Abstract

With the increasing importance of environmental protection, more and more attention has been paid to backfilling coal mining. The prediction and design of the proportioning of backfill materials are significantly important for paste backfilling coal mining. In this study, the key factors affecting paste slurry mass optimization are identified, including mass concentration, solid-cement ratio, and fly ash-cement ratio. According to the identified factors, a paste slurry mass prediction numerical model is established to effectively predict the paste slurry mass of backfilling mining, based on a hybrid algorithm of the back propagation neural network (BPNN) with chaos optimization (CO). With the adopted model indicators, the orthogonal test data of backfill materials are used as samples for learning, training and testing. The performance of the COBPNN and traditional BPNN models are assessed using experimental results. Finally, a mechanical model of gob-side entry retaining is development, and the surface convergence of surrounding rock of roadway and the internal load of filling body are detected in the tested working face. The results indicate that the proposed model has improved the performance of the traditional BPNN model, in terms of tardy convergence, low convergence precision, proneness to local minimum, enables high fitting accuracy and strong generalization, and more accurate and reliable prediction for the paste slurry mass, thus providing a more effective approach for optimizing the proportioning of paste slurry for backfilling coal mining.