Academic Journal
Optimization of native plant selection for phytomining of strategic/critical raw materials using Artificial Bee Colony algorithm.
| Τίτλος: | Optimization of native plant selection for phytomining of strategic/critical raw materials using Artificial Bee Colony algorithm. |
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| Συγγραφείς: | Arslan Topal, E. Işıl1 (AUTHOR) eiarslan@firat.edu.tr, Topal, Murat2,3 (AUTHOR), Çelik, Erdal4,5,6 (AUTHOR), Öbek, Erdal7 (AUTHOR) |
| Πηγή: | International Journal of Phytoremediation. Sep2026, p1-18. 18p. 7 Illustrations. |
| Θεματικοί όροι: | *Bees algorithm, *Native plants, *Mathematical optimization, *Raw materials, *Bioconcentration, *Phytoremediation |
| Περίληψη: | Abstract\nNovelty Statement\nHighlightsThis study investigates the potential of native plant species (NPs) as secondary sources for strategic/critical raw materials (SCRMs) through phytomining, aiming to identify the most efficient accumulator for sustainable element recovery. To achieve high-precision results, the Artificial Bee Colony (ABC) algorithm, a robust meta-heuristic optimization technique inspired by the foraging behavior of honeybees, was utilized to minimize the error between experimental data and predicted models to optimize the accumulation of key elements including cobalt (Co), strontium (Sr), antimony (Sb), vanadium (V), phosphorus (P), lanthanum (La), magnesium (Mg), titanium (Ti), aluminum (Al), and scandium (Sc). The research focused on three NP species—Musk thistle (MT), curly dock (CD), and Juncus (JS)—collected from a geothermal hot spring area. Methodological validation was performed through comparative statistical analysis and the calculation of bioconcentration factor (BCF) and translocation factor (TF) to assess phytoremediation efficiency. The ABC optimization results revealed that MT exhibited the most significant accumulation capacity, particularly for Mg, reaching a peak concentration of 3,910 mg/kgdw in the leaves. The concentration range (lowest to highest value) was between 0.01 (for La) and 3,910 mg/kgdw for the studied plants. Principal component analysis (PCA) corroborated these findings, explaining cumulative variances of 80.4% (MT), 89.4% (CD), and 76.4% (JS). The innovation of this study lies in the application of the ABC algorithm to the field of phytomining, which offers a more dynamic and flexible alternative to traditional regression-based models. The primary advantage of this proposed method is its superior global search capability and ability to handle multi-elemental interactions with high predictive accuracy. However, a potential disadvantage remains the computational complexity and the requirement for fine-tuning control parameters to ensure consistent convergence. Overall, MT is identified as a highly promising candidate for the phytomining of SCRMs, with Mg emerging as the most effectively accumulated element under the optimized ABC model.This study contributes to the field of environmental biotechnology by integrating the Artificial Bee Colony (ABC) optimization algorithm to assess the phytomining potential of native flora for a comprehensive suite of strategic/critical raw materials (SCRMs). Unlike conventional phytoremediation research, which predominantly focuses on soil decontamination using standard statistical approaches, this work utilizes meta-heuristic optimization to evaluate SCRM recovery potential within extreme geothermal ecosystems. A significant finding of this research is the identification of the Musk thistle (MT) as a potent magnesium (Mg) accumulator, with leaf concentrations reaching 3,910 mg/kgdw. By systematically analyzing SCRM partitioning through bioconcentration factor (BCF) and translocation factor (TF), this study offers a refined methodological framework for identifying high-efficiency accumulators in specialized mineralized environments, thereby bridging the gap between traditional pollution studies and resource-oriented phytomining.Optimal strategic/critical raw material (SCRM) accumulation was determined by Artificial Bee Colony algorithm.Mg accumulation in Musk thistle leaves reached a peak of 3,910 mg/kg.This study confirmed Musk thistle as a strategic secondary source for SCRMs.Optimal strategic/critical raw material (SCRM) accumulation was determined by Artificial Bee Colony algorithm.Mg accumulation in Musk thistle leaves reached a peak of 3,910 mg/kg.This study confirmed Musk thistle as a strategic secondary source for SCRMs. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimization of native plant selection for phytomining of strategic/critical raw materials using Artificial Bee Colony algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Arslan+Topal%2C+E%2E+Işıl%22">Arslan Topal, E. Işıl</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> eiarslan@firat.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Topal%2C+Murat%22">Topal, Murat</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Çelik%2C+Erdal%22">Çelik, Erdal</searchLink><relatesTo>4,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Öbek%2C+Erdal%22">Öbek, Erdal</searchLink><relatesTo>7</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Phytoremediation%22">International Journal of Phytoremediation</searchLink>. Sep2026, p1-18. 18p. 7 Illustrations. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Native+plants%22">Native plants</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Raw+materials%22">Raw materials</searchLink><br />*<searchLink fieldCode="DE" term="%22Bioconcentration%22">Bioconcentration</searchLink><br />*<searchLink fieldCode="DE" term="%22Phytoremediation%22">Phytoremediation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract\nNovelty Statement\nHighlightsThis study investigates the potential of native plant species (NPs) as secondary sources for strategic/critical raw materials (SCRMs) through phytomining, aiming to identify the most efficient accumulator for sustainable element recovery. To achieve high-precision results, the Artificial Bee Colony (ABC) algorithm, a robust meta-heuristic optimization technique inspired by the foraging behavior of honeybees, was utilized to minimize the error between experimental data and predicted models to optimize the accumulation of key elements including cobalt (Co), strontium (Sr), antimony (Sb), vanadium (V), phosphorus (P), lanthanum (La), magnesium (Mg), titanium (Ti), aluminum (Al), and scandium (Sc). The research focused on three NP species—Musk thistle (MT), curly dock (CD), and Juncus (JS)—collected from a geothermal hot spring area. Methodological validation was performed through comparative statistical analysis and the calculation of bioconcentration factor (BCF) and translocation factor (TF) to assess phytoremediation efficiency. The ABC optimization results revealed that MT exhibited the most significant accumulation capacity, particularly for Mg, reaching a peak concentration of 3,910 mg/kgdw in the leaves. The concentration range (lowest to highest value) was between 0.01 (for La) and 3,910 mg/kgdw for the studied plants. Principal component analysis (PCA) corroborated these findings, explaining cumulative variances of 80.4% (MT), 89.4% (CD), and 76.4% (JS). The innovation of this study lies in the application of the ABC algorithm to the field of phytomining, which offers a more dynamic and flexible alternative to traditional regression-based models. The primary advantage of this proposed method is its superior global search capability and ability to handle multi-elemental interactions with high predictive accuracy. However, a potential disadvantage remains the computational complexity and the requirement for fine-tuning control parameters to ensure consistent convergence. Overall, MT is identified as a highly promising candidate for the phytomining of SCRMs, with Mg emerging as the most effectively accumulated element under the optimized ABC model.This study contributes to the field of environmental biotechnology by integrating the Artificial Bee Colony (ABC) optimization algorithm to assess the phytomining potential of native flora for a comprehensive suite of strategic/critical raw materials (SCRMs). Unlike conventional phytoremediation research, which predominantly focuses on soil decontamination using standard statistical approaches, this work utilizes meta-heuristic optimization to evaluate SCRM recovery potential within extreme geothermal ecosystems. A significant finding of this research is the identification of the Musk thistle (MT) as a potent magnesium (Mg) accumulator, with leaf concentrations reaching 3,910 mg/kgdw. By systematically analyzing SCRM partitioning through bioconcentration factor (BCF) and translocation factor (TF), this study offers a refined methodological framework for identifying high-efficiency accumulators in specialized mineralized environments, thereby bridging the gap between traditional pollution studies and resource-oriented phytomining.Optimal strategic/critical raw material (SCRM) accumulation was determined by Artificial Bee Colony algorithm.Mg accumulation in Musk thistle leaves reached a peak of 3,910 mg/kg.This study confirmed Musk thistle as a strategic secondary source for SCRMs.Optimal strategic/critical raw material (SCRM) accumulation was determined by Artificial Bee Colony algorithm.Mg accumulation in Musk thistle leaves reached a peak of 3,910 mg/kg.This study confirmed Musk thistle as a strategic secondary source for SCRMs. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/15226514.2026.2735401 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Bees algorithm Type: general – SubjectFull: Native plants Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Raw materials Type: general – SubjectFull: Bioconcentration Type: general – SubjectFull: Phytoremediation Type: general Titles: – TitleFull: Optimization of native plant selection for phytomining of strategic/critical raw materials using Artificial Bee Colony algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Arslan Topal, E. Işıl – PersonEntity: Name: NameFull: Topal, Murat – PersonEntity: Name: NameFull: Çelik, Erdal – PersonEntity: Name: NameFull: Öbek, Erdal IsPartOfRelationships: – BibEntity: Dates: – D: 24 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15226514 Titles: – TitleFull: International Journal of Phytoremediation Type: main |
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