Properties, hazards and valuable metal recovery technologies of red mud: A review

Particuology, Год журнала: 2024, Номер 93, С. 328 - 348

Опубликована: Июль 27, 2024

Язык: Английский

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Impact of Hematite in Red Mud on Hydration Characteristics and Environmental Performance of Cementitious Materials

Cement and Concrete Composites, Год журнала: 2025, Номер unknown, С. 106035 - 106035

Опубликована: Март 1, 2025

Язык: Английский

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Basic Research on the Preparation of Electrolytic Manganese Residue–Red Mud–Ground Granulated Blast Furnace Slag–Calcium Hydroxide Composite Cementitious Material and Its Mechanical Properties

Materials, Год журнала: 2025, Номер 18(6), С. 1218 - 1218

Опубликована: Март 10, 2025

A novel composite cementitious material was constructed by synergistically utilizing multiple industrial solid wastes, including electrolytic manganese residue (EMR), red mud (RM), and ground granulated blast furnace slag (GGBS), with calcium hydroxide [Ca(OH)2] as an alkaline activator. In addition, the mechanical properties of materials were systematically analyzed under different raw ratios, alkali activator dosages, water-binder ratios. To further investigate hydration products mechanisms material, characterization methods, for instance, XRD, FT-IR, SEM-EDS, TG-DTG, employed to characterize materials. ensure that does not cause additional environmental pressure, it toxic leaching. The relevant experimental results indicate optimal ratio EMR–RM–GGBS–Ca(OH)2 components is EMR content 20%, RM 15%, GGBS 52%, 13%, 0.5. Under ratio, at 28 days exhibited a compressive strength 27.9 MPa, well flexural 7.5 MPa. in as-synthesized system primarily encompassed ettringite (AFt) hydrated silicate (C-S-H), their tight bonding middle later curing stages main source engineering strength. heavy metal concentrations 28-day leaching solution fall within limits prescribed drinking water hygiene standard (GB5749-2022), indicating this exhibits satisfactory safety performance. sum up, elucidated process involved research provide useful references pollution-free treatment resource utilization wastes such future.

Язык: Английский

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Application and mechanism analysis of steel slag in resource recovery and environmental remediation: A review

Minerals Engineering, Год журнала: 2025, Номер 227, С. 109268 - 109268

Опубликована: Март 31, 2025

Язык: Английский

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Desulfurization, desilication, and the separation and recovery of iron from bauxite: A review

Minerals Engineering, Год журнала: 2025, Номер 228, С. 109315 - 109315

Опубликована: Апрель 18, 2025

Язык: Английский

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Optimal Red Mud Replacement Level Evaluation in Conventional Concrete Based on Fresh and Hardened Concrete Characteristics

Journal of Sustainable Metallurgy, Год журнала: 2024, Номер 10(2), С. 835 - 850

Опубликована: Май 6, 2024

Язык: Английский

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Improvement of OER kinetics of calcium zirconate by molybdenum doping method in basic media

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 90, С. 972 - 980

Опубликована: Окт. 10, 2024

Язык: Английский

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Synergistic action and effect mechanism of coal gangue powder and red mud on the properties of concretes

Journal of Building Engineering, Год журнала: 2024, Номер 98, С. 110999 - 110999

Опубликована: Окт. 9, 2024

Язык: Английский

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Multi-targeted strength properties of recycled aggregate concrete through a machine learning approach

Engineering Computations, Год журнала: 2024, Номер 42(1), С. 388 - 430

Опубликована: Ноя. 22, 2024

Purpose Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of waste are produced globally per year. In addition, also accelerates the consumption natural resources, depletion these resources. Therefore, this study uses artificial intelligence (AI) examine utilization recycled aggregate (RCA) in concrete. Design/methodology/approach An extensive database 583 data points collected from literature for predictive modeling. Four machine learning algorithms, namely neural network (ANN), random forest (RF), ridge regression (RR) least adjacent shrinkage selection operator (LASSO) (LR), predicting simultaneously compressive tensile strength were evaluated. The dataset contains independent variables two dependent variables. Statistical parameters, including coefficient determination (R 2 ), mean square error (MSE), absolute (MAE) root (RMSE), employed assess accuracy algorithms. K-fold cross-validation was validate obtained results, SHapley Additive exPlanations (SHAP) analysis applied identify most sensitive parameters out input parameters. Findings results indicate that RF prediction model performance is better more satisfactory than other Furthermore, ANN algorithm ranks as second accurate algorithm. However, RR LR exhibit poor findings with low accuracy. successfully SHAP indicates cement content percentages effective parameter. special attention should be given enhance performance. Originality/value This uniquely applies AI optimize use RCA production. By evaluating four ANN, RF, on a comprehensive dataset, identities models strength. determine key result validation adds robustness. highlight superior provide actionable insights into enhancing RCA, contributing sustainable practice.

Язык: Английский

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Industrial solid waste as oxygen carrier in chemical looping gasification technology: A review

Journal of the Energy Institute, Год журнала: 2024, Номер 116, С. 101694 - 101694

Опубликована: Май 31, 2024

Язык: Английский

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