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Interval Modeling of Riverol-Pilipovik Water Treatment Plant and Its Model Order Reduction

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Computing Algorithms with Applications in Engineering

Abstract

Recently, many engineering systems are modeled as interval systems. In this investigation, an interval model is obtained for Riverol-Pilipovik (RP) water treatment plant. A certain amount of uncertainty is considered in all parameters of different coefficients of the transfer function of RP water treatment plant. After obtaining the interval model for RP water treatment plant, model order reduction of transfer function is also accomplished. For model reduction, matching of time moments and Markov parameters in addition to minimization of errors in between time moments and Markov parameters is accomplished. The minimization is done using Jaya algorithm. The results show that the model is adequately approximating the interval modeled RP water treatment plant.

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References

  1. Choudhary AK, Nagar SK (2018) Order reduction in z-domain for interval system using an arithmetic operator. Circuits Syst Signal Process 1–16

    Google Scholar 

  2. Rathore NS, Singh V, Kumar B (2018) Controller design for Doha water treatment plant using grey wolf optimization. J Intell Fuzzy Syst Prepr 1–8

    Google Scholar 

  3. Rathore N, Chauhan D, Singh V (2015) Luus-jaakola optimization procedure for PID controller tuning in reverse osmosis system. In: International conference on electrical, electronics, and robotics (IRAJ-IACEER 2015)

    Google Scholar 

  4. Chaabene AB, Sellami A (2013) A novel control of a reverse osmosis desalination system powered by photovoltaic generator. In: 2013 International conference on electrical engineering and software applications (ICEESA). IEEE, pp 1–6

    Google Scholar 

  5. Rathore NS, Singh V, Phuc BDH (2019) A modified controller design based on symbiotic organisms search optimization for desalination system. J Water Supply: Res Technol-Aqua

    Article  Google Scholar 

  6. Riverol C, Pilipovik V (2005) Mathematical modeling of perfect decoupled control system and its application: a reverse osmosis desalination industrial-scale unit. J Anal Methods Chem 2005(2):50–54

    Article  Google Scholar 

  7. Rao R (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  8. Singh SP, Prakash T, Singh VP (2019) Coordinated tuning of controller-parameters using symbiotic organisms search algorithm for frequency regulation of multi-area wind integrated power system. Eng Sci Technol Int J

    Google Scholar 

  9. Prakash T, Singh VP, Mohanty SR (2019) A synchrophasor measurement based wide-area power system stabilizer design for inter-area oscillation damping considering variable time-delays. Int J Electr Power Energy Syst 105:131–141

    Article  Google Scholar 

  10. Rathore NS, Singh V (2019) Whale optimisation algorithm-based controller design for reverse osmosis desalination plants. Int J Intell Eng Inform 7(1):77–88

    Google Scholar 

  11. Prakash T, Singh V, Patnana N (2019) Gray wolf optimization-based controller design for two-tank system. In: Applications of artificial intelligence techniques in engineering. Springer, Berlin, pp 501–507

    Google Scholar 

  12. Prakash T, Singh V, Singh SP, Mohanty S (2018) Economic load dispatch problem: quasi-oppositional self-learning TLBO algorithm. Energy Syst 9(2):415–438

    Article  Google Scholar 

  13. Rathore NS, Singh V (2018) Design of optimal PID controller for the reverse osmosis using teacher-learner-based-optimization. Membr Water Treat 9(2):129–136

    MathSciNet  Google Scholar 

  14. Singh V (2017) Sine cosine algorithm based reduction of higher order continuous systems. In: 2017 International conference on intelligent sustainable systems (ICISS). IEEE, pp 649–653

    Google Scholar 

  15. Shrivastava S, Singh VP, Dohare R, Singh SP, Chauhan DPS (2016) PID tuning for position control of dc servo-motor using TLBO. In: National conference on process, automation and control. National Institute of Technology, Jaipur

    Article  Google Scholar 

  16. Singh V, Prakash T, Rathore NS, Singh Chauhan DP, Singh SP (2016) Multilevel thresholding with membrane computing inspired tlbo. Int J Artif Intell Tools 25(06):1650030

    Article  Google Scholar 

  17. Prakash T, Singh VP, Mohanty SR (2018) A novel binary whale optimization algorithm-based optimal placement of phasor measurement units. In: Handbook of research on power and energy system optimization. IGI Global, Hershey, pp 115–138

    Google Scholar 

  18. Singh SP, Prakash T, Singh V, Babu MG (2017) Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm. Eng Appl Artif Intell 60:35–44

    Article  Google Scholar 

  19. Prakash T, Singh V, Singh S, Mohanty S (2017) Binary Jaya algorithm based optimal placement of phasor measurement units for power system observability. Energy Convers Manag 140:34–35

    Google Scholar 

  20. Prakash T, Singh VP (2018) A novel membrane computing inspired Jaya algorithm based automatic generation control of multi-area interconnected power system. Hybrid Metaheuristics: Res Appl 84:89

    Google Scholar 

  21. Singh S, Singh V, Singh V (2019) Analytic hierarchy process based approximation of high-order continuous systems using TLBO algorithm. Int J Dyn Control 7(1):53–60

    Article  MathSciNet  Google Scholar 

  22. Bokam J, Singh V, Raw S (2017) Comments on large scale interval system modelling using Routh approximants

    Google Scholar 

  23. Singh V, Chauhan D, Singh S, Prakash T (2017) On time moments and markov parameters of continuous interval systems. J Circuits Syst Comput 26(03):1750038

    Article  Google Scholar 

  24. Singh V, Chandra D (2012) Model reduction of discrete interval system using clustering of poles. Int J Model Ident Control 17(2):116–123

    Article  Google Scholar 

  25. Singh VP, Chandra D (2012) Reduction of discrete interval systems based on pole clustering and improved Padé approximation: a computer-aided approach. Adv Model Optim 14(1):45–56

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

The work is sponsored under “TEQIP Collaborative Research Scheme” by NPIU, a unit of MHRD, Government of India (CRS application ID: 1-5766329561 and Institute PID: 1-466196671).

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Correspondence to Ramesh Devarapalli .

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Chodavarapu, M.M., Singh, V.P., Devarapalli, R. (2020). Interval Modeling of Riverol-Pilipovik Water Treatment Plant and Its Model Order Reduction. In: Giri, V., Verma, N., Patel, R., Singh, V. (eds) Computing Algorithms with Applications in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2369-4_30

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