Rheological Properties of Cemented Tailing Backfill and the Construction of a Prediction Model

Abstract

Workability is a key performance criterion for mining cemented tailing backfill, which should be defined in terms of rheological parameters such as yield stress and plastic viscosity. Cemented tailing backfill is basically composed of mill tailings, Portland cement, or blended cement with supplementary cement material (fly ash and blast furnace slag) and water, among others, and it is important to characterize relationships between paste components and rheological properties to optimize the workability of cemented tailing backfill. This study proposes a combined model for predicting rheological parameters of cemented tailing backfill based on a principal component analysis (PCA) and a back-propagation (BP) neural network. By analyzing experimental data on mix proportions and rheological parameters of cemented tailing backfill to determine the nonlinear relationships between rheological parameters (i.e., yield stress and viscosity) and mix proportions (i.e., solid concentrations, the tailing/cement ratio, the specific weight, and the slump), the study constructs a prediction model. The advantages of the combined model were as follows: First, through the PCA, original multiple variables were represented by two principal components (PCs), thereby leading to a 50% decrease in input parameters in the BP neural network model, which covered 98.634% of the original data. Second, in comparison to conventional BP neural network models, the proposed model featured a simpler network architecture, a faster training speed, and more satisfactory prediction performance. According to the test results, any error between estimated and expected output values from the combined prediction model based on the PCA and the BP neural network was within 5%, reflecting a remarkable improvement over results for BP neural network models with no PCA.

Preparation and Microstructure of Alkali-Activated Rice Husk Ash-Granulated Blast Furnace Slag Tailing Composite Cemented Paste Backfill

“…The tailings were used as filling materials with the addition of paste filling, which solved the problem of tailing accumulation and enabled the full use of resources. Lang L et al [30] studied a predictive model for tailing sand-cemented fillers based on the relationship between major component content and rheological properties. The prediction accuracy was significantly improved by combining the BP neural network with the principal component analysis (PCA) method compared with the BP neural network alone.…”

A Particle Size Distribution Model for Tailings in Mine Backfill

“…Interval distribution, also known as differential distribution or frequency distribution, represents the percentage content of particles in a series of particle size ranges; cumulative distribution represents the percentage content of particles less than specific particle size [37,39]. As the cumulative distribution curve can easily make the cumulative proportion of particles smaller than a specific size, it is widely used in the mine backfill [36,37,[39][40][41]. On the PSD curve, the coarse and fine characteristics of a specific backfill tailing material can be approximately reflected by choosing some points.…”

Simulation of the Compressive Strength of Cemented Tailing Backfill through the Use of Firefly Algorithm and Random Forest Model

“…Filling materials should be provided with a wide range of sources, low price, easy preparation, and transportation, and the formed filling body should have the required physical and mechanical properties and chemical properties. Among these filling materials, the cemented tailing backfill plays an important role [4][5][6]. e cement tailings backfill has been widely used in the underground mines in China, and its development trend is increasing due to the many technical and economic benefits it provides.…”