Integrated experimental and machine learning approach for Reactive Black 5 removal using straw-derived biochars
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International Journal of Environmental Research
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Wastewater contaminated with synthetic dyes poses significant environmental and health risks, and cost-effective solutions
are urgently needed. Conventional adsorbents are often costly and exhibit limited efficiency, fostering an increasing pursuit
for sustainable and economically viable alternatives. This study investigates the use of biochars derived from wheat straw
(WSP), oil seed rape straw (OSR), and Miscanthus straw (MSP) for the removal of Reactive Black 5 dye from water. The
novelty of this work lies in combining agricultural biochars with advanced data-driven approaches—including generalized
linear modeling, machine learning (Random Forest, Gradient Boosting), and Monte Carlo simulations—together with ecotoxicological
validation, to bridge experimental results with predictive modeling. Biochars produced at 550 °C and 700 °C
were evaluated through batch adsorption experiments, FT-IR and SEM/EDS analyses, equilibrium and isotherm modeling,
desorption and regeneration capabilities, and real effluent application. WSP700 achieved the highest removal efficiency
(94%) at 75 g L−1, with adsorption most effective at pH 5. Although higher dosages improved removal, adsorption capacity
decreased due to site aggregation. Random Forest provided the best fit for capturing non-linear behavior, whereas
cross-validation and external interpolation revealed that Gradient Boosting and linear/penalized regressions offered better
generalization performance. Regeneration tests showed 70% desorption efficiency after seven cycles, and ecotoxicological
assays revealed a marked EC50 increase (1.131 → 2.204), indicating reduced environmental risk. Overall, the results
highlight the potential of WSP and OSR biochars as efficient, regenerable, and environmentally safe adsorbents for dye
removal in wastewater treatment, supporting the development of sustainable circular-economy strategies.
Como referenciar
GUILHEN, SABINE N.; SILVA, THALITA T.; SOUSA, GUILHERME E.Z. de; BORRELY, SUELI I.; ARAUJO, LEANDRO G. de. Integrated experimental and machine learning approach for Reactive Black 5 removal using straw-derived biochars. International Journal of Environmental Research, v. 20, p. 1-26, 2025. DOI: 10.1007/s41742-025-01010-3. Disponível em: https://repositorio.ipen.br/handle/123456789/49314. Acesso em: 26 Mar 2026.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.