Advertisement

Case Studies of Smart Algorithm for Industrial Process Control

  • X. Anitha MaryEmail author
  • Lina Rose
  • R. Jegan
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 374)

Abstract

Smart algorithm has a critical role in determining the tuning parameters of controller in process industry. Gasifier is a four-input and four-output system with high degree of interconnections. It is mandatory to design a controller for gasifier with the specified input and output limits. The first section deals with the controller design for gasifier with genetic algorithm optimization. The major requirement of any industrial process is to control the output to obtain the desired result. The problems faced by using the analogue controller can be removed using a digital controller when there is a significant dead time in the process. Even though digital controllers are preferred over analogue controllers for a précised output, the search for particular performance matrices would end up with optimized outputs. Such a system for temperature control is studied using optimized digital controller, which gives an eye to major application in control field, and is demonstrated and detailed in second case study. Section 14.3 deals with the development of smart controller for conical tank. In the process control, the designing of controller for liquid level in tanks and the flow between the tanks is a major task faced by the engineers. If the tanks are interconnected, the level or flow parameters which are above the set point may cause system to unstable condition. Thus the control of such parameters is very crucial in control engineering field.

Keywords

Gasifier Genetic algorithm PID controller Conical tank NGIC PSO Temperature process control 

References

  1. 1.
    Dixon, R., Pike, A.W.: ALSTOM benchmark challenge ii on gasifier control. IEE Proc.-Control Theory Appl. 153(3), 254–261 (2006)CrossRefGoogle Scholar
  2. 2.
    Mary, X.A., Sivakumar, L., Jayakumar, J.: Modeling and control of MIMO gasifier system during coal quality variations. Int. J. Model. Identif. Control (Inderscience Publication) 22(4), 131–139 (2014)CrossRefGoogle Scholar
  3. 3.
    Mary, X.A., Sivakumar, L., Jayakumar, J.: Design of PID filter controller with genetic algorithm for MIMO system in modern power generation. Mod. Appl. Sci. 8(5) 186–196 (2014). ISSN 1913-1844, E-ISSN 1913-1852, Published by Canadian Center of Science and EducationGoogle Scholar
  4. 4.
    Mary, X.A., Sivakumar, L., Jayakumar, J.: Comparative performance evaluation of model reduction techniques for complex non-linear system. Int. J. Eng. Technol. 5(6), 4804–4814 (2014)Google Scholar
  5. 5.
    Sivakumar, L., Anithamary, X.: In: Yongseung, Y. (ed.) Lower Order Modeling and Control of Alstom Fluidized Bed Gasifier, Gasification for Practical Applications (InTech) (2012). ISBN: 978-953-51-0818-4,  https://doi.org/10.5772/48674Google Scholar
  6. 6.
    Pike, A.W., Donne, M.S., Dixon, R.: Dynamic modelling and simulation of the air blown gasification cycle prototype publication, pp. 354–361 (university, York) (1998)Google Scholar
  7. 7.
    Xue, Y., Li, Donghai, Gao, F.: Multi-objective optimization and se-lection for the PI control of ALSTOM gasifier problem. Control Eng. Pract. 18(1), 67–76 (2010)CrossRefGoogle Scholar
  8. 8.
    Simm, A., Liu, G.P.: Improving the performance of the ALSTOM baseline controller using multiobjective optimisation. IEE Proc.-Control Theory Appl. 153(3), 286292 (2006)CrossRefGoogle Scholar
  9. 9.
    Sivakumar, L., Kotteeswaran, R.: Soft computing based partial-retuning of decentralized PI Controller of nonlinear multivariable process, ICT and critical infrastructure. In: Proceedings of the 48th Annual Convention of Computer Society of India- Volume I, Advances in Intelligent Systems and Computing, vol. 248, pp. 117–124 (2014)Google Scholar
  10. 10.
    Al Seyab, R.K., Cao, Y.: Nonlinear model predictive control for the ALSTOM Gasifier. J. Process. Control 16(8), 795–808 (2006)CrossRefGoogle Scholar
  11. 11.
    Taylor, C.J., Shaban, E.M.: Multivariable proportional-integralplus (PIP) control of the ALSTOM nonlinear gasifier simulation. IEE Proc. Control Theory Appl. 153(3), 277–285 (2006)CrossRefGoogle Scholar
  12. 12.
    Wilson, J.A., Chew, M., Jones, W.E.: A state estimation based ap-proach to gasifier control. IEE Proc.-Control Theory Appl. 153(3), 268–276 (2006)CrossRefGoogle Scholar
  13. 13.
    Nobakhti, A., Wang, H.: A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier. Appl. Soft Comput. 8(1), 350–370 (2008)CrossRefGoogle Scholar
  14. 14.
    Mary, X.A., Sivakumar. L.: A reduced order transfer function models for alstom gasifier using genetic algorithm. Int. J. Comput. Appl. (0975 8887) 46(5), 1–6 (2012)Google Scholar
  15. 15.
    Sivakumar, L., Mary, X.A.: A low order transfer function model for MIMO ALSTOM gasifier. IEEE International Conference on Process Modelling, Control and Automation, Coimbatore Institute of Technology, Coimbatore, India, July 2011 (2011)Google Scholar
  16. 16.
    Mary, X.A.: Genetic-algorithm-based performance optimization for non-linear MIMO system. Appl. Comput. Intell. Soft Comput. Eng. 35 (2018)Google Scholar
  17. 17.
    Rusia, P., Bhongade, S.: Control and implementation of digital pid controller using FPGA for precision temperature control. In: IEEE 2014Google Scholar
  18. 18.
    Pires, D.S., Luiz de Oliveira Serra, G.: Fuzzy digital PID controller design based on robust stability criteria. In: IEEE 2014Google Scholar
  19. 19.
    Samata, B. Dr.: Geogia southern university, a controller implementation in FPGA using LabVIEW environment. In: 120th Annual Conference and Exposition, American Society of Engineering Education (2013)Google Scholar
  20. 20.
    Nayak, A., Singh, M.: Study of tuning of PID controller by particle swarm optimization. Int. J. Adv. Eng. Res. Stud. (2015)Google Scholar
  21. 21.
    Marshiana, D., Thirusakthimurugan, P.: Research scholar, Sathyabama University, Chennai, India, Professor, Pondicherry Engineering College, Pondicherry, India Design of Deadbeat Algorithm for a Nonlinear Conical tank system. Sci. Direct Procedia Comput. Sci. 57, 1351–1358 (2015)CrossRefGoogle Scholar
  22. 22.
    Kapil Arasu, S., Panda, A., Prakash, J.: Department of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai-44. India. Experimental validation of a Nonlinear Model based Control scheme on the Variable Area Tank Process Science direct IFAC-Papers Online 49-1 030034 (2016)Google Scholar
  23. 23.
    Lia, H., Lee, L.-W., Chiang, H.-H., Chen, P.-C.: Intelligent switching adaptive control for uncertain non-linear dynamical systems. Appl. Soft Comput. 34, 638654 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Karunya Institute of Technology and SciencesCoimbatoreIndia

Personalised recommendations