Abstract
As an important transportation, the belt conveyor has been widely used and researched. It is urgent to solve the problem of energy saving and consumption reduction of belt conveyor. Aiming at reducing high energy consumption in the rated-speed operation of a belt conveyor, the present paper establishes an energy-saving belt-speed model of a belt conveyor using a polynomial regression-fitting algorithm and a small number of sample observations, and proposes a speed regulation strategy and particle swarm optimization–proportional–integral–derivative algorithm for the variable-belt-speed energy-saving control of a belt conveyor based on the material flow rate. The control strategy and algorithm adjust the running speed of the belt conveyor accurately according to changes in the material flow rate, thus reducing damage of frequent speed regulation to the belt conveyor and saving energy. Simulation analysis of a practical case shows that energy-saving belt-speed model, speed regulation strategy, and algorithm effectively reduce the energy consumption of a belt conveyor, and they thus have high application value in coal, ports, power, mine, metallurgy, chemical, and other industries. Further work in this field can be focused on the prediction of material flow rate of belt conveyor, the controllable adjustment duration of algorithm and the reduction of overshoot.
Highlights
The belt conveyor is used for continuous transportation in modern production
It can be seen that the polynomial regression fitting algorithm proposed in this paper does not need complicated theoretical calculation, less sample observation data, better fitting effect, faster fitting speed and simpler and easier operation, which effectively reduces the complexity of obtaining energy-saving belt-speed model of belt conveyor
This paper proposes a polynomial regression-fitting algorithm to establish the energy-saving belt-speed model of a belt conveyor, a speed regulation strategy and the particle swarm optimization (PSO)-PID algorithm
Summary
The belt conveyor is used for continuous transportation in modern production. It has become one of the three main industrial transportation modes along with automobiles and trains, and has been widely used in coal, ports, electricity, power, mining, metallurgy, chemical, and other industries. Wang Xiaowei et al [4] established an energy-saving belt-speed model of the belt conveyor using a radial-basis-function neural network based on the operation sample data of a belt conveyor, but the model error is large. Daijie He et al [5, 6] studied the potential risks of regulating the speed of a belt conveyor and proposed solutions to mitigate those risks. The present paper, on the premise of comparing the fitting results of polynomial regression and a back-propagation (BP) neural network, establishes an energy-saving belt-speed model of a belt conveyor adopting a polynomial regression-fitting algorithm with a small number of sample observations. The research results in this paper can maximize energy savings, and are more suitable for engineering application
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