Abstract
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.
Highlights
Heavy metals, discharged even at low concentrations into natural water bodies, have threatening impacts on human life and the environment
The adaptive network-based fuzzy inference system (ANFIS) model showed a better performance in processing a small size of training datasets when compared to artificial neural network (ANN) [39]
An efficient ANFIS model was developed based on a database comprised of the experimental measurements published in seven independent studies
Summary
Heavy metals, discharged even at low concentrations into natural water bodies, have threatening impacts on human life and the environment. One of such toxic heavy metals is arsenic (As), which is considered an environmental hazard [1]. Long-term exposure can cause diabetes, pulmonary disease, cardiovascular disease, cancer, and skin lesions [2]. Various technologies, such as coagulation–filtration [3], membrane separation [4,5], ion exchange [6], adsorption [7,8], and hybrid membrane systems [9,10] have been employed to remove As from contaminated water. The adsorption process has long been used in the water and wastewater industries for its ease of handling, minimal sludge production, cost-effectiveness, and regeneration capability [11]
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