Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

Abstract

The utilization of solid waste for filling mining presents substantial economic and environmental advantages, making it the primary focus of current filling mining technology development. To enhance the mechanical properties of superfine tailings cemented paste backfill (SCPB), this study conducted response surface methodology experiments to investigate the impact of various factors on the strength of SCPB, including the composite cementitious material, consisting of cement and slag powder, and the tailings’ grain size. Additionally, various microanalysis techniques were used to investigate the microstructure of SCPB and the development mechanisms of its hydration products. Furthermore, machine learning was utilized to predict the strength of SCPB under multi-factor effects. The findings reveal that the combined effect of slag powder dosage and slurry mass fraction has the most significant influence on strength, while the coupling effect of slurry mass fraction and underflow productivity has the lowest impact on strength. Moreover, SCPB with 20% slag powder has the highest amount of hydration products and the most complete structure. When compared to other commonly used prediction models, the long-short term memory neural network (LSTM) constructed in this study had the highest prediction accuracy for SCPB strength under multi-factor conditions, with root mean square error (RMSE), correlation coefficient (R), and variance account for (VAF) reaching 0.1396, 0.9131, and 81.8747, respectively. By optimizing the LSTM using the sparrow search algorithm (SSA), the RMSE, R, and VAF improved by 88.6%, 9.4%, and 21.9%, respectively. The research results can provide guidance for the efficient filling of superfine tailings.

Autonomous Process Execution Control Algorithms of Solid Intelligent Backfilling Technology: Development and Numerical Testing

“…For example, as shown in Figure 8, the tamping device has multiple paths to choose from, such as Paths I and II, from the current position A to the target position B in the movable paths set. From Equation (7), it is evident that both paths have the same costs as angle adjustments. Path I had a pose interference, Z = 0, and this path was discarded; Path II had no interference, Z = 1, and Path II was selected.…”

“…The existing backfilling methods, such as tailings and cemented backfilling, based on industrial pumps and the pipeline transportation of cementitious materials, achieved a significant development in intelligent research and initially achieved intelligent control of the backfilling material, key parameters, and core processes [7,8]. However, the SBM method was based on the collaborative control of backfilling hydraulic support and a perforated bottom discharge scraper conveyor, and the difficulty lay in the high number of key parameters and intricate core procedure.…”

Preparation and Mechanical Properties of Dolomite Dust-Emulsified Asphalt Composites

“…[1] In China's industrial field, 85% of the raw materials come from mineral products in mining production. The demand for mineral resources is growing, and the huge mining industry has brought a lot of tailings and waste dust storage [2]. Dolomite is a natural carbonate mineral with abundant reserves, accounting for about 1.7% of the total crust.…”