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Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

Reddi Kamesh1, K Yamuna Rani1

  • 1Process Dynamics and Control Group, Chemical Engineering Division, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India.

ISA Transactions
|June 26, 2016
PubMed
Summary
This summary is machine-generated.

A new parameterized data-driven fuzzy model accurately controls semi-batch processes. This data-driven fuzzy (PDDF) approach offers optimal control comparable to advanced methods for complex systems.

Keywords:
Chemical engineering, Process control, Parameterized Data-driven fuzzy (PDDF) modelingOptimal controlOrthonormal polynomial approximationsSingle rate and multirate cases

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Area of Science:

  • Chemical Engineering
  • Control Systems Engineering
  • Computational Intelligence

Background:

  • Semi-batch processes exhibit complex nonlinear and time-varying dynamics.
  • Accurate modeling is crucial for effective process control and optimization.
  • Existing data-driven models may struggle with the inherent complexities of these systems.

Purpose of the Study:

  • To propose a novel Parameterized Data-Driven Fuzzy (PDDF) model structure for semi-batch processes.
  • To apply the PDDF model for optimal control strategy development.
  • To evaluate the performance of the PDDF model against established methods.

Main Methods:

  • Developed a PDDF model using orthonormally parameterized inputs, initial states, and process parameters.
  • Fuzzy rules were derived from a linear data-driven model, with defuzzification using linear regression.
  • Applied the fuzzy model to formulate optimal control problems for single and multi-rate systems.

Main Results:

  • The PDDF model accurately captured the nonlinear and time-varying behavior of a multivariable semi-batch reactor.
  • Optimal control results using the PDDF model were comparable to those from an exact first principles model.
  • Performance was also found to be comparable to or superior to optimization results from a data-driven artificial neural network model.

Conclusions:

  • The proposed PDDF modeling approach provides an effective tool for semi-batch process analysis and control.
  • PDDF modeling offers a competitive alternative to first principles and artificial neural network models for complex process optimization.
  • This approach demonstrates significant potential for improving the efficiency and performance of semi-batch operations.