基于支持向量机的煤矿料浆液位智能测量系统

Hua Guo, Xuejing Zhang, Wenya Yang, Jinshan Zhuang, Mengjuan Zhu, Le Kong, Qinglin Han, Zhiying Zhang
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引用次数: 0

摘要

充填采矿技术是煤炭开采的新趋势。本文设计了一种用于制浆站料浆液位测量的智能传感器系统。采用多传感器数据融合和SVM算法对采集的液位值进行训练,与BP神经网络相比,该方法得到的液位预测结果的MSE降低了73.7%。该系统不仅可以解决目前煤矿充填系统搅拌槽料浆液位需要人工监测的现状,也可用于其他复杂工业应用中对液位的精确测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent slurry level measurement system of coal mine based on SVM
The filling mining technology is a new trend in coal mining. This paper designs an intelligent sensor system for slurry level measurement of pulping station. Multi-sensor data fusion and the SVM algorithm are adopted for training of collected liquid level values, and compared with the BP neural network, the MSE of the liquid level prediction results obtained using our method is 73.7% lower. This system cannot only address the current state requiring manual monitoring of mixing tank slurry level in coal mine filling system, but can also be used to accurately measure the liquid levels in other complicated industrial applications.
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