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Related Experiment Video

Updated: Dec 11, 2025

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Neuroevolutive Algorithms Applied for Modeling Some Biochemical Separation Processes.

Silvia Curteanu1, Elena-Niculina Dragoi2, Alexandra Cristina Blaga2

  • 1Faculty of Chemical Engineering and Environmental Protection "Cristofor Simionescu", "Gheorghe Asachi" Technical University of Iasi, Iasi, Romania. silvia_curteanu@yahoo.com.

Methods in Molecular Biology (Clifton, N.J.)
|August 18, 2020
PubMed
Summary

Neuroevolution combines artificial neural networks with bioinspired methods for process optimization. Differential Evolution, a key algorithm, efficiently solves complex biochemical separation problems like folic acid extraction and vitamin C pertraction.

Keywords:
Artificial neural networksDifferential evolution algorithmPertractionReactive extraction

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Area of Science:

  • Computational intelligence
  • Biochemical engineering
  • Artificial intelligence

Background:

  • Artificial neural networks (ANNs) face challenges in topology determination and training.
  • Evolutive and bioinspired approaches offer solutions for ANNs and process optimization.
  • Neuroevolution integrates ANNs with bioinspired algorithms for enhanced problem-solving capabilities.

Purpose of the Study:

  • To discuss the primary mechanisms employed in neuroevolution.
  • To demonstrate the efficacy of neuroevolutionary tools through biochemical separation examples.
  • To highlight the application of Differential Evolution within neuroevolutionary procedures.

Main Methods:

  • Neuroevolutionary procedures integrating artificial neural networks with bioinspired algorithms.
  • Differential Evolution (DE) as the bioinspired metaheuristic.
  • Case studies involving reactive extraction of folic acid and pertraction of vitamin C.

Main Results:

  • Neuroevolutionary approaches effectively address challenges in neural network topology and training.
  • Differential Evolution demonstrates significant potential in solving diverse problems.
  • The studied neuroevolutionary methods proved efficient for biochemical separation processes.

Conclusions:

  • Combining ANNs with bioinspired methods (neuroevolution) is a powerful technique for process optimization.
  • Differential Evolution is a highly effective metaheuristic for neuroevolutionary applications.
  • Neuroevolution shows great promise for complex biochemical separation tasks.