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Abstract

Coal mine paste backfilling (CMPB) technology is developed rapid recently as a sustainable and green mining technology. As the application of paste technology matures gradually, it is developing towards fine research, aiming at improving filling quality and reducing filling cost. To increase feeding speed and improve weighing accuracy of the coal mine paste backfilling weighing system (CMPBWS), the mathematical weighing progress model of CMPBWS is established and the weighing control system is optimized based on the adaptive iterative algorithm. The weighing process is divided into three stages, which are the rapid feeding stage, the lower feeding stage, and the prediction feeding stage. The weighing speed of each stage is controlled with different ways. The adaptive iterative learning control method (AILCM) is introduced and used in the prediction feeding stage. The advance stop value is dynamically modified by the AILCM. The numerical simulation study shows that the actual value is much closer to the set value after several iterations by the AILCM. With the method proposed in the paper, the weighing accuracy and feeding speed of CMPBW are both improved.

Keywords

Paste backfilling weighing system feeding speed weighing accuracy adaptive iterative learning control advance stop value

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Precision weighing control of coal mine paste backfilling weighing system

Abstract

Keywords

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