ORIGINAL PAPER
Descriptive and predictive analysis of trace metals in coal ash: statistical insights and modeling
1
AGH University of Krakow
These authors had equal contribution to this work
Submission date: 2025-07-15
Final revision date: 2026-03-31
Acceptance date: 2026-04-10
Publication date: 2026-06-02
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2026;42(2):89-111
KEYWORDS
TOPICS
ABSTRACT
Coal combustion generates ash that contains trace metals with both economic and environmental relevance. This study aims to assess the concentration, variability, and inter-element relationships of nine trace metals (Sb, Co, Cu, Ga, Mo, Ni, Ag, V, Zn) in ash samples from 28 coal specimens of varying rank (lignite, subbituminous, and bituminous) collected from Polish deposits. Elemental concentrations were determined via ICP-MS, and oxide composition was analyzed to examine geochemical associations. Descriptive statistics, correlation analysis, and predictive modeling (multiple linear regression and Support Vector Machine, SVM) were applied to characterize elemental behavior and identify reliable predictors of metal content in ash. The results show substantial variability in elemental concentrations, with several metals (e.g., Ni, V, Zn) enriched in ash relative to parent coal. Bituminous coal ashes generally exhibited higher average concentrations than lignite ashes, except for Zn. Strong linear correlations were found between selected metal pairs (e.g., Cu–Co, Ag–Co), while correlations with oxides (notably Al2O3, Fe2O3, Cr2O3) supported their role in controlling metal distribution. Regression models demonstrated predictive capability for most elements (R2 up to 0.88), with SVM models showing improved performance (R2 up to 0.94, MAPE as low as 19.78%). These findings highlight the importance of oxide composition in trace metal behavior and provide a methodological basis for assessing ash quality, environmental risk, and potential resource recovery.
FUNDING
This research project was partly supported by the AGH University of Krakow, Faculty of Geology, Geophysics and Environmental Protection, as a part of a statutory project.
CONFLICT OF INTEREST
The Authors have no conflict of interest to declare.
METADATA IN OTHER LANGUAGES:
Polish
Analiza opisowa i predykcyjna metali śladowych w popiołach ze spalania węgla: ujęcie statystyczne i modelowanie
węgiel, popiół, modele regresyjne, eksploracyjna analiza danych, pierwiastki krytyczne
Spalanie węgla generuje popioły zawierające pierwiastki śladowe o istotnym znaczeniu gospodarczym i środowiskowym. Celem niniejszego badania była ocena stężenia, zmienności oraz relacji pomiędzy dziewięcioma metalami śladowymi (Sb, Co, Cu, Ga, Mo, Ni, Ag, V, Zn) w próbkach popiołów uzyskanych z 28 próbek węgla o różnym stopniu uwęglenia (węgiel brunatny, brunatny twardy i kamienny) pochodzących z polskich złóż. Stężenia pierwiastków oznaczono metodą ICP-MS, a analizę składu tlenkowego przeprowadzono w celu identyfikacji powiązań geochemicznych. Wykorzystano statystyki opisowe, analizę korelacji oraz modele predykcyjne (regresja liniowa wieloraka i maszyny wektorów nośnych – SVM) do scharakteryzowania zachowania pierwiastków i wskazania niezawodnych predyktorów ich zawartości w popiele. Wyniki wykazały znaczną zmienność stężeń pierwiastków, przy czym niektóre metale (np. Ni, V, Zn) uległy wzbogaceniu w popiele względem węgla macierzystego. Popioły z węgla kamiennego zawierały przeciętnie wyższe stężenia metali niż z węgla brunatnego, z wyjątkiem cynku. Zaobserwowano silne korelacje liniowe pomiędzy wybranymi parami metali (np. Cu–Co, Ag–Co) oraz istotne związki z tlenkami (zwłaszcza Al2O3, Fe2O3, Cr2O3), co potwierdza ich wpływ na rozkład metali. Modele regresji wykazały dobrą zdolność predykcyjną (R2 do 0,88), a modele SVM uzyskały jeszcze lepsze wyniki (R2 do 0,94, MAPE nawet 19,78%). Uzyskane wyniki podkreślają znaczenie składu tlenkowego w analizie zachowania pierwiastków śladowych i dostarczają metodycznych podstaw do oceny jakości popiołu, ryzyka środowiskowego i możliwości odzysku surowców.
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