Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines

Yafei Hu; Shenghua Yin; Keqing Li; Bo Zhang; Bin Han

doi:10.1007/s12613-022-2563-8

Volume 30 Issue 9

Sep. 2023

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Article Navigation > International Journal of Minerals, Metallurgy and Materials > 2023 > 30(9): 1692-1704

Yafei Hu, Shenghua Yin, Keqing Li, Bo Zhang, and Bin Han, Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1692-1704. https://doi.org/10.1007/s12613-022-2563-8

Cite this article as:

Yafei Hu, Shenghua Yin, Keqing Li, Bo Zhang, and Bin Han, Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1692-1704. https://doi.org/10.1007/s12613-022-2563-8

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Research Article

Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines

+ Author Affiliations

Corresponding author:
Bin Han E-mail: bin.han@ustb.edu.cn
Received: 28 August 2022; Revised: 23 October 2022; Accepted: 24 October 2022; Available online: 26 October 2022

Abstract

Abstract

The development of solid waste resources as constituent materials for wet shotcrete has significant economic and environmental advantages. In this study, the concept of using tailings as aggregate and fly ash and slag powder as auxiliary cementitious material is proposed and experiments are carried out by response surface methodology (RSM). Multivariate nonlinear response models are constructed to investigate the effect of factors on the uniaxial compressive strength (UCS) of tailings wet shotcrete (TWSC). The UCS of TWSC is predicted and optimized by constructing Gaussian process regression (GPR) and genetic algorithm (GA). The UCS of TWSC is gradually enhanced with the increase of slag powder dosage and fineness modulus, and it is enhanced first and then decreased with the increase of fly ash dosage. The microstructure of TWSC has the highest gray value and the highest UCS when the fly ash dosage is about 120 kg·m⁻³. The GPR–GA model constructed in this study achieves high accuracy prediction and optimization of the UCS of TWSC under multi-factor conditions.
- tailings;
- solid waste;
- wet shotcrete;
- machine learning;
- mix proportion

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References(49)

References

[1]	Q.S. Chen, Y.B. Tao, Y. Feng, Q.L. Zhang, and Y.K. Liu, Utilization of modified copper slag activated by Na₂SO₄ and CaO for unclassified lead/zinc mine tailings based cemented paste backfill, J. Environ. Manage., 290(2021), art. No. 112608. doi: 10.1016/j.jenvman.2021.112608
[2]	W. Sun, D. Wu, H. Liu, and C. Qu, Thermal, mechanical and ultrasonic properties of cemented tailings backfill subjected to microwave radiation, Constr. Build. Mater., 313(2021), art. No. 125535. doi: 10.1016/j.conbuildmat.2021.125535
[3]	Q. Chen, Y. Tao, Q. Zhang, and C. Qi, The rheological, mechanical and heavy metal leaching properties of cemented paste backfill under the influence of anionic polyacrylamide, Chemosphere, 286(2022), art. No. 131630. doi: 10.1016/j.chemosphere.2021.131630
[4]	Y.F. Hu, K.Q. Li, B. Zhang, and B. Han, Strength investigation of the cemented paste backfill in alpine regions using lab experiments and machine learning, Constr. Build. Mater., 323(2022), art. No. 126583. doi: 10.1016/j.conbuildmat.2022.126583
[5]	H. Dang, Z.D. Chang, H.L. Zhou, S.H. Ma, M. Li, and J.L. Xiang, Extraction of lithium from the simulated pyrometallurgical slag of spent lithium-ion batteries by binary eutectic molten carbonates, Int. J. Miner. Metall. Mater., 29(2022), No. 9, p. 1715. doi: 10.1007/s12613-021-2366-3
[6]	C. Qi, Q. Chen, X. Dong, Q. Zhang, and Z.M. Yaseen, Pressure drops of fresh cemented paste backfills through coupled test loop experiments and machine learning techniques, Powder Technol., 361(2020), p. 748. doi: 10.1016/j.powtec.2019.11.046
[7]	A.X. Wu, Z.E. Ruan, and J.D. Wang, Rheological behavior of paste in metal mines, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 717. doi: 10.1007/s12613-022-2423-6
[8]	X. Wei, W. Ni, S. Zhang, X. Wang, J. Li, and H. Du, Influence of the key factors on the performance of steel slag–desulphurisation gypsum-based hydration–carbonation materials, J. Build. Eng., 45(2022), art. No. 103591. doi: 10.1016/j.jobe.2021.103591
[9]	S.H. Yin, L.M. Wang, X. Chen, and A.X. Wu, Agglomeration and leaching behaviors of copper oxides with different chemical binders, Int. J. Miner. Metall. Mater., 28(2021), No. 7, p. 1127. doi: 10.1007/s12613-020-2081-5
[10]	C. Xu, W. Ni, K. Li, S. Zhang, Y. Li, and D. Xu, Hydration mechanism and orthogonal optimisation of mix proportion for steel slag–slag-based clinker-free prefabricated concrete, Constr. Build. Mater., 228(2019), art. No. 117036. doi: 10.1016/j.conbuildmat.2019.117036
[11]	J.J. Li, S. Cao, E. Yilmaz, and Y.P. Liu, Compressive fatigue behavior and failure evolution of additive fiber-reinforced cemented tailings composites, Int. J. Miner. Metall. Mater., 29(2022), No. 2, p. 345. doi: 10.1007/s12613-021-2351-x
[12]	S. Yin, Y. Shao, A. Wu, Y.M. Wang, and X. Chen, Expansion and strength properties of cemented backfill using sulphidic mill tailings, Constr. Build. Mater., 165(2018), p. 138. doi: 10.1016/j.conbuildmat.2018.01.005
[13]	D. Wu, W. Sun, S. Liu, and C. Qu, Effect of microwave heating on thermo-mechanical behavior of cemented tailings backfill, Constr. Build. Mater., 266(2021), art. No. 121180. doi: 10.1016/j.conbuildmat.2020.121180
[14]	C. Qi, A. Fourie, and Q. Chen, Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill, Constr. Build. Mater., 159(2018), p. 473. doi: 10.1016/j.conbuildmat.2017.11.006
[15]	S. Zhao, Q. Sui, C. Cao, et al., Mechanical model of lateral fracture for the overlying hard rock strata along coal mine goaf, Geomech. Eng., 27(2021), No. 1, p. 75.
[16]	Y. Zhang, W. Gao, W. Ni, et al., Influence of calcium hydroxide addition on arsenic leaching and solidification/stabilisation behaviour of metallurgical-slag-based green mining fill, J. Hazard. Mater., 390(2020), art. No. 122161. doi: 10.1016/j.jhazmat.2020.122161
[17]	X.D. Zhao, H.X. Zhang, and W.C. Zhu, Fracture evolution around pre-existing cylindrical cavities in brittle rocks under uniaxial compression, Trans. Nonferrous Met. Soc. China, 24(2014), No. 3, p. 806. doi: 10.1016/S1003-6326(14)63129-0
[18]	H.W. Song, A. Ahmad, F. Farooq, et al., Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms, Constr. Build. Mater., 308(2021), art. No. 125021. doi: 10.1016/j.conbuildmat.2021.125021
[19]	G. Xue, E. Yilmaz, W. Song, and S. Cao, Fiber length effect on strength properties of polypropylene fiber reinforced cemented tailings backfill specimens with different sizes, Constr. Build. Mater., 241(2020), art. No. 118113. doi: 10.1016/j.conbuildmat.2020.118113
[20]	E.M. Li, J. Zhou, X.Z. Shi, et al., Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill, Eng. Comput., 37(2021), No. 4, p. 3519. doi: 10.1007/s00366-020-01014-x
[21]	D.W. Zhang, K.F. Zhao, H. Li, D.M. Wang, L.L. Wang, and G.F. Zhang, Dispersion properties of fly ash–slag powders under the different environment, Constr. Build. Mater., 296(2021), art. No. 123649. doi: 10.1016/j.conbuildmat.2021.123649
[22]	S. Zhang, T. Shi, W. Ni, et al., The mechanism of hydrating and solidifying green mine fill materials using circulating fluidized bed fly ash–slag-based agent, J. Hazard. Mater., 415(2021), art. No. 125625. doi: 10.1016/j.jhazmat.2021.125625
[23]	P.V.R.K. Reddy and D.R. Prasad, A study on workability, strength and microstructure characteristics of graphene oxide and fly ash based concrete, Mater. Today Proc., 62(2022), p. 2919. doi: 10.1016/j.matpr.2022.02.495
[24]	M. Zhai, J. Zhao, D. Wang, Y. Wang, and Q. Wang, Hydration properties and kinetic characteristics of blended cement containing lithium slag powder, J. Build. Eng., 39(2021), art. No. 102287. doi: 10.1016/j.jobe.2021.102287
[25]	V. Nilsen, L.T. Pham, M. Hibbard, A. Klager, S. Cramer, and D. Morgan, Prediction of concrete coefficient of thermal expansion and other properties using machine learning, Constr. Build. Mater., 220(2019), p. 587. doi: 10.1016/j.conbuildmat.2019.05.006
[26]	Y.E. Asri, M.B. Aicha, M. Zaher, and A.H. Alaoui, Prediction of compressive strength of self-compacting concrete using four machine learning technics, Mater. Today Proc., 57(2022), p. 859. doi: 10.1016/j.matpr.2022.02.487
[27]	C.C. Qi, Q.S. Chen, A. Fourie, et al., Constitutive modelling of cemented paste backfill: A data-mining approach, Constr. Build. Mater., 197(2019), p. 262. doi: 10.1016/j.conbuildmat.2018.11.142
[28]	C. Qi and A. Fourie, Cemented paste backfill for mineral tailings management: Review and future perspectives, Miner. Eng., 144(2019), art. No. 106025. doi: 10.1016/j.mineng.2019.106025
[29]	Z. Yu, X.Z. Shi, X. Chen, et al., Artificial intelligence model for studying unconfined compressive performance of fiber-reinforced cemented paste backfill, Trans. Nonferrous Met. Soc. China, 31(2021), No. 4, p. 1087. doi: 10.1016/S1003-6326(21)65563-2
[30]	J. Wu, H. Jing, Q. Yin, L. Yu, B. Meng, and S. Li, Strength prediction model considering material, ultrasonic and stress of cemented waste rock backfill for recycling gangue, J. Clean. Prod., 276(2020), art. No. 123189. doi: 10.1016/j.jclepro.2020.123189
[31]	C. Qi, Q. Chen, and S.S. Kim, Integrated and intelligent design framework for cemented paste backfill: A combination of robust machine learning modelling and multi-objective optimization, Miner. Eng., 155(2020), art. No. 106422. doi: 10.1016/j.mineng.2020.106422
[32]	C. Qi, Q. Chen, A. Fourie, and Q. Zhang, An intelligent modelling framework for mechanical properties of cemented paste backfill, Miner. Eng., 123(2018), p. 16. doi: 10.1016/j.mineng.2018.04.010
[33]	E. Barreno-Avila, E. Moya-Moya, and C. Pérez-Salinas, Rice-husk fiber reinforced composite (RFRC) drilling parameters optimization using RSM based desirability function approach, Mater. Today Proc., 49(2022), p. 167. doi: 10.1016/j.matpr.2021.07.498
[34]	M.A. Fauzi, M.F. Arshad, N.M. Nor, and E. Ghazali, Modeling and optimization of properties for unprocessed-fly ash (u-FA) controlled low-strength material as backfill materials, Clean. Eng. Technol., 6(2022), art. No. 100395. doi: 10.1016/j.clet.2021.100395
[35]	L. Zhu, Z. Jin, Y. Zhao, and Y. Duan, Rheological properties of cemented coal gangue backfill based on response surface methodology, Constr. Build. Mater., 306(2021), art. No. 124836. doi: 10.1016/j.conbuildmat.2021.124836
[36]	Y. Wang, H. Zhang, Y. Tan, and J. Zhu, Sealing performance of compacted block joints backfilled with bentonite paste or a particle-powder mixture, Soils Found., 61(2021), No. 2, p. 496. doi: 10.1016/j.sandf.2021.01.005
[37]	K.R. Vishwakarma, N.L. Richards, and M.C. Chaturvedi, Microstructural analysis of fusion and heat affected zones in electron beam welded ALLVAC® 718PLUS™ superalloy, Mater. Sci. Eng. A, 480(2008), No. 1-2, p. 517. doi: 10.1016/j.msea.2007.08.002
[38]	J.H. Hu, F.W. Zhao, Y. Kuang, D.J. Yang, M.H. Zheng, and L. Zhao, Microscopic characteristics of the action of an air entraining agent on cemented paste backfill pores, Alex. Eng. J., 59(2020), No. 3, p. 1583. doi: 10.1016/j.aej.2020.04.005
[39]	J.X. Xiao, X. Li, and J.H. Shi, Local linear smoothers using inverse Gaussian regression, Stat. Papers, 60(2019), No. 4, p. 1225. doi: 10.1007/s00362-017-0871-2
[40]	J.Q. Shi, R. Murray-Smith, and D.M. Titterington, Bayesian regression and classification using mixtures of Gaussian processes, Int. J. Adapt. Control Signal Process., 17(2003), No. 2, p. 149. doi: 10.1002/acs.744
[41]	J.Q. Shi, B. Wang, R. Murray-Smith, and D.M. Titterington, Gaussian process functional regression modeling for batch data, Biometrics, 63(2007), No. 3, p. 714. doi: 10.1111/j.1541-0420.2007.00758.x
[42]	J. Dearmon and T.E. Smith, Gaussian process regression and Bayesian model averaging: An alternative approach to modeling spatial phenomena, Geogr. Anal., 48(2016), No. 1, p. 82. doi: 10.1111/gean.12083
[43]	C.H. Yu, M. Li, C. Noe, S. Fischer-Baum, and M. Vannucci, Bayesian inference for stationary points in Gaussian process regression models for event-related potentials analysis, Biometrics, 2022. DOI: 10.1111/biom.13621
[44]	C. Demay, B. Iooss, L. Le Gratiet, and A. Marrel, Model selection based on validation criteria for Gaussian process regression: An application with highlights on the predictive variance, Qual. Reliab. Eng. Int., 38(2022), No. 3, p. 1482. doi: 10.1002/qre.2973
[45]	G. Maculotti, G. Genta, D. Quagliotti, M. Galetto, and H. Hansen, Gaussian process regression-based detection and correction of disturbances in surface topography measurements, Qual. Reliab. Eng. Int., 38(2021), No. 3, p. 1501. doi: 10.1002/qre.2980
[46]	T. Mukherjee, A. Banerjee, G. Varsamopoulos, S.K.S. Gupta, and S. Rungta, Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers, Comput. Netw., 53(2009), No. 17, p. 2888. doi: 10.1016/j.comnet.2009.06.008
[47]	A. Rezaei-Bazkiaei, E. Dehghan-Niri, E.M. Kolahdouz, A.S. Weber, and G.F. Dargush, A passive design strategy for a horizontal ground source heat pump pipe operation optimization with a non-homogeneous soil profile, Energy Build., 61(2013), p. 39. doi: 10.1016/j.enbuild.2013.01.040
[48]	V. Houšt’, J. Eliáš, and L. Miča, Shape optimization of concrete buried arches, Eng. Struct., 48(2013), p. 716. doi: 10.1016/j.engstruct.2012.11.037
[49]	K. Zorlu, C. Gokceoglu, F. Ocakoglu, H.A. Nefeslioglu, and S. Acikalin, Prediction of uniaxial compressive strength of sandstones using petrography-based models, Eng. Geol., 96(2008), No. 3-4, p. 141. doi: 10.1016/j.enggeo.2007.10.009