Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model

Arsenic is a deleterious heavy metal that is usually removed from polluted water based on adsorption processes. The latest mode of modeling such a process is to implement artificial intelligence (AI). In the current work, a new artificial neural network (ANN) model was developed to predict the adsor...

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Bibliographic Details
Published in:Applied sciences 2022-02, Vol.12 (3), p.999
Main Authors: Al-Yaari, Mohammed, Aldhyani, Theyazn H. H., Rushd, Sayeed
Format: Article
Language:English
Subjects:
adaptive network-based fuzzy inference system (ANFIS)
Adsorbents
Adsorption
Aqueous solutions
Arsenates
Arsenic
Arsenic removal
Artificial intelligence
artificial neural network (ANN)
Correlation coefficient
Correlation coefficients
Datasets
Fuzzy logic
Fuzzy sets
Heavy metals
Inoculum
Machine learning
Membrane separation
Neural networks
Optimization
Pollutant removal
Process parameters
Root-mean-square errors
Water pollution
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Summary:Arsenic is a deleterious heavy metal that is usually removed from polluted water based on adsorption processes. The latest mode of modeling such a process is to implement artificial intelligence (AI). In the current work, a new artificial neural network (ANN) model was developed to predict the adsorption efficiency of arsenate (As(III)) from contaminated water by analyzing different architectures of an adaptive network-based fuzzy inference system (ANFIS). The database for the current study consisted of the experimental data of the adsorption of As(III) by different adsorbents/biosorbents. The data were randomly divided into two sets: 70% for the training phase and 30% for the testing phase. Four statistical evaluation metrics, namely, mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2) were used for the analysis. The best performing ANFIS model was characterized with the average values of 97.72%, 0.9333, 0.137, and 0.274 of R%, R2, MSE, and RMSE, respectively. In addition, a parametric investigation revealed that the most dominating parameters on the adsorption process efficiency were in the following order: pH, As initial concentration, contact time, adsorbent dosage, inoculum size, and temperature. The results of the current study would be useful in the adsorption process scale-up and optimization.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12030999