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Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine

Zohreh Sheikh Khozani1, Mohammad Ehteram2, Wan Hanna Melini Wan Mohtar3

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PubMed
Summary

A new deep learning model accurately predicts wastewater effluent quality parameters. This advanced system, combining CNN and MKRBFNN with SSA optimization, offers robust wastewater treatment plant monitoring.

Keywords:
Artificial neural networksDeep learningOptimization algorithmsWastewater treatment plant

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Area of Science:

  • Environmental Engineering
  • Artificial Intelligence
  • Water Resource Management

Background:

  • Wastewater treatment plants (WWTPs) are crucial for urban water cycles and pollution reduction.
  • Effective monitoring and control of WWTPs necessitate advanced modeling techniques.
  • Existing models often lack the capability for simultaneous prediction and uncertainty estimation.

Purpose of the Study:

  • To introduce a novel deep learning model for predicting wastewater effluent quality parameters (EQPs).
  • To develop a hybrid model coupling Convolutional Neural Network (CNN) with a Multi-Kernel Radial Basis Function Neural Network (MKRBFNN).
  • To integrate the Salp Swarm Algorithm (SSA) for optimizing model parameters and enhancing feature extraction.

Main Methods:

  • A hybrid CNN-MKRBFNN model was developed for predicting EQPs.
  • The Salp Swarm Algorithm (SSA) was employed to optimize CNN and MKRBFNN parameters.
  • Influent parameters (BOD, COD, TSS, VSS) were used as inputs to predict effluent parameters (COD, BOD, TSS).

Main Results:

  • The CNN-MKRBFNN-SSA model achieved high Nash-Sutcliffe efficiencies: 0.98 for COD, 0.97 for BOD, and 0.98 for TSS.
  • The model demonstrated robust performance in simulating complex wastewater treatment phenomena.
  • Simultaneous prediction and uncertainty estimation of EQPs were successfully achieved.

Conclusions:

  • The proposed CNN-MKRBFNN-SSA model is a powerful tool for WWTP effluent quality prediction.
  • The hybrid deep learning approach offers significant improvements in accuracy and robustness.
  • This methodology enhances the monitoring and control capabilities for wastewater treatment processes.