Random Forest Slurry Pressure Loss Model Based on Loop Experiment

A reasonable arrangement of filling pipelines can solve the problems of low line magnification, a high flow rate, large pipe pressure, etc., in deep well filling slurry transportation. The transportation pressure loss value of filling slurry is the main parameter for the layout design of filling pipelines. At present, pressure loss data are mainly obtained through the loop pipe experiment, which has problems such as a large amount of labor, high cost, low efficiency, and a limited amount of experimental data. In this paper, combined with a new generation of artificial intelligence technology, the random forest machine learning algorithm is used to analyze and model the experimental data of a loop pipe to predict the pressure loss of slurry transportation. The degree of precision reaches 0.9747, which meets the design accuracy requirements, and it can replace the loop pipe experiment to assist with the filling design

Random Forest Slurry Pressure Loss Model Based on Loop Experiment

A reasonable arrangement of filling pipelines can solve the problems of low line magnification, a high flow rate, large pipe pressure, etc., in deep well filling slurry transportation. The transportation pressure loss value of filling slurry is the main parameter for the layout design of filling pipelines. At present, pressure loss data are mainly obtained through the loop pipe experiment, which has problems such as a large amount of labor, high cost, low efficiency, and a limited amount of experimental data. In this paper, combined with a new generation of artificial intelligence technology, the random forest machine learning algorithm is used to analyze and model the experimental data of a loop pipe to predict the pressure loss of slurry transportation. The degree of precision reaches 0.9747, which meets the design accuracy requirements, and it can replace the loop pipe experiment to assist with the filling design

Application of Extended Set Pair Analysis on Wear Risk Evaluation of Backfill Pipeline

Filling slurry can inevitably cause irreversible wear to the pipeline, which represents great costs to mines. This study aims to propose an extended set pair analysis (SPA) for the wear risk evaluation of backfill pipeline. First, to fully describe the wear risk of backfill pipeline, an evaluation index system was established from the aspects of slurry characteristics, pipeline properties, and slurry flow state. Then, the experts grading method was modified with probabilistic linguistic term sets (PLTSs) to obtain subjective weights. Meanwhile, the criteria importance through intercriteria correlation (CRITIC) approach was used to calculate objective weights. By introducing a preference coefficient, they were integrated to determine the comprehensive weights. After that, the classical SPA was extended with membership functions and fuzzy entropy theory, so that the wear risk of backfill pipeline can be evaluated from the perspectives of both the risk level and complexity. Finally, the proposed methodology was applied to assess the wear risk in the Jinchuan nickel mine, Dahongshan copper mine, Hedong gold mine, and Xincheng gold mine. The reliability of evaluation results was further verified through sensitivity and comparative analyses. Results indicate that the proposed methodology is feasible for the wear risk evaluation of backfill pipeline, and can provide guidance on the wear risk management

Application of Extended Set Pair Analysis on Wear Risk Evaluation of Backfill Pipeline

Filling slurry can inevitably cause irreversible wear to the pipeline, which represents great costs to mines. This study aims to propose an extended set pair analysis (SPA) for the wear risk evaluation of backfill pipeline. First, to fully describe the wear risk of backfill pipeline, an evaluation index system was established from the aspects of slurry characteristics, pipeline properties, and slurry flow state. Then, the experts grading method was modified with probabilistic linguistic term sets (PLTSs) to obtain subjective weights. Meanwhile, the criteria importance through intercriteria correlation (CRITIC) approach was used to calculate objective weights. By introducing a preference coefficient, they were integrated to determine the comprehensive weights. After that, the classical SPA was extended with membership functions and fuzzy entropy theory, so that the wear risk of backfill pipeline can be evaluated from the perspectives of both the risk level and complexity. Finally, the proposed methodology was applied to assess the wear risk in the Jinchuan nickel mine, Dahongshan copper mine, Hedong gold mine, and Xincheng gold mine. The reliability of evaluation results was further verified through sensitivity and comparative analyses. Results indicate that the proposed methodology is feasible for the wear risk evaluation of backfill pipeline, and can provide guidance on the wear risk management