Journal of Engineering Research

During the loading operation of C70/C80 large-rated-load freight cars with the rapid quantitative train loading system, the coal volume in the buffer bin suffers from large short-term fluctuations and supply-demand imbalance. To address this problem, an expected coal feeding rate model based on multi-source information fusion is proposed. For the first time, this model performs online fusion of the coal volume in the buffer bin, the cached coal volume on the belt conveyor, loading progress, and train marshalling information, and constructs a piecewise analytical function with time as the independent variable and the expected coal feeding rate as the dependent variable, which serves as the real-time tracking target for the upstream feeding control system. Industrial test results show that compared with the traditional "bin level-threshold" strategy, the expected coal feeding rate model reduces the average loading time per car from 65 seconds to 58 seconds and the number of start-stop cycles of the coal feeder from 3 times per train to 1 time per train, significantly improving loading efficiency and equipment service life.

Rapid quantitative train loading system; Coal volume in the buffer bin; Multi-source information fusion; Expected coal feed rate model