Milling is among the most energy-consuming technological stages of copper ore processing. It is performed in mills, which are machines of high rotational masses. The start of a mill filled to capacity requires appropriate solutions that mitigate the overloading. One method for increasing the energy efficiency of ball mills is to optimize their drive systems. This article looks at two variants of drive systems with efficiencies higher than the already existing solutions. The first variant is a low-speed synchronous motor with permanent magnets without a gearbox, and the second variant is an asynchronous high-efficiency motor with a gearbox and a fluid coupling. The energy performance analysis of the three solutions was based on the average energy consumption indicator per mass unit of the milled material and on the energy consumption per hour. The investigations required models of the drive systems and analyses with the use of the Monte Carlo methods. The highest energy efficiency is observed in the case of the solution based on the permanent magnet motor. However, the drive system with the high-speed motor offers a gentle start-up possibility owing to the fluid coupling.
The article presents the detection of damage to rollers based on the transverse vibration signal measured on the conveyor belt. A solution was proposed for a wireless measuring device that moves with the conveyor belt along of the route, which records the signal of transverse vibrations of the belt. In the first place, the research was conducted in laboratory conditions, where a roller with prepared damage was used. Subsequently, the process of validating the adopted test procedure under real conditions was performed. The approach allowed to verify the correctness of the adopted technical assumptions of the measuring device and to assess the reliability of the acquired test results. In addition, an LSTM neural network algorithm was proposed to automate the process of detecting anomalies of the recorded diagnostic signal based on designated time series. The adopted detection algorithm has proven itself in both laboratory and in-situ tests.
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