基于响应曲面法的钛精矿共用煤-铁粉的预还原

曾富洪; 周兰花

doi:10.3969/j.issn.1000-6532.2025.02.024

基于响应曲面法的钛精矿共用煤-铁粉的预还原

曾富洪,
周兰花^,

钒钛资源综合利用四川省重点实验室，四川　攀枝花　617000

基金项目: 钒钛资源综合利用实验室省重点资助项目（035300235）

详细信息

作者简介: 曾富洪（1969-），男，教授，主要从事冶金资源综合利用研究

通讯作者: 周兰花（1969-），女，博士，教授，主要从事冶金资源综合利用研究

中图分类号: TD981

收稿日期: 2022-02-10

刊出日期: 2025-04-25

Pre-reduction of Titanium Concentrate with Pulverized Coal and Iron Power Based on Response Surface Methodology Method

ZENG Fuhong,
ZHOU Lanhua^,

Vanadium and Titanium Resource Comprehensive Utilization Key Laboratory of Sichuan Province, Panzhihua 617000, Sichuan, China

More Information

Corresponding author: ZHOU Lanhua, zhoulanhuazk@163.com

Received Date: 10 February 2022

Publish Date: 25 April 2025

摘要

摘要:
这是一篇冶金工程领域的文章。开展了钛精矿粉共同添加煤粉和铁粉的单因素和响应曲面法加热还原实验，研究了温度、加热时间、铁粉添加量各因素不同水平对钛精矿中铁氧化物还原影响，构建了铁的金属化率3次回归模型，探究了各因素对铁的金属化率影响规律。单因素实验表明：铁粉添加量1.5% ～ 4.0%中，高于2.5%后，铁的金属化率能明显得到提高。响应曲面法实验结果分析发现：在对铁氧化物的还原影响程度中，温度>加热时间>铁粉添加量，温度与时间交互作用>加热时间与铁粉添加量的交互作用；1 450 ℃、33.5 min、4%铁粉添加量优化条件下，铁的金属化率可达86.79%，还原产品中金属铁明显呈现。
- 冶金工程 /
- 钛精矿 /
- 铁氧化物 /
- 还原 /
- 响应曲面法 /
- 铁金属化率
Abstract:
This is an article in the field of metallurgical engineering. The single factor and response surface experiments of heating reduction of titanium concentrate powder adding pulverized coal and iron powder were carried out, respectively so as to study the effects of temperature, heating time and iron powder addition on iron oxide reduction in titanium concentrate. The three regression model of iron metallization rate was constructed, and the influence law of various influencing factors on iron metallization rate was explored. The single factor test shows that the iron metallization rate can be significantly improved above 2.5% in the range of 1.5% ～ 4.0% of the addition amount of iron powder. The response surface method experiment shows that temperature is the most important factor, followed by time and then amount of iron powder, and the interaction between temperature and time is greater than that between heating time and the amount of iron powder added in the degree of influence on the reduction of iron. The iron metallization rate can reach 86.79%, and metallic iron obviously appears in the reduced product at the optimized conditions of 1 450 ℃, 33.5 min and 4% of iron power addition.
- Metallurgical engineering /
- Titanium concentrate /
- Iron oxide /
- reduction /
- Response surface methodology /
- Iron metallization rate

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图 1 铁的金属化率与时间、温度关系

Figure 1.

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图 2 1 400 ℃下铁的金属化率与金属铁粉添加量关系

Figure 2.

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图 3 模型预测的Y与实验值Y之间关系

Figure 3.

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图 4 钛精矿-煤粉体系1 450 ℃下加热33.5 min产物的SEM

Figure 4.

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图 5 图4中点1处扫描能谱分析

Figure 5.

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图 6 图4中点2处扫描能谱分析

Figure 6.

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图 7 图4中点3处扫描能谱分析

Figure 7.

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表 1 钛精矿成分/％

Table 1. Composition of titanium concentrate

TFe	TiO_２	FeO	Fe₂O₃	CaO	MnO	MgO	SiO₂	Al₂O₃	V₂O₅	其他
32.2	46.6	35.5	6.5	1.1	0.8	3.2	3.7	1.0	0.5	1.1

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表 2 煤粉成分/%

Table 2. Coal composition

固定碳	灰分	挥发分
78.26	11.76	9.98

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表 3 单因素实验方案

Table 3. Test program of single factors

温度/℃	时间/min	钛精矿的铁粉添加比例 /%	温度/℃	时间/min	钛精矿的铁粉添加比例 /%	温度/℃	时间/min	钛精矿的铁粉添加比例 /%
1 250	10	0	1 350	20	0	1 450	30	0
1 300	10	0	1 400	20	0	1 250	35	0
1 350	10	0	1 450	20	0	1 300	35	0
1 400	10	0	1 250	25	0	1 350	35	0
1 450	10	0	1 300	25	0	1 400	35	0
1 250	15	0	1 350	25	0	1 450	35	0
1 300	15	0	1 400	25	0	1 400	30	1.5
1 350	15	0	1 450	25	0	1400	30	2
1 400	15	0	1 250	30	0	1 400	30	2.5
1 450	15	0	1 300	30	0	1 400	30	3
1 250	20	0	1 350	30	0	1 400	30	3.5
1 300	20	0	1 400	30	0	1 400	30	4

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表 4 实验因数选取水平与编码值

Table 4. Actual and code value of the variables

水平	温度		时间		钛精矿的铁粉添加比例
	实际/℃	编号	实际/min	编号	实际/%	编号
	T	A	t	B	M	C
1	1 450	1	35	1	4	1
0	1 375	0	22.5	0	2	0
-1	1 300	-1	10	-1	0	-1

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表 5 响应面法实验结果

Table 5. Program and results of RSM test

A	B	C	Y /%	A	B	C	Y /%	A	B	C	Y /%	A	B	C	Y /%	A	B	C	Y /%
1	-1	0	17.1	0	1	-1	53.2	1	0	-1	64.4	-1	0	1	22.4	1	1	0	75.3
0	1	1	64.3	0	0	0	43.3	-1	1	0	42.2	0	0	0	49.3	-	-	-	-
-1	-1	0	8.8	0	0	0	43.2	1	0	1	73.4	0	0	0	42.2	-	-	-	-
-1	0	-1	17.5	0	-1	-1	31.5	0	0	0	45.5	0	-1	1	24.5	-	-	-	-

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表 6 未手动处理的方差分析

Table 6. ANOVA without manual operation

来源	平方和	df	均方	F 值	P 值	显著程度
模型	6 378	12	531.5	65.9	0.000 5	*
A	2 396.1	1	2 396.1	297.1	< 0.000 1	**
B	945.56	1	945.56	117.24	0.000 4	*
C	4.2	1	4.2	0.52	0.510 3
AB	153.76	1	153.76	19.07	0.012	*
AC	4.2	1	4.2	0.52	0.510 3
BC	81.9	1	81.9	10.16	0.033 3	*
A²	64.04	1	64.04	7.94	0.047 9	*
B²	103.17	1	103.17	12.79	0.023 2	*
C²	55.33	1	55.33	6.86	0.058 8
A²B	113.25	1	113.25	14.04	0.02	*
A²C	12.01	1	12.01	1.49	0.289 5
AB²	399.03	1	399.03	49.48	0.002 2	*
Pure Error	32.26	4	8.06
Cor Total	6 410.26	16
注：P≤0.000 1，为非常显著，用*表示；P≤0.05，为显著，用表示；P>0.05，为不显著。

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表 7 手动处理的方差分析

Table 7. ANOVA analysis with manual operation

来源	平方和	df	均方	F 值	P 值	显著程度
模型	6 369.6	10	636.96	93.98	< 0.000 1	**
A	2 396.1	1	2 396.1	353.54	< 0.000 1	**
B	945.56	1	945.56	139.51	< 0.000 1	**
AB	153.76	1	153.76	22.69	0.003 1	*
BC	81.9	1	81.9	12.08	0.013 2	*
A²	64.04	1	64.04	9.45	0.021 8	*
B²	103.17	1	103.17	15.22	0.008	*
C²	55.33	1	55.33	8.16	0.028 9	*
A²B	113.25	1	113.25	16.71	0.006 4	*
A²C	48.3	1	48.3	7.13	0.037	*
AB²	399.03	1	399.03	58.88	0.000 3	*
Residual	40.66	6	6.78
Lack of Fit	8.41	2	4.2	0.52	0.629 3	not significant
Pure Error	32.26	4	8.06
Cor Total	6 410.26	16
注：P≤0.000 1，为非常显著，用*表示；P≤0.05，为显著，用表示；P>0.05，为不显著。

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