Modelling Treatment by Adsorption from Waste Water Using Artificial Neural Network and Multiple Linear Regressions (MLR) Approaches

Authors

  • Meriem. Sediri Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Médéa, Ain D’Heb 26000, Médéa, Algeria.
  • Salah. Hanini Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Médéa, Ain D’Heb 26000, Médéa, Algeria.
  • Maamar. Laidi Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Médéa, Ain D’Heb 26000, Médéa, Algeria.
  • Siham. Abbas Turki Department of Electrics and Computing Engineering, University of Médéa, Ain D’Heb 26000, Médéa, Algeria.
  • Hakima. Cherifi Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Médéa, Ain D’Heb 26000, Médéa, Algeria.
  • Hamadache. Mabrouk Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Médéa, Ain D’Heb 26000, Médéa, Algeria.

DOI:

https://doi.org/10.9734/bpi/cmsdi/v5/1932

Keywords:

Dynamic adsorption, adsorbent–adsorbate, modelling, MLP-ANN, MLR

Abstract

Artificial neural networks (MLP-ANN) and multiple linear regressions (MLR) models were used for predicting the dynamic adsorption of the complex system of adsorbent adsorbate in the solid-liquid phase. A structure of 09 neurons in the input layer, 16 neurons in the hidden layer, and 1 neuron in the output layer was built.

According to the statistically obtained result for the ANN model in terms of root mean square error (RMSE= 0.0521) and correlation coefficient (R = 0.991), the ANN presents a powerful tool and gives more significant results than the MLR model.

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Published

2024-08-23

How to Cite

Meriem. Sediri, Salah. Hanini, Maamar. Laidi, Siham. Abbas Turki, Hakima. Cherifi, & Hamadache. Mabrouk. (2024). Modelling Treatment by Adsorption from Waste Water Using Artificial Neural Network and Multiple Linear Regressions (MLR) Approaches. Chemical and Materials Sciences: Developments and Innovations Vol. 5, 136–148. https://doi.org/10.9734/bpi/cmsdi/v5/1932

Issue

Chemical and Materials Sciences: Developments and Innovations Vol. 5

Section

Chapters