Abstract
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.
Highlights
Copper is one of the oldest metals discovered by human beings
Single hidden layer Extreme learning machine is a kind of feedforward neural network
Principal component analysis (PCA) is used to simplify the spectral data, and the cumulative contribution rate can reach 99.8% when the principal component dimension is 15, and the 15 dimensional principal component is used as the input of the network
Summary
Copper is one of the oldest metals discovered by human beings. About 7000 years ago, casting of bronze ware appeared in Eurasia [1]. The proven reserves of copper resources in the world are. The top ten countries with global copper reserves are Chile, the United States, Peru, Congo (Kinshasa), Australia, China, Russia, Mexico, Canada, and Argentina [2]. With the progress of the times, the continuous innovation of science and technology and the continuous development of world industrialization, the applications of copper are more and more extensive. Single hidden layer Extreme learning machine is a kind of feedforward neural network It of input layer, one hidden layer and output layer.
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