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 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 (R-2) 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%, R-2, 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.

Automated summary

In this study, a new artificial neural network (ANN) model was developed using different architectures of an adaptive network-based fuzzy inference system (ANFIS) to predict the adsorption efficiency of arsenate (As(III)) from polluted water. The results showed that the ANFIS model had high prediction accuracy and identified the dominant factors affecting the adsorption process efficiency.