Detecting the backfill pipeline blockage and leakage through an LSTM-based deep learning model

Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine backfill.The pipeline system for gravity-transport high-density backfill(GHB)is complex.Specifically designed,efficient,and accurate abnormal pipeline detection methods for GHB are rare.This work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage diagnosis.First,an industrial pipeline monitoring system was introduced using pressure and flow sensors.Second,blockage and leakage field experiments were designed to solve the problem of negative sample deficiency.The pipeline's statistical characteristics with different working statuses were analyzed to show their complexity.Third,the architecture of the LSTM-DL model was elaborated on and evaluated.Finally,the LSTM-DL model was compared with state-of-the-art(SOTA)learning algorithms.The results show that the backfilling cycle comprises multiple working phases and is intermittent.Although pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different cycles.Plugging causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation range.Among the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage states.The proposed model can accurately recognize various pipeline statuses of complex GHB systems.

Key theory and technology of cemented paste backfill for green mining of metal mines

To reduce or eliminate environmental damage during mining processes,green mining practices have emerged as a focal point in China's metal mining research.Cemented paste backfll technology plays a pivotal role in promoting green mining within the metal industry.The technology allows safely backflling of surface tailings into underground mining airspaces,effectively addressing the challenges associated with tailings storage and underground goaves.In this paper,we introduce the paste rheology theory system,which forms the theoretical backbone of cemented paste backfll.We delve into key technologies such as paste thickening,mixing,transportation,and the use of economical,low-carbon materials.Additionally,we analyze macro and micromechanical properties,in-situ performance monitoring,barricade construction,intelligent control,and numerical simulations of the process.We establish several demonstration projects,both domestic and international,that utilize cemented paste backfill technology to foster greener mining practices.Cemented paste backfill technology is widely used all over the world.It has evolved from its initial stages to being recognized as an advanced application by various ministries and commissions:Ultimately,we propose future research directions for cemented paste backfill technology in the context of eco-friendly metal mining.These perspectives encompass theory,technology,equipment,and mode,which can strongly contribute to the sustainability of the mining industry in China.