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Swarm Intelligence for Estimating Model Parameters in Thermodynamic Systems

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Modeling, Simulation and Optimization (CoMSO 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 373))

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Abstract

Parameter estimation from VLE data has drawn ample amount of attention in nonlinear vapor–liquid thermodynamic modeling problems. Traditional optimization methods are very sensitive to the initial guesses of unknown parameters and often fail to converge to the global optimum of the parameter estimation nonlinear mathematical programming problems. In this work, we demonstrate the application of a swarm intelligence-based algorithm called the Particle Swarm Optimization (PSO) algorithm. It can solve efficiently the nonlinear parameter estimation problems and finds the global optimum with high probability. Furthermore, it is not sensitive to the initial estimates of unknown parameters. PSO is easy to execute and is experimentally proven to perform well on many optimization problems. The results obtained using PSO are compared with those reported in literature. Also, the new results obtained using PSO are presented.

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Acknowledgements

Financial support from the Guru Gobind Singh Indraprastha University is gratefully acknowledged. The work has been supported under Faculty Research Grant Scheme (FRGS) for the year 2022-23 (F. No. GGSIPU/FRGS/2022/1223/35).

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Authors and Affiliations

  1. Process Systems Engineering Laboratory, University School of Chemical Technology, Guru Gobind Singh Indraprastha University, New Delhi, 110078, India

    Swati Yadav, Pragya Palak & Rakesh Angira

Authors
  1. Swati Yadav
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  2. Pragya Palak
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  3. Rakesh Angira
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Corresponding author

Correspondence to Rakesh Angira .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Silchar, Cachar, Assam, India

    Biplab Das

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Cachar, Assam, India

    Ripon Patgiri

  3. National Institute of Technology Silchar, Cachar, India

    Sivaji Bandyopadhyay

  4. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  5. Department of Mechanical Engineering, Curtin University, Miri, Malaysia

    Sukanta Roy

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Yadav, S., Palak, P., Angira, R. (2024). Swarm Intelligence for Estimating Model Parameters in Thermodynamic Systems. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E., Roy, S. (eds) Modeling, Simulation and Optimization. CoMSO 2022. Smart Innovation, Systems and Technologies, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-99-6866-4_21

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