Fu Yanhua, Xie Hongfei, Mao Yachun, Ren Tao, Xiao Dong
JangHo Architecture College, Northeastern University, Shenyang 110169, China.
Information Science and Engineering School, Northeastern University, Shenyang 110004, China.
Sensors (Basel). 2020 Nov 27;20(23):6780. doi: 10.3390/s20236780.
Copper is an important national resource, which is widely used in various sectors of the national economy. The traditional detection of copper content in copper ore has the disadvantages of being time-consuming and high cost. Due to the many drawbacks of traditional detection methods, this paper proposes a new method for detecting copper content in copper ore, that is, through the spectral information of copper ore content detection method. First of all, we use chemical methods to analyze the copper content in a batch of copper ores, and accurately obtain the copper content in those ores. Then we do spectrometric tests on this batch of copper ore, and get accurate spectral data of copper ore. Based on the data obtained, we propose a new two hidden layer extreme learning machine algorithm with variable hidden layer nodes and use the regularization standard to constrain the extreme learning machine. Finally, the prediction model of copper content in copper ore is established by using the algorithm. Experiments show that this method of detecting copper ore content using spectral information is completely feasible, and the algorithm proposed in this paper can detect the copper content in copper ores faster and more accurately.
铜是一种重要的国家资源，广泛应用于国民经济的各个部门。传统的铜矿石中铜含量检测方法存在耗时且成本高的缺点。由于传统检测方法存在诸多弊端，本文提出了一种新的铜矿石中铜含量检测方法，即通过光谱信息检测铜矿石含量的方法。首先，我们采用化学方法对一批铜矿石中的铜含量进行分析，准确获取这些矿石中的铜含量。然后对这批铜矿石进行光谱测试，得到铜矿石准确的光谱数据。基于所获得的数据提出了一种新的具有可变隐藏层节点的双隐藏层极限学习机算法，并采用正则化准则对极限学习机进行约束。最后利用该算法建立了铜矿石中铜含量的预测模型。实验表明，这种利用光谱信息检测铜矿石含量的方法完全可行，本文所提出的算法能够更快、更准确地检测铜矿石中的铜含量。
