Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm

Bac, Bui Hoang; Nguyen, Hoang; Thảo, Nguyễn Thị; Duyen, Le Thi; Hanh, Vo Thi; Dũng, Nguyễn Tiến; Khang, Luong Quang; An, Do Manh

doi:10.1016/j.chemosphere.2021.131012

Cited by 19 publications

(3 citation statements)

References 49 publications

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“…Deep learning methods in microscopic imaging have now been developed to automate mineral grain segmentation and recognition [205][206][207]. With recent AI developments, the intelligent identification and quantification of minerals is becoming possible [208][209][210]. The voids between geological and artificial intelligence sciences can be filled with the latest research advancements.…”

Section: Future Research and Directionsmentioning

confidence: 99%

Mineral Characterization Using Scanning Electron Microscopy (SEM): A Review of the Fundamentals, Advancements, and Research Directions

Ali,

Zhang,

Santos

2023

Applied Sciences

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Scanning electron microscopy (SEM) is a powerful tool in the domains of materials science, mining, and geology owing to its enormous potential to provide unique insight into micro and nanoscale worlds. This comprehensive review discusses the background development of SEM, basic SEM operation, including specimen preparation and image processing, and the fundamental theoretical calculations underlying SEM operation. It provides a foundational understanding for engineers and scientists who have never had a chance to dig in depth into SEM, contributing to their understanding of the workings and development of this robust analytical technique. The present review covers how SEM serves as a crucial tool in mineral characterization, with specific discussion on the workings and research fronts of SEM-EDX, SEM-AM, SEM-MLA, and QEMSCAN. With automation gaining pace in the development of all spheres of technology, understanding the uncertainties in SEM measurements is very important. The constraints in mineral phase identification by EDS spectra and sample preparation are conferred. In the end, future research directions for SEM are analyzed with the possible incorporation of machine learning, deep learning, and artificial intelligence tools to automate the process of mineral identification, quantification, and efficient communication with researchers so that the robustness and objectivity of the analytical process can be improved and the analysis time and involved costs can be reduced. This review also discusses the idea of integrating robotics with SEM to make the equipment portable so that further mineral characterization insight can be gained not only on Earth but also on other terrestrial grounds.

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Section: Future Research and Directionsmentioning

confidence: 99%

Mineral Characterization Using Scanning Electron Microscopy (SEM): A Review of the Fundamentals, Advancements, and Research Directions

Ali,

Zhang,

Santos

2023

Applied Sciences

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“…In papers [1][2][3][4][5][6][7] the authors investigated the influence of various sorbents on technologies and water quality during the extraction of heavy metal ions. Technologies based on the use of biological factors are considered in works [8][9][10][11][12] and sorbents based on bentonite clays are considered in [13][14][15][16]. Researches [3; 4] show bentonites and zeolites as the most promising sorbents mainly due to their availability and technological convenience.…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Determination of Hydraulic Grain Size of Natural and Artificial Sorbents for Simulation of Settle Facility

Marysyk

2022

LRaWM

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In the conditions of progressive contamination of surface sources of water supply and inefficient wastewater treatment when using existing water treatment technologies, the research problem and the justification of the use of sorption materials for the retention of specific pollutants, in particular heavy metal ions and radionuclides, is urgent. The parameters that determine the efficiency of sorbents are indicators of their sedimentation rate. The purpose of the experiments was to determine the sedimentation rate indicators for bentonite and copper ferrocyanide, build sorbent sedimentation graphs, and establish the estimated sedimentation rate of sorbents in the sedimentation tank based on the studied data considering temperature regime. Deposition of the sorbent in settling tanks occurs with the non-stop movement of water at a low speed in the direction from the inlet to the outlet. The experiments are aimed at substantiating the efficiency and criteria of a universal facility, which is able to work equally effectively with sorbents in different aggregate states. The process of sorbent sedimentation in water is characterized by the kinetics of sorbent flakes conglomerates sedimentation. These processes are displayed in the form of deposition kinetics graphs. The experiment used powdered bentonite and a solution of copper ferrocyanide, consisting of yellow blood salt and copper sulphate in a given proportional ratio. In the course of the study the following parameters were determined: the hydraulic grain size of bentonite powdery clay, the dependence of the sedimentation rate on the temperature regime. The liquid layer was divided into layers that show changes in the amount of suspended substances depending on the depth, which made it possible to determine the dimensions of the settling tank, the height of the liquid overflow, which, in turn, made it possible to conduct simulation experiments on virtual machines with a full-scale clarifier-absorber in accordance to geometric parameters.

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“…In recent years, meta-heuristic algorithms are applied for solving complex problems in different applications such as optimization of weight and cost of cantilever retaining wall 70 , multi-response machining processes 71 , symbiosis organisms search for global optimization and image segmentation 72 , human social learning intelligence 73 , nanotubular halloysites in weathered pegmatites 74 , numerical optimization and real-world applications 75 , convergence analysis 76 , higher Dimensional Optimization Problems 77 , non-dominated sorting advanced 78 , Lagrange Interpolation 79 . LCA is quite different from the existing meta-heuristic algorithms although it belongs to the category of human-based meta-heuristics.…”

Section: Introductionmentioning

confidence: 99%

Learning cooking algorithm for solving global optimization problems

Gopi,

Mohapatra

2024

Sci Rep

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In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of humans in order to solve challenging problems. The LCA strategy is primarily motivated by observing how mothers and children prepare food. The fundamental idea of the LCA strategy is mathematically designed in two phases: (i) children learn from their mothers and (ii) children and mothers learn from a chef. The performance of the proposed LCA algorithm is evaluated on 51 different benchmark functions (which includes the first 23 functions of the CEC 2005 benchmark functions) and the CEC 2019 benchmark functions compared with state-of-the-art meta-heuristic algorithms. The simulation results and statistical analysis such as the t-test, Wilcoxon rank-sum test, and Friedman test reveal that LCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the LCA algorithm has been employed to solve seven real-world engineering problems, such as the tension/compression spring design, pressure vessel design problem, welded beam design problem, speed reducer design problem, gear train design problem, three-bar truss design, and cantilever beam problem. The results demonstrate the LCA’s superiority and capability over other algorithms in solving complex optimization problems.

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Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm

Cited by 19 publications

References 49 publications

Mineral Characterization Using Scanning Electron Microscopy (SEM): A Review of the Fundamentals, Advancements, and Research Directions

Mineral Characterization Using Scanning Electron Microscopy (SEM): A Review of the Fundamentals, Advancements, and Research Directions

Determination of Hydraulic Grain Size of Natural and Artificial Sorbents for Simulation of Settle Facility

Learning cooking algorithm for solving global optimization problems

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