Development of steel-slag-based cementitious material and optimization of slurry ratio based on genetic algorithm and support vector machine (GA?SVM)

YANG Xiao-bing; YAN Ze-peng; YIN Sheng-hua; LI Wei-guang; GAO Qian

doi:10.13374/j.issn2095-9389.2022.02.25.001

Volume 44 Issue 11

Nov. 2022

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YANG Xiao-bing, YAN Ze-peng, YIN Sheng-hua, LI Wei-guang, GAO Qian. Development of steel-slag-based cementitious material and optimization of slurry ratio based on genetic algorithm and support vector machine (GA?SVM)[J]. Chinese Journal of Engineering, 2022, 44(11): 1897-1908. doi: 10.13374/j.issn2095-9389.2022.02.25.001

Citation:

YANG Xiao-bing, YAN Ze-peng, YIN Sheng-hua, LI Wei-guang, GAO Qian. Development of steel-slag-based cementitious material and optimization of slurry ratio based on genetic algorithm and support vector machine (GA?SVM)[J]. Chinese Journal of Engineering, 2022, 44(11): 1897-1908. doi: 10.13374/j.issn2095-9389.2022.02.25.001

Citation:

YANG Xiao-bing, YAN Ze-peng, YIN Sheng-hua, LI Wei-guang, GAO Qian. Development of steel-slag-based cementitious material and optimization of slurry ratio based on genetic algorithm and support vector machine (GA?SVM)[J]. Chinese Journal of Engineering, 2022, 44(11): 1897-1908. doi: 10.13374/j.issn2095-9389.2022.02.25.001

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Development of steel-slag-based cementitious material and optimization of slurry ratio based on genetic algorithm and support vector machine (GA?SVM)

doi: 10.13374/j.issn2095-9389.2022.02.25.001

YANG Xiao-bing^{1, 2, 3},
YAN Ze-peng^{1, 3
,
,},
YIN Sheng-hua^{1, 3},
LI Wei-guang²,
GAO Qian^{1, 3}

1.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
State Key Laboratory of Mineral Processing, Beijing 102628, China
3.
Key Laboratory of High Efficient Mining and Safety of Metal Mines (Ministry of Education), University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: yan_zepeng@163.com
Received Date: 2022-02-25
Available Online: 2022-09-02
Publish Date: 2022-11-01

Abstract

Abstract

To address the problem of high filling cost in an open pit to an underground mine, based on the machine learning method, the filling cementitious material needed for subsequent backfill mining method was developed using the available industrial wastes around the mine, and the ratio of filling slurry was optimized. First, the physical and chemical properties of the materials were analyzed. Unconfined compressive strength tests were conducted with different activator formulations to analyze the influence of each component on the strength of the backfill body. A genetic algorithm and support vector machine (GA?SVM) model was established to predict the steel-slag-based cementitious material formula using the experimental data, and the optimal ratio was determined based on the model prediction results. X-ray diffraction (XRD) and scanning electron microscope (SEM) were used to analyze the hydration products and microstructure characteristics of steel-slag-based cementitious materials at different curing ages and slag dosage conditions and determine the hydration mechanism of steel-slag-based cementitious materials. Finally, the slurry proportion was optimized by strength (i.e., 7 and 28 days) and working characteristics (i.e., slump and bleeding rate) based on the principle of gray target decision. Results revealed that the relative errors of the GA?SVM model for predicting the steel-slag-based cementitious materials strength at 7 and 28 days are 3.6%–12.62% and 6.9%–10.19%, respectively, thereby indicating high prediction accuracy. The optimal proportion of steel-slag-based cementitious materials determined by prediction analysis is steel slag content of 30%, desulfurized gypsum content of 4%, cement clinker content of 12%, and mirabilite content of 1%. The main hydration products of steel-slag-based cementitious materials are amorphous C?S?H gel, ettringite, tricalcium aluminate hydrate, Ca(OH)₂, and CaCO₃. The calcium hydroxide content increases with the steel slag content, which generates a large number of pores and deteriorates the structure and strength of the sample. When the new steel-slag-based cementitious material is applied to the actual backfilling of the mine, the optimal ratio parameters of filling slurry are obtained through the optimization of the model of the gray target decision (i.e., cement?sand ratio of 1∶4 and mass concentration of 72%). Corresponding verification experiments were conducted, and the corresponding strength and working characteristic parameters were 1.74 MPa, 3.61 MPa, 24.2 cm, and 5.91%, which all met the requirements of subsequent filling. With this proportion, the filling cost is 113 ￥·m^?3, which is 38.92% lower than that of the cement filler. The research results will benefit the comprehensive utilization of solid waste and provide support for safe, clean, and efficient mining.
- paste filling,
- GA?SVM,
- steel-slag-based cementitious material,
- grey target decision,
- proportioning optimization

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References(26)

References

[1]	李夕兵, 周健, 王少鋒, 等. 深部固體資源開采評述與探索. 中國有色金屬學報, 2017, 27(6):1236 doi: 10.19476/j.ysxb.1004.0609.2017.06.021 Li X B, Zhou J, Wang S F, et al. Review and practice of deep mining for solid mineral resources. Chin J Nonferrous Met, 2017, 27(6): 1236 doi: 10.19476/j.ysxb.1004.0609.2017.06.021
[2]	Kesimal A, Yilmaz E, Ercikdi B. Evaluation of paste backfill mixtures consisting of sulphide-rich mill tailings and varying cement contents. Cem Concr Res, 2004, 34(10): 1817 doi: 10.1016/j.cemconres.2004.01.018
[3]	尹升華, 劉家明, 陳威, 等. 不同粗骨料對膏體凝結性能的影響及配比優化. 工程科學學報, 2020, 42(7):829 Yin S H, Liu J M, Chen W, et al. Optimization of the effect and formulation of different coarse aggregates on performance of the paste backfill condensation. Chin J Eng, 2020, 42(7): 829
[4]	Yang X B, Xiao B L, Gao Q, et al. Determining the pressure drop of cemented Gobi sand and tailings paste backfill in a pipe flow. Constr Build Mater, 2020, 255: 119371 doi: 10.1016/j.conbuildmat.2020.119371
[5]	張連富, 吳愛祥, 王洪江. 泵送劑對高含泥膏體流變特性影響及機理. 工程科學學報, 2018, 40(8):918 Zhang L F, Wu A X, Wang H J. Effects and mechanism of pumping agent on rheological properties of highly muddy paste. Chin J Eng, 2018, 40(8): 918
[6]	Tilmaz E, Belem T, Benzaazoua M, et al. Assessment of the modified CUAPS apparatus to estimate in situ properties of cemented paste backfill. Geotech Test J, 2010, 33(5): 351
[7]	韋寒波, 巴蕾, 溫震江, 等. 基于熵權多屬性決策的鎂渣膠結料開發及料漿配比優化. 中國有色金屬學報,https://kns.cnki.net/kcms/detail/43.1238.tg.20210902.1616.009.html Wei H B, Ba L, Wen Z J, et al. Development of magnesium slag binder and optimization of slurry ratio based on entropy weight multi-attribute decision. Chin J Nonferrous Met,https://kns.cnki.net/kcms/detail/43.1238.tg.20210902.1616.009.html
[8]	楊曉炳. 低品質多固廢協同制備充填料漿及其管輸阻力研究[學位論文]. 北京: 北京科技大學, 2020 Yang X B. Study on the Collaborative Preparation of Filling Materials with Low Quality and Multi-Solid Wastes and Their Pressure Drop in Pipeline Transportation [Dissertation]. Beijing: University of Science and Technology Beijing, 2020
[9]	Shi C J, Qian J S. High performance cementing materials from industrial slags—a review. Resour Conserv Recycl, 2000, 29(3): 195 doi: 10.1016/S0921-3449(99)00060-9
[10]	Flatt R J, Roussel N, Cheeseman C R. Concrete: An eco material that needs to be improved. J Eur Ceram Soc, 2012, 32(11): 2787 doi: 10.1016/j.jeurceramsoc.2011.11.012
[11]	Gijbels K, Iacobescu R I, Pontikes Y, et al. Alkali-activated binders based on ground granulated blast furnace slag and phosphogypsum. Constr Build Mater, 2019, 215: 371 doi: 10.1016/j.conbuildmat.2019.04.194
[12]	Jiang H Q, Qi Z J, Yilmaz E, et al. Effectiveness of alkali-activated slag as alternative binder on workability and early age compressive strength of cemented paste backfills. Constr Build Mater, 2019, 218: 689 doi: 10.1016/j.conbuildmat.2019.05.162
[13]	Gorai B, Jana R K, Premchand. Characteristics and utilisation of copper slag—a review. Resour Conserv Recycl, 2003, 39(4): 299 doi: 10.1016/S0921-3449(02)00171-4
[14]	吳凡, 楊發光, 肖柏林, 等. 鋼渣摻量對膏體早期強度及流變特性的影響. 材料導報, 2021, 35(3):3021 doi: 10.11896/cldb.20050029 Wu F, Yang F G, Xiao B L, et al. Influence of steel slag dosage on early age strength and rheological properties of paste. Mater Rep, 2021, 35(3): 3021 doi: 10.11896/cldb.20050029
[15]	Teng S, Lim T Y D, Sabet Divsholi B. Durability and mechanical properties of high strength concrete incorporating ultra fine Ground Granulated Blast-furnace Slag. Constr Build Mater, 2013, 40: 875 doi: 10.1016/j.conbuildmat.2012.11.052
[16]	肖柏林, 苗勝軍, 高謙, 等. 冶金渣膠結材料對超細全尾砂的固化特性研究. 中國有色金屬學報,http://kns.cnki.net/kcms/detail/43.1238.TG.20210820.1435.011.html Xiao B L, Miao S J, Gao Q, et al. Study on solidification characteristics of metallurgical slag binder materials for ultra-fine tailings backfill. Chin J Nonferrous Met,http://kns.cnki.net/kcms/detail/43.1238.TG.20210820.1435.011.html
[17]	Qi C C, Fourie A, Chen Q S, et al. A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill. J Clean Prod, 2018, 183: 566 doi: 10.1016/j.jclepro.2018.02.154
[18]	楊嘯, 楊志強, 高謙, 等. 混合充填骨料膠結充填強度試驗與最優配比決策研究. 巖土力學, 2016, 37(增刊2): 635 Yang X, Yang Z Q, Gao Q, et al. Cemented filling strength test and optimal proportion decision of mixed filling aggregate. Rock Soil Mech, 2016, 37(Suppl 2): 635
[19]	馬修元, 段鈺鋒, 劉猛, 等. 基于PSO-BP神經網絡的水焦漿管道壓降預測. 中國電機工程學報, 2012, 32(5):54 doi: 10.13334/j.0258-8013.pcsee.2012.05.005 Ma X Y, Duan Y F, Liu M, et al. Prediction of pressure drop of coke water slurry flowing in pipeline by PSO-BP neural network. Proc CSEE, 2012, 32(5): 54 doi: 10.13334/j.0258-8013.pcsee.2012.05.005
[20]	Pourbasheer E, Riahi S, Ganjali M R, et al. Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. Eur J Med Chem, 2009, 44(12): 5023 doi: 10.1016/j.ejmech.2009.09.006
[21]	馬礪, 張鵬宇, 郭睿智, 等. 巷道火災密閉過程煙氣溫度預測的GA-SVM模型. 中國礦業大學學報, 2021, 50(4):641 doi: 10.13247/j.cnki.jcumt.001309 Ma L, Zhang P Y, Guo R Z, et al. GA-SVM model for prediction flue gas temperature of roadway fire under sealing process. J China Univ Min Technol, 2021, 50(4): 641 doi: 10.13247/j.cnki.jcumt.001309
[22]	畢娟, 李希建. 基于博弈論組合賦權灰靶模型的煤礦安全綜合評價. 中國安全生產科學技術, 2019, 15(7):113 doi: 10.11731/j.issn.1673-193x.2019.07.018 Bi J, Li X J. Comprehensive evaluation of coal mine safety based on grey target model with combination weighting of game theory. J Saf Sci Technol, 2019, 15(7): 113 doi: 10.11731/j.issn.1673-193x.2019.07.018
[23]	溫震江, 高謙, 王忠紅, 等. 基于RSM-DF的礦渣膠凝材料復合激發劑配比優化. 巖石力學與工程學報, 2020, 39(增刊1): 3103 Wen Z J, Gao Q, Wang Z H, et al. Optimization of compound activator ratio of the ground granulated blast furnace slag powder cementitious material based on RSM-DF. Chin J Rock Mech Eng, 2020, 39(Suppl 1): 3103
[24]	李茂輝, 楊志強, 王有團, 等. 粉煤灰復合膠凝材料充填體強度與水化機理研究. 中國礦業大學學報, 2015, 44(4):650 doi: 10.13247/j.cnki.jcumt.000365 Li M H, Yang Z Q, Wang Y T, et al. Experiment study of compressive strength and mechanical property of filling body for fly ash composite cementitious materials. J China Univ Min Technol, 2015, 44(4): 650 doi: 10.13247/j.cnki.jcumt.000365
[25]	董越, 楊志強, 高謙. 鋼渣取代量對復合充填膠凝材料性能的影響. 硅酸鹽通報, 2016, 35(9):2967 doi: 10.16552/j.cnki.issn1001-1625.2016.09.048 Dong Y, Yang Z Q, Gao Q. Effect of steel slag substitution on the properties of composite cementitious backfill material. Bull Chin Ceram Soc, 2016, 35(9): 2967 doi: 10.16552/j.cnki.issn1001-1625.2016.09.048
[26]	崔孝煒, 倪文, 任超. 鋼渣礦渣基全固廢膠凝材料的水化反應機理. 材料研究學報, 2017, 31(9):687 doi: 10.11901/1005.3093.2016.741 Cui X W, Ni W, Ren C. Hydration mechanism of all solid waste cementitious materials based on steel slag and blast furnace slag. Chin J Mater Res, 2017, 31(9): 687 doi: 10.11901/1005.3093.2016.741