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<p>Steam turbine is pivotal in industrial and power generation because it has become an essential technology for optimization of energy that efficiently converts thermal energy from steam into mechanical energy. This manuscript proposes a predictive modelling of steam turbine by invoking artificial neural network analysis and develops dynamic mathematical model for steam turbine via multiple fractional techniques. A novel mathematical model for steam turbine is developed by means modern fractional differentials and artificial neural network subject to steady-state condition for high efficiency and long operation. For seeking the transfer function, mathematical techniques of Laplace and Sumudu transforms, rectified linear function, mean squared error have been invoked to have estimation for ratio of output to input as a dynamic response. At last, the comparison of best validation performance versus epoch, absolute error versus pressure, training dynamics versus epoch, error histogram versus targets and actual scale versus predicted scale in terms of fit have been deeply discussed for the efficiency of steam turbine.</p>

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Comparative behavior of steam turbine model for dynamical power system analyses by means of multiple fractional and artificial neural network techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Kashif Ali Abro,
  • Basma Souayeh,
  • Aymen Flah

摘要

Steam turbine is pivotal in industrial and power generation because it has become an essential technology for optimization of energy that efficiently converts thermal energy from steam into mechanical energy. This manuscript proposes a predictive modelling of steam turbine by invoking artificial neural network analysis and develops dynamic mathematical model for steam turbine via multiple fractional techniques. A novel mathematical model for steam turbine is developed by means modern fractional differentials and artificial neural network subject to steady-state condition for high efficiency and long operation. For seeking the transfer function, mathematical techniques of Laplace and Sumudu transforms, rectified linear function, mean squared error have been invoked to have estimation for ratio of output to input as a dynamic response. At last, the comparison of best validation performance versus epoch, absolute error versus pressure, training dynamics versus epoch, error histogram versus targets and actual scale versus predicted scale in terms of fit have been deeply discussed for the efficiency of steam turbine.