Rheological properties of cemented paste backfill and the construction of a prediction model

Abstract

The Cemented Paste Backfill (CPB) yield stress is a key rheological parameter for paste filling technology, which has significant practical value for pipeline optimization and equipment selection of pipeline conveying systems. However, the slurry yield stress is affected by many factors. In order to accurately analyze and predict the CPB yield stress, this study uses the sparrow search algorithm to optimize the relevance vector machine (SSA-RVM) and proposes the prediction model of SSA-RVM CPB yield stress regression. Based on 136 sets of rheological tests for copper mine, different waste rock/tailing sand ratios, mass concentrations, and water-cement ratios select to predict the yield stress at different training set ratios (78%, 85%, 92%). Compared with the traditional relevance vector machine (RVM) regression model, the SSA-RVM regression prediction model has higher prediction accuracy. In addition, the coefficient of determination R2 of the predicted and true values obtained from the SSA-RVM model increased by 0.0407, 0.0438, and 0.0500 for the training set ratios of 78%, 85%, and 92%, respectively. The results suggested that SSA-RVM can efficiently predict the CPB yield stress, which can be a reference for the design of paste-filled pipe conveying systems.

Keywords

• Cemented paste backfill
• Yield stress
• Prediction
• Optimization
• Machine learning