Optimal method of selecting silicon content data in blast furnace hot metal based on k-means++

doi:10.11949/0438-1157.20191115

Abstract

Abstract:

High-quality data sets are the basis for accurate prediction of silicon content in blast furnace hot metal. There are some difficulties in processing the silicon content in hot metal. One challenge is the uneven recording, especially multiple silicon contents have a large fluctuation in some sample periods. Another is that silicon content is difficult to correlate with input variables. Aiming at these problems, we proposed an optimal method of selecting silicon content data in hot metal based on k-means++ clustering algorithm. Firstly, the fast clustering ability of k-means++ is utilized to divide samples to represent different furnace conditions. Secondly, the frequency histogram of the silicon content of each cluster is counted to determine the high frequency interval. Finally, using the high frequency range as the criterion, we select the best silicon content value associated with the sample. Taking a 2650 m³ blast furnace in a certain steel works as an example, established the deep learning models respectively based on multi-layer perceptron and LSTM for prediction. The results indicated that compared with the traditional averaging method, the mean square error (MSE) can be reduced by 0.003 and the hit rate is increased by more than 10%. Thus, this method has a good guiding significance for preprocessing the silicon content data in hot metal.

Key words: prediction, dynamic modeling, neural networks, blast furnace, silicon content in hot metal, optimal selecting data, k-means++, deep learning

摘要：

优质数据集是实现高炉铁水硅含量准确预报的基础。针对铁水硅含量数据记录不均衡，特别是部分样本周期内存在多个硅含量值且波动较大，难以与输入变量进行合理关联等难题，提出了一种基于k-means++聚类算法的铁水硅含量数据优选方法。该方法首先利用k-means++的快速聚类能力，将样本分簇，用以表征不同的炉况；其次统计各簇硅含量频数直方图，由此确定高频区间；最后以高频区间为标准，遴选与样本关联的最佳硅含量值。以某钢铁厂2650 m³的高炉为例，分别建立基于多层感知器和LSTM的深度学习模型进行预测，结果表明，该优选方法处理的数据与传统均值法相比，均方误差可减少0.003，命中率提高10%以上，对铁水硅含量数据的预处理具有较好的指导意义。

关键词: 预测, 动态建模, 神经网络, 高炉炼铁, 铁水硅含量, 数据优选, k-means++, 深度学习

CLC Number:

TP 393.4

Linzi YIN, Yuyin GUAN, Zhaohui JIANG, Xuemei XU. Optimal method of selecting silicon content data in blast furnace hot metal based on k-means++[J]. CIESC Journal, 2020, 71(8): 3661-3670.

尹林子, 关羽吟, 蒋朝辉, 许雪梅. 基于k-means++的高炉铁水硅含量数据优选方法[J]. 化工学报, 2020, 71(8): 3661-3670.

Figures/Tables 15

Table 1 The proportion of different silicon contents during the sample period

样本周期内的硅含量检测数目	样本数	占比/%
0	79	10.63
1	184	24.76
2	243	32.7
3	166	22.34
4	54	7.27
5	15	2.02
7	1	0.13
9	1	0.13

Fig.1 MSE of silicon content for each sample period

Fig.2 Flow chart of “k-means++ optimal selecting method”

Fig.3 Clusters of input variables by k-means++ for the first time

Fig.4 k-means ++ results after removing abnormal clusters

Table 2 Statistics of each cluster

簇	α	$\sum \|T_{i}\|$	$\|T\|$	ρ
Cluster A	5	366	393	0.93
Cluster B	5	109	129	0.84
Cluster C	5	17	26	0.65
Cluster D	5	103	116	0.89
Cluster E	3	45	71	0.63

Table 2 Statistics of each cluster

簇	α	$\sum \|T_{i}\|$	$\|T\|$	ρ
Cluster A	5	366	393	0.93
Cluster B	5	109	129	0.84
Cluster C	5	17	26	0.65
Cluster D	5	103	116	0.89
Cluster E	3	45	71	0.63

Fig.5 Frequency histogram of each cluster

Table 3 Proportion of silicon content of each cluster in different sample periods

样本周期内硅含量检测数目	Cluster A		Cluster B		Cluster C		Cluster D		Cluster E
样本周期内硅含量检测数目	样本数	占比	样本数	占比	样本数	占比	样本数	占比	样本数	占比
0	36	9.84%	11	10.10%	2	11.80%	11	10.70%	5	11.10%
1	96	26.20%	25	22.90%	6	35.30%	19	18.50%	10	22.20%
2	113	30.90%	38	34.90%	3	17.70%	40	38.80%	16	35.60%
3	83	22.70%	26	23.90%	4	23.50%	23	22.30%	10	22.20%
4	27	7.38%	8	7.34%	2	11.80%	7	6.80%	3	6.67%
5	10	2.73%	1	0.92%	0	0	2	1.94%	1	2.22%
7	0	0	0	0	0	0	1	0.97%	0	0
9	1	0.27%	0	0	0	0	0	0	0	0

Table 4 Statistics of samples which can be matched by the silicon content in the high-frequency interval

簇	$\|T\|$	可匹配样本数	占比/%
Cluster A	116	98	84.48
Cluster B	129	101	78.29
Cluster C	393	457	124.86
Cluster D	71	59	83.1
Cluster E	26	16	61.54

Table 4 Statistics of samples which can be matched by the silicon content in the high-frequency interval

簇	$\|T\|$	可匹配样本数	占比/%
Cluster A	116	98	84.48
Cluster B	129	101	78.29
Cluster C	393	457	124.86
Cluster D	71	59	83.1
Cluster E	26	16	61.54

Fig.6 Multi-layer perceptron network structure

Fig.7 Comparison between the prediction and the actual value based on the multi-layer perceptron model

Table 5 Comparison between the data sets of “k-means++ optimal selection method” and “averaging method” based on the multi-layer perceptron model

方法	MSE	HR(0.05%)	HR(0.1%)	TAR
均值法	0.0070	46.67%	81.06%	49.24%
k-means++优选法	0.0036	61.50%	90.61%	51.02%

Fig.8 LSTM network structure

Fig.9 Comparison between the prediction and the actual value based on the LSTM network structure

Table 6 Comparison between the data sets of “k-means++ optimal selection method” and “averaging method” based on the LSTM network structure

方法	MSE	HR(0.05%)	HR(0.1%)	TAR
均值法	0.0066	48.94%	81.52%	55.30%
k-means++优选法	0.0027	67.02%	94.15%	57.82%

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