in the process industry. Computers & Chemical Engineering, 33, 795–814.CrossRefGoogle Scholar
  13. Kah, P., Layus, P., Hiltunen, E., & Martikainen, J. (2014). Real-time weld process monitoring. Advanced Materials Research, 933, 117–124.CrossRefGoogle Scholar
  14. Liu, L., Chen, J. & Xu, L. (2008). Realization and application research of BP neural network based on MATLAB. In International seminar on future biomedical information engineering.Google Scholar
  15. Lopes, A. M., Lapa, J. P. & Oliveira, L. A. (2006). Turbulent laminar regime transition unit—practical workbook. Retrieved January 18, 2018 from
  16. Mansano, R. K., Godoy, E. P., & Porto, A. J. V. (2014). The bene ts of soft sensor and multi-rate control for the implementation of wireless networked control systems. Sensors, 14, 24441–24461.CrossRefGoogle Scholar
  17. Mansoori, G. (2001), Deposition and fouling of heavy organic oils and other compounds. In 9th International conference on properties an phases equilibria for product and process design.Google Scholar
  18. Markopoulos, A. P., Georgiopoulos, S., & Manolakos, D. E. (2016). On the use of back propagation and ra-dial basis function neural networks in surface rough-ness prediction’. Journal of Industrial Engineering International, 12, 389–400.CrossRefGoogle Scholar
  19. Marques, J. A. A. S. & Sousa, J. J. O. (1997). Formula of colebrook—white: old but current. explicit solutions. In 3rd Symposium on hydraulics and water re-sources in Portuguese-speaking Countries (Silusba).Google Scholar
  20. Melo, T. R., Bezerra, M. M., da Silva, J. J., & da Rocha Neto, J. S. (2017). Implementation of a decentralized pid control system on an experimental platform using labview. Latin America Transactions, 15, 213–218.CrossRefGoogle Scholar
  21. Ortega, E. (2012). Calculation of the friction energy.\_fator\_atrito.ppt.
  22. Palcios, R. H. C., da Silva, I. N., Goedtel, A., Godoy, W. F., & Oleskovicz, M. (2014). A robust neural method to estimate torque in three-phase induction motor. Journal of Control, Automation and Electrical Systems, 25, 493–502.Google Scholar
  23. Samarasinghe, S. (2006). Neural Networks for Applied Sciences and Engineering. Boca Raton: Auerbach Publications.CrossRefzbMATHGoogle Scholar
  24. Saptoro, A. (2014). State of the art in the develop-ment of adaptive soft sensors based on just-in-time models’. International Conference and Workshop on Chemical Engineering, 9, 226–234.Google Scholar
  25. Severson, K., Chaiwatanodom, P., & Braatz, R. D. (2015). Perspectives on process monitoring of industrial systems’. International Federation of Automatic Control (IFAC), 48, 931–939.Google Scholar
  26. Tiwari, S. K., & Kaur, G. (2017). Model reduction by new clustering method and frequency response matching’. Journal of Control, Automation and Electrical Systems, 28, 78–85.CrossRefGoogle Scholar
  27. Wang, H., Oh, Y., & Yoon, E. S. (1998). Strategies for modeling and control of nonlinear chemical processes using neural networks’. Computers & Chemical Engineering, 22, 832–862.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringFederal University of Campina Grande (UFCG)Campina GrandeBrazil

Personalised recommendations