Mill Feed Control System and Algorithm Based on Python
Open Access
- 24 June 2022
- Vol. 12 (7), 804
- https://doi.org/10.3390/min12070804
Abstract
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.Keywords
This publication has 20 references indexed in Scilit:
- Fine grinding: How mill type affects particle shape characteristics and mineral liberationMinerals Engineering, 2017
- Block factorization of step response model predictive control problemsJournal of Process Control, 2017
- A specific energy-based ball mill model: From batch grinding to continuous operationMinerals Engineering, 2016
- Hybrid modeling of an industrial grinding-classification processPowder Technology, 2015
- Soft sensing of particle size in a grinding process: Application of support vector regression, fuzzy inference and adaptive neuro fuzzy inference techniques for online monitoring of cement finenessPowder Technology, 2014
- Modeling and optimization of high chromium alloy wear in phosphate laboratory grinding mill with fuzzy logic and particle swarm optimization techniqueMinerals Engineering, 2010
- Implementation of a Multivariable Controller for Grinding-Classification ProcessIFAC Proceedings Volumes, 2009
- Grinding mill circuits — A survey of control and economic concernsInternational Journal of Mineral Processing, 2008
- Self-tuning adaptive control for an industrial weigh belt feeder.ISA Transactions, 2003
- Fuzzy setsInformation and Control, 1965