Power Consumption Model of Permanent-Magnet Direct-Drive Belt Conveyor System Based on GACO–BP

Abstract

Permanent-magnet direct-drive belt conveyors (PMDDBCs) rotate at high speed most of the time, resulting in a large number of invalid energy consumption. To realize the speed regulation of PMDDBC, it is necessary to clarify the relationship between the belt speed, coal quantity of the conveyor and total power of the system. Based on the BP neural network, this paper establishes the power consumption model of PMDDBC, which is related to coal quantity, belt speed and total power. Furthermore, an improved hybrid algorithm (GACO) that combines the advantages of genetic algorithm (GA) and ant colony optimization (ACO) is proposed to optimize the BP power consumption model. The GACO–BP power consumption model is obtained. The original power consumption model is compared with the GACO–BP power consumption model through experiments. Results demonstrate that the GACO–BP power consumption model reduces various prediction errors, while the optimization ability, prediction accuracy and convergence speed are significantly enhanced. It provides a reliable speed regulation basis for the permanent-magnet direct-drive belt conveyor system and also provides a theoretical reference for energy savings and consumption reduction in the coal industry.