Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm
The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 -...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dong, Chaowei [verfasserIn] Zhou, Nan [verfasserIn] Ferro, Giuseppe Andrea [verfasserIn] Yan, Hao [verfasserIn] Xu, Jianfei [verfasserIn] Wang, Haodong [verfasserIn] Liu, Sixu [verfasserIn] Zhang, Zhanguo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
Multi-objective optimization algorithm |
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| Übergeordnetes Werk: |
Enthalten in: Construction and building materials - Amsterdam [u.a.] : Elsevier Science, 1987, 418 |
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| Übergeordnetes Werk: |
volume:418 |
| DOI / URN: |
10.1016/j.conbuildmat.2024.135432 |
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| Katalog-ID: |
ELV06720807X |
|---|
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| 100 | 1 | |a Dong, Chaowei |e verfasserin |0 (orcid)0000-0002-4125-0175 |4 aut | |
| 245 | 1 | 0 | |a Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm |
| 264 | 1 | |c 2024 | |
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| 520 | |a The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. | ||
| 650 | 4 | |a Engineering performance | |
| 650 | 4 | |a Multi-objective optimization algorithm | |
| 650 | 4 | |a Mixture design | |
| 650 | 4 | |a Pure gangue backfilling slurry | |
| 650 | 4 | |a Utilization of coal gangue | |
| 700 | 1 | |a Zhou, Nan |e verfasserin |4 aut | |
| 700 | 1 | |a Ferro, Giuseppe Andrea |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Jianfei |e verfasserin |0 (orcid)0009-0002-9054-9680 |4 aut | |
| 700 | 1 | |a Wang, Haodong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Sixu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhanguo |e verfasserin |4 aut | |
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10.1016/j.conbuildmat.2024.135432 doi (DE-627)ELV06720807X (ELSEVIER)S0950-0618(24)00573-7 DE-627 ger DE-627 rda eng 690 VZ 56.45 bkl Dong, Chaowei verfasserin (orcid)0000-0002-4125-0175 aut Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. Engineering performance Multi-objective optimization algorithm Mixture design Pure gangue backfilling slurry Utilization of coal gangue Zhou, Nan verfasserin aut Ferro, Giuseppe Andrea verfasserin aut Yan, Hao verfasserin aut Xu, Jianfei verfasserin (orcid)0009-0002-9054-9680 aut Wang, Haodong verfasserin aut Liu, Sixu verfasserin aut Zhang, Zhanguo verfasserin aut Enthalten in Construction and building materials Amsterdam [u.a.] : Elsevier Science, 1987 418 Online-Ressource (DE-627)320423115 (DE-600)2002804-0 (DE-576)259271187 nnns volume:418 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.45 Baustoffkunde VZ AR 418 |
| spelling |
10.1016/j.conbuildmat.2024.135432 doi (DE-627)ELV06720807X (ELSEVIER)S0950-0618(24)00573-7 DE-627 ger DE-627 rda eng 690 VZ 56.45 bkl Dong, Chaowei verfasserin (orcid)0000-0002-4125-0175 aut Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. Engineering performance Multi-objective optimization algorithm Mixture design Pure gangue backfilling slurry Utilization of coal gangue Zhou, Nan verfasserin aut Ferro, Giuseppe Andrea verfasserin aut Yan, Hao verfasserin aut Xu, Jianfei verfasserin (orcid)0009-0002-9054-9680 aut Wang, Haodong verfasserin aut Liu, Sixu verfasserin aut Zhang, Zhanguo verfasserin aut Enthalten in Construction and building materials Amsterdam [u.a.] : Elsevier Science, 1987 418 Online-Ressource (DE-627)320423115 (DE-600)2002804-0 (DE-576)259271187 nnns volume:418 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.45 Baustoffkunde VZ AR 418 |
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10.1016/j.conbuildmat.2024.135432 doi (DE-627)ELV06720807X (ELSEVIER)S0950-0618(24)00573-7 DE-627 ger DE-627 rda eng 690 VZ 56.45 bkl Dong, Chaowei verfasserin (orcid)0000-0002-4125-0175 aut Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. Engineering performance Multi-objective optimization algorithm Mixture design Pure gangue backfilling slurry Utilization of coal gangue Zhou, Nan verfasserin aut Ferro, Giuseppe Andrea verfasserin aut Yan, Hao verfasserin aut Xu, Jianfei verfasserin (orcid)0009-0002-9054-9680 aut Wang, Haodong verfasserin aut Liu, Sixu verfasserin aut Zhang, Zhanguo verfasserin aut Enthalten in Construction and building materials Amsterdam [u.a.] : Elsevier Science, 1987 418 Online-Ressource (DE-627)320423115 (DE-600)2002804-0 (DE-576)259271187 nnns volume:418 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.45 Baustoffkunde VZ AR 418 |
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10.1016/j.conbuildmat.2024.135432 doi (DE-627)ELV06720807X (ELSEVIER)S0950-0618(24)00573-7 DE-627 ger DE-627 rda eng 690 VZ 56.45 bkl Dong, Chaowei verfasserin (orcid)0000-0002-4125-0175 aut Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. Engineering performance Multi-objective optimization algorithm Mixture design Pure gangue backfilling slurry Utilization of coal gangue Zhou, Nan verfasserin aut Ferro, Giuseppe Andrea verfasserin aut Yan, Hao verfasserin aut Xu, Jianfei verfasserin (orcid)0009-0002-9054-9680 aut Wang, Haodong verfasserin aut Liu, Sixu verfasserin aut Zhang, Zhanguo verfasserin aut Enthalten in Construction and building materials Amsterdam [u.a.] : Elsevier Science, 1987 418 Online-Ressource (DE-627)320423115 (DE-600)2002804-0 (DE-576)259271187 nnns volume:418 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.45 Baustoffkunde VZ AR 418 |
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10.1016/j.conbuildmat.2024.135432 doi (DE-627)ELV06720807X (ELSEVIER)S0950-0618(24)00573-7 DE-627 ger DE-627 rda eng 690 VZ 56.45 bkl Dong, Chaowei verfasserin (orcid)0000-0002-4125-0175 aut Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. Engineering performance Multi-objective optimization algorithm Mixture design Pure gangue backfilling slurry Utilization of coal gangue Zhou, Nan verfasserin aut Ferro, Giuseppe Andrea verfasserin aut Yan, Hao verfasserin aut Xu, Jianfei verfasserin (orcid)0009-0002-9054-9680 aut Wang, Haodong verfasserin aut Liu, Sixu verfasserin aut Zhang, Zhanguo verfasserin aut Enthalten in Construction and building materials Amsterdam [u.a.] : Elsevier Science, 1987 418 Online-Ressource (DE-627)320423115 (DE-600)2002804-0 (DE-576)259271187 nnns volume:418 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.45 Baustoffkunde VZ AR 418 |
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Dong, Chaowei @@aut@@ Zhou, Nan @@aut@@ Ferro, Giuseppe Andrea @@aut@@ Yan, Hao @@aut@@ Xu, Jianfei @@aut@@ Wang, Haodong @@aut@@ Liu, Sixu @@aut@@ Zhang, Zhanguo @@aut@@ |
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In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. 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| author |
Dong, Chaowei |
| spellingShingle |
Dong, Chaowei ddc 690 bkl 56.45 misc Engineering performance misc Multi-objective optimization algorithm misc Mixture design misc Pure gangue backfilling slurry misc Utilization of coal gangue Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm |
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690 VZ 56.45 bkl Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm Engineering performance Multi-objective optimization algorithm Mixture design Pure gangue backfilling slurry Utilization of coal gangue |
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ddc 690 bkl 56.45 misc Engineering performance misc Multi-objective optimization algorithm misc Mixture design misc Pure gangue backfilling slurry misc Utilization of coal gangue |
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ddc 690 bkl 56.45 misc Engineering performance misc Multi-objective optimization algorithm misc Mixture design misc Pure gangue backfilling slurry misc Utilization of coal gangue |
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Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm |
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Dong, Chaowei Zhou, Nan Ferro, Giuseppe Andrea Yan, Hao Xu, Jianfei Wang, Haodong Liu, Sixu Zhang, Zhanguo |
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research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm |
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Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm |
| abstract |
The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. |
| abstractGer |
The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. |
| abstract_unstemmed |
The gangue grouting and backfilling for the subsequent space after coal mining can effectively address solid waste disposal and protect the ecological environment in the mining area. In this study, gangue sand (GS) with different particle size grading (A: 4.75 - 2.36 mm; B:2.36 - 1.18 mm; C: 1.18 - 0.6 mm; D: 0.6 - 0.3 mm; E: 0.3 - 0.15 mm; F: 0.15 - 0.075 mm; G: 0.075 - 0 mm) and water contents (H) were considered, and the fluidity, bleeding rate and uniaxial compressive strength (UCS) of the pure gangue backfilling slurry (PGBS) were determined as response objectives. Subsequently, fifty-two sets of proportion optimization schemes of the PGBS were generated by a D-optimal mixture design, the individual and interactive effects of influencing factors on the response objectives were studied, and the optimal proportion of the PGBS was globally obtained based on a multi-objective optimization algorithm. The variance (ANOVA) results show that the P-values of the prediction model for fluidity, bleeding rate, and UCS are less than 0.0001 (much less than 0.05), their lack of fit P-values are greater than 0.05, and their R 2 reaches 0.9225, 0.8780 and 0.8378, respectively. The three response-targeted prediction models are statistically significant and highly effective, demonstrating superior fitting to experimental data and accurate forecasting of the PGBS performance. The gangue sand with a particle size of 0.075 - 0 mm (G) and water content (H) have decisive effects on the three response objectives, and G exhibits a similar effect to that of cementitious powder in PGBS. Additionally, the two influencing factors A-F, A-H, D-G, and E-G have significant interactive effects on fluidity, and D-H has a significant interactive effect on bleeding rate. The optimal proportion of PGBS obtained by the multi-objective optimization algorithm is A: B: C: D: E: F: G: H = 0.15: 0.13: 0.04: 0.06: 0.099: 0.1: 0.184: 0.237. The results of repeated experiments suggest that the error between the predicted value and the measured value of the three responses is less than 6%, and the optimization results are accurate and reliable. Moreover, the surface of the PGBS sample with the optimal proportion exhibits fewer pores, and the powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes. These features facilitate the formation of a tightly-connected flocculent structure and the improvement of pumping characteristics and mechanical properties of the slurry. The research provides an empirical and pragmatic foundation for achieving the large-scale, efficient, and environmentally friendly disposal of coal gangue. |
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| title_short |
Research on proportion and performance optimization of pure gangue backfilling slurry based on multi-objective differential evolution algorithm |
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Zhou, Nan Ferro, Giuseppe Andrea Yan, Hao Xu, Jianfei Wang, Haodong Liu, Sixu Zhang, Zhanguo |
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Zhou, Nan Ferro, Giuseppe Andrea Yan, Hao Xu, Jianfei Wang, Haodong Liu, Sixu Zhang, Zhanguo |
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| doi_str |
10.1016/j.conbuildmat.2024.135432 |
| up_date |
2024-07-06T20:28:36.645Z |
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