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• W3201639684 abstract "This study aims at providing a robust artificial intelligent model for predicting the efficiency of heavy metal removal from aqueous solutions of biochar systems with high accuracy and reliability. Not only is it environmentally significant, but it is also a powerful tool for improving biochar adsorption efficiency, reducing the risk of a global water shortage. Accordingly, 22 types of biomass feedstock with a total of 44 biochar systems and 353 experiments, aiming to remove six heavy metal ions (i.e., Cu2+, Pb2+, Zn2+, As3+, Cd2+, and Ni2+) from water were considered and evaluated. Subsequently, an artificial neural network (ANN) model was designed for predicting the heavy metal adsorption efficiency onto these biochar systems. To improve the accuracy of the ANN model, the queuing search algorithm (QSA), a human activities-based algorithm, was applied, aiming to optimize the parameters of the developed ANN model, called the QSA-ANN model. The results showed that the proposed optimization QSA-ANN model provided high accuracy with a root-mean-squared error (RMSE) of 0.051 and 0.074; determination coefficient (R2) of 0.978 and 0.960; variance accounted for (VAF) of 97.707 and 95.882, for the training and testing phases, respectively. Compared to the traditional ANN model, the accuracy of the proposed optimization QSA-ANN model was improved 2.7% on the training dataset and 2.9% on the testing dataset. With an accuracy of 96% in practice, the proposed optimization QSA-ANN model was recommended for practical engineering to predict and improve heavy metal adsorption efficiency onto biochar systems." @default.
• W3201639684 created "2021-09-27" @default.
• W3201639684 creator A5067583663 @default.
• W3201639684 creator A5080411341 @default.
• W3201639684 date "2022-01-01" @default.
• W3201639684 modified "2023-09-24" @default.
• W3201639684 title "A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm" @default.
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• W3201639684 doi "https://doi.org/10.1016/j.chemosphere.2021.132251" @default.
• W3201639684 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34826934" @default.
• W3201639684 hasPublicationYear "2022" @default.
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