Deakin University
Browse
nguyen-machinelearningmethodfor-2021.pdf (1.51 MB)

Machine learning method for simulation of adsorption separation: Comparisons of model's performance in predicting equilibrium concentrations

Download (1.51 MB)
Version 2 2024-06-06, 01:05
Version 1 2022-03-17, 21:53
journal contribution
posted on 2024-06-06, 01:05 authored by G Yin, FJI Alazzawi, S Mironov, F Reegu, AS El-Shafay, ML Rahman, CH Su, YZ Lu, Hoang Chinh NguyenHoang Chinh Nguyen
In this work, we implemented different models for predicting adsorption separation of a dye from aqueous solution using porous materials. The equilibrium data of solute concentrations were collected from resources and used in the models for training and verification purposes to develop the models. For prediction of the equilibrium solute concentrations (Ce), we used tree models: Multi-layer Perceptron (MLP), Passive aggressive regression, and Decision Tree (DT) Regressor. In the modeling, we considered the adsorbent dosage as well as solution pH as the input parameters to the model, and the model was able to generate the output values, i.e., equilibrium concentrations based on the input variables. The evaluation of the models’ performances revelated that the final R2 scores are 0.99, 0.98, 0.93 for DT, MLP and Passive-Aggressive, respectively and a very low RMSE of 0.055 for decision tree that shows this model is the best among models used in this study. Indeed, decision tree model is recommended among the other three models to be employed for correlation of adsorption equilibrium data.

History

Journal

Arabian Journal of Chemistry

Volume

15

Article number

103612

Pagination

1-10

Location

Amsterdam, The Netherlands

Open access

  • Yes

ISSN

1878-5352

eISSN

1878-5379

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

3

Publisher

Elsevier

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC