The Occurrence Law of Residual Leaching Reagent and Rare Earth by in-situ Leaching based on K-means

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1 , pp The Occurrence Law of Residual Leaching Reagent and Rare Earth by in-situ Leaching based on K-means Jiang Xu 1,2, Yongjun Ren 1,2, XiuJuan Feng 3, Jin Wang 4 1 School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing , China 2 Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing , China 3 School of architecture & surveying and mapping engineering, Jiangxi University of Science and Technology, Ganzhou , China 4 School of Information Engineering, Yangzhou University, Yangzhou , China renyj100@126.com, jinwang@yzu.edu.cn Abstract. Rare earth is an important strategic resource like the oil resource, and the national pay more and more attention on it. The increasing research on rare earth tailings by in-situ leaching, and improving the utilization of rare earth resources, reducing the impact of tailings on the environment, is very significant. Because the experiment data is messy, so that it is difficult to directly draw the remnants law of rare earth elements, ammonium nitrogen and nitrate. In this paper, we firstly propose an improved k-meaning: optimizing the initial center points through computing the density of data objects. A new evaluation function is proposed, namely equalization function, which enable the cluster number to be generated automatically. Then we utilize the proposed k-means technology to pre-process the experiment data. We split the data to some classes according to the different concentration of leaching solution. And then, the nitric concentration and residual tailings leaching reagent of rare earth are classed based on k-means technique. Experimental study found that, when the concentration of ammonium sulfate is 2.0%, leaching flowing is 1.0ml/min and leaching solution is 400ml per kilogram of tailings, the leaching rate of the rare earth and the concentration of rare earth in the leaching solution were both higher, which will have good economic benefits. Keywords: Rare Earth Ore, In-situ Leaching, K-means, Initial Center Point, Balanced Evaluation Function This research was supported by the NSFC (NO , , ), Jiangsu Province Natural Science Research Program (NO.BK ) and the PAPD fund. ISSN: ASTL Copyright 2017 SERSC

2 1 Introduction The rare earth reserves of China are 36 million tons, about 36% of the world's total reserves, ranking first in the world. Southern weathering crust rare earth ore is endemic and valuable to China, which widely distributed in Fujian, Jiangxi, Guangdong, Hunan, Guangxi provinces. Its development and utilization in the world has the pivotal influence. About 85% of the rare earth elements are mainly hydrated or hydroxyl hydrated cation, which are adsorbed on the clay minerals. The mineral composition of the weathering crust leaching type rare earth ore is relatively simple. And the clay minerals are about 40% ~ 70% [1]. At present, about 80% of the south ion type rare earth ore is processed by using insitu leaching technology to the actual production. Because the topsoil and ore body do not excavated and the vegetation is not destroyed, it has a great advantage in the protection of the mining area. However, there are many problems in the implementation process, so that the actual results did not reach the original expectation, and even some serious environmental accidents. Moreover, rare earth tailings may lead to waste of resources, and even the residual leaching reagent may cause environmental pollution. Thus it is very necessary to study the residual amount of rare earth and its leaching reagent in the in-situ leaching tailings [1,2]. Ammonium sulfate and deionized water were used as leaching reagent to extract rare earth tailings by in-situ from Ganzhou, Jiangxi Province. The experiment aimed to explore the variation trend of the concentration of rare earth ions, ammonium nitrogen and nitrate with different leaching flowing and different concentration with leaching solution. However, the experiment data is messy, so that it is difficult to directly draw the remnants law of rare earth elements, ammonium nitrogen and nitrate. In this paper, we firstly utilize k-means technology to pre-process the experiment data. We split the data to some classes according to the different concentration of leaching solution. And then, the nitric concentration and residual tailings leaching reagent of rare earth are classed based on k-means technique. Then the center value of every class is achieved. Finally, we can explore the variation trend of the concentration of rare earth ions, ammonium nitrogen and nitrate with different leaching flowing and different concentration with leaching solution according to the processed center data. 2 Related Work The rare earth ions attaching in the weathered crust elution-deposited rare earth ore can react with the more active chemical properties cation ion(for example, Na+, ). The clay minerals of the adsorbed rare earth ions consist of an ion exchange resin with unequal size and complex structure. Generally, clay minerals are stationary phases. When it contains the solution of the leaching reagent, it is mobile phase. The rare earth ions on clay minerals react with the ions with the same number of charge in the solution. This procession is a reversible reaction with adsorption - desorption re- 224 Copyright 2017 SERSC

3 adsorption re-desorption. The experiments use ammonium leaching and its chemical reaction equation can be expressed as [3]: [A1 4(Si 40 10)(OH) 8] m nre 3+ (s) + 3nNH 4 + (aq) [A1 4(Si 40 10)(OH) 8] m (NH 4 + ) 3n(s) + nre 3+ (aq) Where s denotes stationary phase, and aq is aqueous phase. The reaction equation represents the theoretical basis for the leaching process of weathered crust leaching type rare earth ore. Generally, strong acid or strong alkali, weak acid salt, and high salt or low salt or organic acid can effectively leaching weathering crust leaching rare earth ion at moderate concentration of solution. 3 Improved K-means Scheme 3.1 K-means K-means algorithm is the most widely used based on Clustering method to divide, has the advantages of simple and fast. Especially for numerical attribute data, it can better reflect the significance of clustering in geometry and statistics. But the original K- means algorithm has some defects: (1) the algorithm requires the user to give k value in advance. In practice, due to lack of experience, the K value is generally difficult to determine.(2) it is sensitive to initial cluster centers. The different initial center may lead to different clustering results. (3)There are a lot of evaluation functions, for example, separation coefficient, separation and separation efficiency of entropy, compact function etc. But these functions are not very ideal for solving the optimal number of clusters. In order to overcome the shortcomings of the original K-means algorithm, different scholars proposed a series of variable K-means method from different angles. Huang proposed a variable dynamic weighted clustering algorithm based on K- means, which improves the variable selection problems[4]. Dhillon et al re adjusts the class centers in every iterative process to improve the performance [5]. Zhang et al use weights to adjust the soft iterative and optimize process according to the data distribution [6]. For high-dimensional data, Yang [7] et al. proposed a new similarity measure function, which overcomes the disadvantage of traditional distance function. The genetic algorithm was used to the target function, and put forward a new clustering algorithm [8]. In reference [9] a more effective pruning method is utilized to improve the accuracy and efficiency of clustering. Kaufman [10] et al proposed a heuristic method, by estimating the local density of data points in the sample as the initial value. Copyright 2017 SERSC 225

4 3.2 Balanced evaluation function The evaluation functions for clustering design are focused on two aspects. The interior of each cluster should be compact, and the distance between each cluster should be as far as possible. A direct realizing method is to investigate the difference of intra class and that of inter class in C. The difference of intra class can be defined using a variety of distance functions. The simplest method is to compute the sum of the squares of the distances from each point in the class to the center of the class that it belongs to, that is wc () k k 2 1 i i i 1 x c i. i w( c) w( c ) d( x, c ) 1 j i k j i The difference of inter class is defined as the distance between different cluster 2 centers, i.e., b( c) d( c, c ). The difference of intra class shows the compactness of clustering. The difference of inter class denotes the difference between classes. The overall quality of clustering can be defined as a combination of intra class and inter class differences. Based on this idea, we propose a balanced evaluation function. At first, the equilibrium evaluation function is defined as the square root of the square sums of the difference of intra class and the difference of inter class, i.e., J( c, k) w( c) b( c) 2 2 (1) Using the balanced evaluation function as the criterion function can effectively balance the incoordination between the difference of inter class and that of intra class. When the equilibrium evaluation function reaches the minimum, the optimal spatial clustering results are obtained, where k is selected as follows: min{ J( c, k )}, k 1,, K. (2) 3.3 K-means algorithm based on the balanced evaluation function According to experience, the result of k is not more than, and the improved algorithm does not need to be given in advance. Repeated executes k-means algorithm times. Using balance the evaluation function as the criterion function, the minimum value of the evaluation function is searched and the corresponding k value is recorded, which is the number of the optimal clustering results. So the improved algorithm based on the minimum value of the evaluation function automatically generates the number of clusters. K-means algorithm based on the balanced evaluation function Input: Sample data set with n objects and the experience value, coefr Output: Optimal k clusters, and the balance function is minimum. Step 1: for i=1 to n do 1.1 Call the initial center point algorithm based on density to determine i object as the initial center; n n 226 Copyright 2017 SERSC

5 1.2 The average value of the cluster is calculated, and each sample object is assigned to the nearest cluster; 1.3 The average value of the cluster is updated; 1.4 According to the formula (1) to calculate the evaluation function, until its value is convergent, otherwise gotostep1.2. Step 2: According to the formula (2) to search the smallest value of the balance function, and the corresponding k value is the optimal number of cluster. J( c, k) 4 The Occurrence Law of Residual Leaching Reagent and Rare Earth Take 1.0 kg of rare earths in situ leaching tailings and leaching on the inside diameter of 50 mm glass column with 2.0%, 2.5% and 2.0% respectively of leaching ammonium sulfate solution with 2.5 ml/min traffic. Then we utilize the proposed improved k- means to process the experiment data. The following figure is the dynamic curve of nitrate concentration in leachate along with the change of leaching liquid product, when the leaching rate is 2.5 ml/min. concentration of nitric acid root ug/ml % Ammonium sulfate 2.5% Ammonium sulfate 3.0% Ammonium sulfate Leaching solution volume/ml Fig. 1. Nitrate concentration The figure 2 is the curve of rare earth concentration in leachate along with the change of leaching liquid product, when 2% ammonium sulfate leaching and different leaching velocity are adopted. We find that, when the concentration of ammonium sulfate is 2.0%, leaching flowing is 1.0ml/min and leaching solution is 400ml per kilogram of tailings, the leaching rate of the rare earth and the concentration of rare earth in the leaching solution were both higher. And this will have good economic benefits; the free states of rare earth elements and ammonium nitrogen in rare earth mine tailings are very few. Copyright 2017 SERSC 227

6 concentration of rare earth ug/ml Leaching solution volume/ml 1.0ml/min 1.5ml/min 2.0ml/min 2.5ml/min Fig. 2. Rare earth concentration 5 Conclusions In this paper, we firstly propose an improved k-meaning: optimizing the initial center points through computing the density of data objects. A new evaluation function is proposed, namely equalization function, which enable the cluster number to be generated automatically. Then we utilize the proposed k-means technology to pre-process the experiment data. We split the data to some classes according to the different concentration of leaching solution. And then, the nitric concentration and residual tailings leaching reagent of rare earth are classed based on k-means technique. Then the center value of every class is achieved. Finally, we can explore the variation trend of the concentration of rare earth ions, ammonium nitrogen and nitrate with different leaching flowing and different concentration with leaching solution according to the processed center data. Acknowledgments. This work is supported by the NSFC (NO , , ), Jiangsu Province Natural Science Research Program (NO. BK ) and the PAPD fund. Professor Jin Wang is the corresponding author. References 1. Ruan Chi and Jun Tian. The Weathered Crust Elution-deposited Rare Earth Ore [M], New York:Nova Science Publisher, (2008) 228 Copyright 2017 SERSC

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