Experimental Study on Performance Optimization of Grouting Backfill Material Based on Mechanically Ground Coal Gangue Utilizing Urea and Quicklime Article Swipe
Xiao Wang
,
Jixiong Zhang
,
Meng Li
,
Binbin Huo
,
Ling Jin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/ma16031097
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/ma16031097
Previously conducted studies have established that grouting backfill in mining-induced overburden bed separation and mined-out areas with broken rocks provides an efficient strategy to control strata movement and surface subsidence caused by underground mining. Grouting backfill materials (GBMs) based on coal gangue (CG) are highly desirable in coal mining for accessibility to abundant CG and urgent demand for CG disposal. However, CG is generally employed as coarse aggregate due to rather rigid and inert properties, limiting its application in GBMs. Herein, to reduce reliance on fine aggregates, such as fly ash and clay, cemented GBM formulations using ground CG powder as a dominant component were proposed. Urea and quicklime were utilized as additives to optimize slurry transportability and compressive strength. Besides typical grinding without additives, CG powder was also prepared via grinding with urea, intending to enhance the hydrogen bonding (HB) interaction between urea and minerals contained in CG. The effect of grinding time and urea on CG particle size and phase composition was investigated. Then, the dependence of slurry transportability and compressive strength on grinding time, solid concentration, urea, and quicklime dosage were revealed. It has been experimentally proved that grinding for 30~90 min significantly decreased CG particle size and even induced crystal deformation of dolomite and kaolinite. For GBMs, urea improved slurry flowability, possibly caused by decreased water absorption on the CG surface and the release of water encapsulated in hydrated cement particles. Moreover, quicklime strengthened GBM bodies, which could be explained by an accelerated pozzolanic reaction between CG powder and additional CH supplied by quicklime hydration. G60U3-based GBM-B2 with 5% quicklime provided a stable and smooth slurry with a bleeding rate of 1.25%, a slump flow of 205 mm, and a hardened body with a seven-day UCS of 1.51 MPa.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ma16031097
- https://www.mdpi.com/1996-1944/16/3/1097/pdf?version=1674821523
- OA Status
- gold
- Cited By
- 8
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4318465554
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4318465554Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ma16031097Digital Object Identifier
- Title
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Experimental Study on Performance Optimization of Grouting Backfill Material Based on Mechanically Ground Coal Gangue Utilizing Urea and QuicklimeWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-27Full publication date if available
- Authors
-
Xiao Wang, Jixiong Zhang, Meng Li, Binbin Huo, Ling JinList of authors in order
- Landing page
-
https://doi.org/10.3390/ma16031097Publisher landing page
- PDF URL
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https://www.mdpi.com/1996-1944/16/3/1097/pdf?version=1674821523Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1996-1944/16/3/1097/pdf?version=1674821523Direct OA link when available
- Concepts
-
Slurry, Materials science, Urea, Grinding, Compressive strength, Cement, Aggregate (composite), Fly ash, Kaolinite, Gangue, Chemical engineering, Composite material, Metallurgy, Chemistry, Engineering, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3, 2023: 3Per-year citation counts (last 5 years)
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53Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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