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 -...
Saved in:
| Published in: | Construction & building materials Vol. 418; p. 135432 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
08.03.2024
|
| Subjects: | |
| ISSN: | 0950-0618, 1879-0526 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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 R2 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.
[Display omitted]
•The composition of pure gangue backfilling slurry (PGBS) contains only gangue sand (GS) of particle size grading and water.•A multi-objective optimization design method of mixture design and differential evolution algorithm was employed.•The fluidity, bleeding rate, and uniaxial compressive strength (UCS) of PGBS are investigated experimentally.•The powder (<10 µm) uniformly adheres to the surface of GS with different particle sizes, which significantly affects the transport characteristics and mechanical properties of PGBS. |
|---|---|
| ISSN: | 0950-0618 1879-0526 |
| DOI: | 10.1016/j.conbuildmat.2024.135432 |