Implementation of intelligent upgrading for the raw coal refuse discharging system in Jiangzhuang Coal Mine

To overcome the bottlenecks of high labor intensity, high operating costs, and low intelligence levels in the manual separating process of the raw coal refuse discharging system, Jiangzhuang Coal Mine implemented a full-process intelligent reconstruction of its “shallow-depth heavy medium bath & manual reverse separating” process (in operation since 2014). This was achieved through the coupling and integration of control technologies: “50～200 mm intelligent dry separating, >200 mm LIDAR separating, and >50 mm total refuse crushing.” The study utilizes a three-stage roller screen classification as the starting point. Raw coal blocks of 50～200 mm are directed into an X-ray & vision dual-mode intelligent dry separating system, while blocks >200 mm are identified by 3D LIDAR and diverted at high speed via guide plates. All refuse >50 mm is reduced to <80 mm by a double-toothed roll crusher and transported via an enclosed pipe belt conveyor. Simultaneously, a centralized control platform was embedded to integrate data from seven stages—feeding, screening, crushing, separating, transportation, dust extraction, and video monitoring—into the dispatch center, enabling “one-click start-stop, remote linkage, and fault self-diagnosis.” Production practice demonstrates that the labor efficiency of the raw coal system surged from 320.51 t/shift to 757.57 t/shift, with an annual cost saving of 4.1706 million RMB. Manual separating and mine-car refuse discharge have been eliminated, resulting in zero minor injuries or major accidents for two consecutive years. The system features wet-dry compatibility, allowing a switch to the backup shallow-depth system within 5 minutes to ensure continuous production. This practice establishes an integrated “classification-identification-crushing-centralized control” refuse discharging process. It constructs a demonstration model for aging mines in their resource-depletion stage to reduce manpower through technology, ensure safety via intelligence, and cut costs through intensification, providing a replicable and scalable low-cost intelligent upgrade path for similar coking coal preparation plants.