Grinding is an important link in the process of mineral processing. It plays a vital role in mineral processing by optimizing the grinding process, improving the quality of grinding products and ensuring the follow-up operation indicators. In this paper, the Python language, intelligent theoretical control technology and mineral processing were combined to solve the problem of ore feeding control in mineral processing. Using error factor analysis, an extended control algorithm was designed. The NumPy library and data collected from the Yuan YangMou concentrator in China were used to quantitatively analyze the factors affecting the error of electronic belt scales. This paper introduces the use of Kalman filtering for electronic belt scale weight data to reduce the effect of noise and hence reduce errors. The factors affecting the process of mill feeding are also analyzed. The core ideas and methods of fuzzy control theory are summarized, and a Python-based fuzzy controller suitable for the mill feeding process that improves the overall robustness and accuracy of feeding system is implemented.

Using Python as a programming language, this study investigates the problem of controlling the ore amount in the field of mineral processing. First, data on the influencing factors collected from a certain beneficiation mill in Yuanyang are quantitatively analyzed using the NumPy module library. Factors having a greater influence are screened out and then selected as the input while the motor frequency is generated as the output by the fuzzy control algorithm developed using the SciKit Fuzzy module library. The range of values of the fuzzy control variable is defined, the fuzzy membership function is generated, and the fuzzy control rule is established. Finally, the fuzzy controller is activated to realize fuzzy control of the mine. The NumPy algorithm can be effectively applied to the quantitative analysis of data, and the calculation results are reasonable and interpretable. The simulation results are as follows: the hardness of the raw ore and the weight of the belt scale are the key factors for controlling the ore amount, and they can be used as the input variables of the fuzzy control system. The fuzzy controller developed using the SciKit Fuzzy module library can be effectively applied to the field of mineral processing, and it overcomes the limitations of current computer technology in industrial mineral processing.