Estimating heavy metals absorption efficiency in an aqueous solution using nanotube-type halloysite from weathered pegmatites and a novel Harris hawks optimization-based multiple layers perceptron neural network

In this study, nanotube-type halloysites from weathered pegmatites were investigated to absorb Pb 2+ in an aqueous solution. Also, a novel hybrid intelligent model based on the multiple layers perceptron (MLP) neural network and the Harris hawks optimization (HHO) algorithm (i.e., HHO-MLP neural net...

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Bibliographic Details
Published in:Engineering with computers 2022-12, Vol.38 (Suppl 5), p.4257-4272
Main Authors: Bac, Bui Hoang, Nguyen, Hoang, Thao, Nguyen Thi Thanh, Hanh, Vo Thi, Duyen, Le Thi, Dung, Nguyen Tien, Du, Nguyen Khac, Hiep, Nguyen Huu
Format: Article
Language:English
Subjects:
Absorption
Algorithms
Aqueous solutions
CAE) and Design
Calculus of Variations and Optimal Control
Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Efficiency
Heavy metals
Hybrid systems
Lead
Math. Applications in Chemistry
Mathematical and Computational Engineering
Nanotubes
Neural networks
Optimization
Original Article
Systems Theory
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Summary:In this study, nanotube-type halloysites from weathered pegmatites were investigated to absorb Pb 2+ in an aqueous solution. Also, a novel hybrid intelligent model based on the multiple layers perceptron (MLP) neural network and the Harris hawks optimization (HHO) algorithm (i.e., HHO-MLP neural network) was proposed for estimating the absorption of Pb 2+ from an aqueous solution using this novel material. XRD, SEM–EDS, and TEM analysis revealed the existence of overlapping tubular halloysites in the studied sample, similar to the results of previous studies. Various conditions of contact time, solution pH, the adsorbent weight, and Pb 2+ initial concentration were considered and evaluated using batch adsorption experiments with a total of 53 cases. Subsequently, an HHO-MLP neural network was developed and applied to predict Pb 2+ absorption efficiency in water by the nanotube-type halloysite from weathered pegmatites. A traditional MLP neural network model (without optimized by the HHO algorithm) was also investigated to predict and compare with that of the proposed HHO-MLP neural network model. The experimental results indicated that the nanotube-type halloysite from weathered pegmatites is a potential material used in processing water and removing heavy metals, i.e., Pb 2+ , with a promising development. Furthermore, the obtained results of the proposed HHO-MLP neural network model showed that this model is a robust intelligent model for estimating the efficiency of the Pb 2+ absorption in water using nanotube-type halloysite from weathered pegmatites (i.e., MSE = 1.647; RMSE = 1.283; R 2  = 0.931). It can be applied to increase the Pb 2+ absorption efficiency to eliminate Pb 2+ in an aqueous solution.
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-021-01459-8