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Optimization of Dechlorination Experiment Design Using Lightweight Deep Learning Model.

Jianghua Peng1, Houzhang Tan1

  • 1School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Computational Intelligence and Neuroscience
|July 5, 2022
PubMed
Summary

This study introduces a lightweight deep learning (DL) model for effective chloride ion removal and concrete durability enhancement. The model accurately predicts chloride diffusion, offering new directions for industrial dechlorination applications.

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Area of Science:

  • Environmental Science
  • Materials Science
  • Computer Science

Background:

  • Chloride ion contamination poses risks to infrastructure durability and the environment.
  • Current dechlorination technologies require optimization for efficiency and environmental protection.

Purpose of the Study:

  • To investigate chloride ion removal using advanced adsorption techniques.
  • To develop and validate a lightweight deep learning (DL) model for predicting chloride diffusion in concrete.

Main Methods:

  • Adsorption and desorption performance analysis of dechlorination adsorbents.
  • Comparative analysis of data statistics.
  • Application of a lightweight DL model to chloride diffusion experiments in slag powder and fly ash concrete.

Main Results:

  • Chloride ion concentration decreased with adsorption time; removal rate increased with temperature.
  • Optimal adsorbent regeneration achieved with 2 mol/L sodium hydroxide.
  • Lightweight DL model demonstrated low error (approx. 0.2) in predicting chloride diffusion in concrete at various curing ages.

Conclusions:

  • The study provides an effective dechlorination strategy using optimized adsorbent regeneration.
  • The lightweight DL model is a feasible tool for predicting chloride ion diffusion in concrete.
  • This research offers a new theoretical basis and optimization direction for industrial dechlorination.