Journal of Sensors

Volume 2018, Article ID 6357905, 9 pages

https://doi.org/10.1155/2018/6357905

## Study of the Magnetic Properties of Haematite Based on Spectroscopy and the IPSO-ELM Neural Network

^{1}Intelligent Mine Research Center, Northeastern University, Shenyang 110819, China^{2}Information Science & Engineering School, Northeastern University, Shenyang 110004, China^{3}School of Science, Shenyang Jianzhu University, Shenyang 1100000, China^{4}College of Control Technology, Le Quy Don Technical University, Hanoi 100000, Vietnam

Correspondence should be addressed to Dong Xiao; nc.ude.uen.esi@gnodoaix

Received 14 July 2018; Revised 26 August 2018; Accepted 24 September 2018; Published 28 November 2018

Academic Editor: Harith Ahmad

Copyright © 2018 Yachun Mao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The detection of the magnetic properties of haematite plays an important role in the adjustment of the beneficiation process of haematite and the improvement of metal recovery. The existing methods for measuring the magnetic properties of iron ore either have large errors or take a long time. Therefore, it is very necessary to find a method that can quickly and accurately detect the magnetic properties of haematite. This paper presents a method to detect the magnetic properties of haematite based on the extreme learning machine based on the improved particle swarm optimization (IPSO-ELM) algorithm and spectroscopy. The improved particle swarm optimization algorithm is used to optimize the input weights, hidden layer deviations, and hidden layer nodes of the ELM network. Introducing the linear decreasing inertia weight for the particle swarm algorithm, taking into account the norm of the output weight in the particle update process and using the variation idea to change the length of the particle give the IPSO-ELM better stability and generalization ability. The experimental results show that the IPSO-ELM prediction model has a good prediction performance and has better generalization ability than that of the ELM and PSO-ELM prediction models. Compared with traditional chemical analysis methods and manual methods, this method has great advantages in terms of economy, speed, and accuracy.

#### 1. Introduction

Iron ore is the main raw material for production of steel, and haematite is one of the main types of iron ore. The beneficiation process of iron ore is an important process in the production process of iron ore. The choice of beneficiation process plays a decisive role in the quality of iron ore. Taking the Anshan mining area as an example, the iron content of the ore is 20–40%, with an average of 30%. Ore must be beneficiated, and the iron content after selection can reach more than 60%. For haematite, the detection of its magnetic property will play an important role in the adjustment of its beneficiation process and the improvement of metal recovery. Studies have shown that [1] with the haematite having magnetic properties above 20%, if the weak magnetic separation process is adopted, the tailings grade is only approximately 7%, and the metal recovery rate is above 87%. Haematite with magnetic properties between 10% and 20% uses a weak magnetic separation process in which the tailings grade will increase to approximately 14%, and the metal recovery rate will be between 64% and 70%. For the haematite with its magnetic properties below 10%, if a single weak magnetic separation process is used, the recovery rate of metals will be very low, and a joint process consisting of strong magnetic separation, reelection, and flotation is required. There are two existing methods for detecting the magnetic property of existing iron ore. One method is to use a magnetometer to detect it. The speed of this method is relatively fast. However, in the course of use, the magnetometer is susceptible to interference from the surrounding environment and generates large errors, and the percentage of magnetic iron ore samples cannot be accurately determined [2]. The second method is the acid dissolution assay method. This method can accurately detect the content of magnetic iron ore in iron ore. However, this method is expensive and takes a long time. Therefore, to find a method that can quickly and accurately detect the magnetic properties of haematite is of great significance to the selection of the beneficiation process of haematite ore. Spectral analysis is a method of identifying substances and determining their chemical composition and relative content based on the spectrum of a substance. This method has been widely used in classification, the grade identification of rocks and minerals, food quality inspection, and many other aspects due to its advantages of a high analysis speed, simple operation, low cost, and high efficiency [3–6]. The factors influencing the magnetic property of iron ore are mainly the content of the magnetic minerals in iron ore. Since the oxides of silicon, magnesium, and calcium in haematite will affect the spectral data of haematite, the visible and infrared spectral data of haematite often contain considerable chemical information unrelated to the detection of the magnetic properties of this mineral, which makes the spectral data of haematite high dimensional and contain much redundant information. Therefore, the amount of spectral data of haematite must be reduced. Principal component analysis (PCA) [7] uses the idea of dimensionality reduction to convert multiple indicators into several comprehensive indicators (principal components), each of which can reflect most of the information of the original variables, and the information contained is not duplicated.

The extreme learning machine (ELM) neural network is a kind of single hidden layer feed-forward neural network proposed by Huang et al. in 2004, which has the advantages of fast learning and strong generalization [8]. Due to its advantages of fast learning and strong generalization ability, the algorithm has been widely studied and applied in recent years [9–11]. In the literature [12], an evolutionary ELM (E-ELM) was proposed which used the differential evolutionary algorithm to select the input weights and Moore-Penrose (MP) generalized inverse to analytically determine the output weights. In the literature [13], an improved ELM was proposed by selecting input weights for an ELM with linear hidden neurons. This approach maintains testing accuracy with stable condition, but it was only limited to ELM with linear hidden neurons. In recent years, many studies have focused on the structural improvements of ELM, such as ELM with double hidden layers [14] and ELM with multiple hidden layers [15, 16]. The spectral data of haematite is a set of highly coupled and nonlinear matrices. Extreme learning machine, as a neural network model in the field of machine learning, has a powerful function in dealing with nonlinear fitting problems. Therefore, the ELM is used to process the spectral data of haematite to obtain the detection model of the haematite magnetic properties.

However, the ELM randomly selects the input weights and hidden biases, and there will be some weights and biases of 0. This approach will cause the ELM to require a large number of hidden layer nodes to achieve the desired effect, and there will be large differences in the results of each operation. The particle swarm optimization (PSO) algorithm is a global optimization algorithm proposed by Dr. Eberhart and Dr. Kennedy in 1995 [17]. Shi and Eberhart introduced a new parameter-inertial weight for particle swarm optimization, which enhanced the ability of the algorithm [18]. Because the PSO is relatively simple and easy to implement compared to other optimization algorithms, and since there are no excessive parameters to be adjusted, it has attracted the attention of many scholars. New achievements have been continuously made in the performance improvement and analysis of algorithms [19, 20], which are widely used in many areas [21]. In the literature [22], a modified particle swarm optimization (PSO) algorithm was presented; in this algorithm, the whole search space is divided into several grid cells improved the efficiency of the algorithm. In addition, simulated annealing is incorporated into the update of a particle’s local leader to prevent premature convergence of the swarm. The literature [23] presents the first study of multiobjective particle swarm optimization (PSO) for cost-based feature selection problems. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO.

Therefore, the particle swarm optimization algorithm can be used to optimize the ELM input weights and hidden layer biases. In 2006, Xu and Shu proposed an extreme learning machine based on particle swarm optimization (PSO-ELM) [24]. To improve the performance of the PSO algorithm, Han et al. improved the generalization ability of the PSO-ELM by considering the norm of the output weight matrix [25]. However, the above improvement method does not consider the selection of the number of nodes in the hidden layer of the ELM neural network. Li et al. [26] introduced the mutation operator to optimize the number of hidden layer nodes of the ELM, which enhanced the population diversity and improved the convergence speed of the algorithm, but its generalization ability was poor. Based on the improvement of traditional ELM and the research on PSO, this paper uses the improved particle swarm optimization (IPSO) algorithm, which introduces the linearly decreasing inertia weight for the particle swarm algorithm, applies the idea of mutation to change the length of the particle, and considers the norm of the output weight in the process of particle updating in order to optimize the input weights, the hidden biases, and the number of hidden layer nodes of the ELM neural network. This approach improves the generalization ability and improves the convergence speed of the algorithm. An extreme learning machine based on the improved particle swarm optimization (IPSO-ELM) was used to process the spectral data of haematite to obtain a model for detecting the magnetic properties of haematite.

The key issues that need to be solved in this paper are three aspects: data acquisition, data processing, and model building. In the data acquisition phase, haematite samples from the mining area need to be collected and subjected to spectral and chemical tests to obtain spectral data of the sample and accurate magnetic properties. In the data processing stage, high-dimensional haematite spectral data needs to be dimensioned to facilitate the establishment of the model. In the model establishment stage, it is necessary to use the spectral data after the dimension reduction and the accurate magnetic rate to establish the haematite magnetic permeability detection model based on the IPSO-ELM algorithm proposed in this paper. The test results were analyzed to verify the feasibility of detecting the magnetic properties of haematite based on spectral data and ELM algorithm.

#### 2. Related Work

##### 2.1. Sample Preparation and Spectral Testing

The Anshan iron ore mine of Liaoning Province is one of the major iron ore mines in China, and the main type of iron ore in this area is haematite. Therefore, this study selected two mining areas in Anshan as experimental areas and collected haematite samples at the site. The sample is block shaped and the size is approximately 30 cm × 30 cm × 30 cm.

Core drilling and cutting are performed on the collected samples. During the processing of the experimental samples, the core is corroded along the direction of the silicon and iron formed in the ore and then cut along the direction perpendicular to the core cylinder to prepare circular lamellar experimental samples with a diameter of 6 cm and a thickness of 0.5–1 cm, as shown in Figure 1. During cutting, the thickness of the sample is as thin as possible in order to ensure that the distribution of various mineral components on the surface of the experimental sample is consistent with the overall sample and that the spectral test results of the sample have a good correspondence relationship with the chemical composition test results.