响应曲面法多目标优化铜冶炼渣氧压选择性浸出工艺

doi:10.16085/j.issn.1000-6613.2019-1595

摘要/Abstract

摘要：

采用响应曲面法的中心组合设计原理，建立浸出温度、硫酸浓度及液固比及三者之间交互作用对选择性浸出率与矿浆过滤速率的多元二次回归方程，并使用自适应权重粒子群算法对铜冶炼渣氧压硫酸选择性浸出工艺进行多目标优化。结果表明：浸出温度、硫酸浓度和液固比均是影响浸出率和过滤速率的主要因素，各响应因素间存在交互效应，且选择性浸出率与矿浆过滤速率在最佳条件上存在差异。优化后的选择性浸出率和矿浆过滤速率最佳的工艺条件为：温度为204.1℃、硫酸浓度为0.46mol/L、液固比为6.9mL/g，此条件下选择性浸出率为96.95%，过滤速率为399.42L/(m²?h)，与验证实验中平均选择性浸出率、平均过滤速率分别为96.57%，398L/(m²?h)相比，偏差较小，预测值与验证实际值吻合好，表明模型选择准确，优化方案可信。

关键词: 铜冶炼渣, 选择性浸出, 矿浆过滤性能, 响应曲面法, 自适应权重粒子群算法, 多目标优化

Abstract:

Based on the central combination design principle of response surface methodology, the multiple quadratic regression equation relating leaching temperature, sulfuric acid concentration, liquid-solid ratio and interaction between them on selective leaching rate and slurry filtration rate was established, and the multi-objective optimization of the oxygen pressure sulfuric acid selective leaching process for copper smelting slag was performed using the adaptive weighted particle swarm method. The results show that the leaching temperature and sulfuric acid concentration and liquid-solid ratio are the main factors affecting the leaching rate and filtration rate. There are interaction effects among the response factors affecting the leaching rate and filtration rate, and each of the optimal conditions for the selective leaching rate and the pulp filtration rate are different. The optimum conditions for the simultaneous optimization of selective leaching rate and slurry filtration rate are as follows: temperature of 204.1°C, sulfuric acid concentration of 0.46mol/L, and liquid-solid ratio of 6.9mL/g. Under the optimum conditions, the selective leaching rate is 96.95%, the filtration rate is 399.42L/(m²?h). Compared the average selective leaching rate and average filtration rate with that of 96.57% and 398L/(m²?h) respectively in verification experiment, the deviation between them is small. The value predicted by the equation is in good agreement with the verified actual value, indicating that the model obtained is accurate and the optimization scheme is credible.

Key words: copper metallurgical slag, selective leaching, slurry filtration property, response surface methodology, adaptive weight particle swarm optimization, multi-objective optimization

中图分类号:

TQ02

史公初, 廖亚龙, 苏博文, 张宇, 郗家俊. 响应曲面法多目标优化铜冶炼渣氧压选择性浸出工艺[J]. 化工进展, 2020, 39(S1): 270-280.

Gongchu SHI, Yalong LIAO, Bowen SU, Yu ZHANG, Jiajun XI. Multi-objective optimization of pressure oxidative selective leaching of copper smelting slag by response surface methodology[J]. Chemical Industry and Engineering Progress, 2020, 39(S1): 270-280.

图/表 15

参考文献 37

1	朱祖泽. 现代铜冶金学[M]. 北京: 科学出版社, 2003: 2-8.
	ZHU Zuze. Modern copper metallurgy[M]. Beijing: Science Press, 2003: 2-8.
2	赵凯, 宫晓然, 李杰, 等. 急冷铜渣矿物学及其综合利用[J]. 中国矿业, 2015(9): 102-106.
	ZHAO Kai, GONG Xiaoran, LI Jie, et al. Mineralogical characteristics and comprehensive utilization of rapid cooling copper slag[J]. China Mining Magazine, 2015(9): 102-106.
3	ALTER H. The composition and environmental hazard of copper slags in the context of the Basel convention[J]. Resources Conservation and Recycling, 2005, 43(4): 353-360.
4	廖亚龙, 叶朝, 王祎洋, 等. 铜冶炼渣资源化利用研究进展[J]. 化工进展, 2017, 36(8): 3066-3073.
	LIAO Yalong, YE Chao, WANG Yiyang, et al. Resource utilization of copper smelter slag—A-state-of-the-arts review[J]. Chemical Industry and Engineering Progress, 2017, 36(8): 3066-3073.
5	王国红. 铜冶炼炉渣缓冷技术研究与生产实践[J]. 铜业工程, 2014(4): 27-30.
	WANG Guohong. Copper smelting slag slow cooling technology research and practice[J]. Copper Engineering, 2014(4): 27-30.
6	张海鑫. 浅谈铜冶炼渣缓冷工艺[J]. 中国有色冶金, 2013, 42(3): 32-33.
	ZHANG Haixin. Discussion on slow cooling process of copper smelting slag[J]. China Nonferrous Metallurgy, 2013, 42(3): 32-33.
7	LI Y, PEREDERIY I, PAPANGELAKIS V G. Cleaning of waste smelter slags and recovery of valuable metals by pressure oxidative leaching[J]. Journal of Hazardous Materials, 2008, 152(2): 607-615.
8	孙建军, 黄自力, 杨蘖, 等. 铜冶炼渣硫酸浸出回收铜试验研究[J]. 矿产综合利用, 2017(6): 102-107.
	SUN Jianjun, HUANG Zili, YANG Nie, et al. Study on recovery of copper from copper smelting slag by sulfuric acid leaching[J]. Multipurpose Utilization of Mineral Resources, 2017(6): 102-107.
9	黄斐荣, 廖亚龙, 王祎洋, 等. 响应曲面法优化镍转炉渣氧压选择性浸出工艺[J]. 环境科学研究, 2016, 29(6): 894-899.
	HUANG Feirong, LIAO Yalong, WANG Yiyang, et al. Optimization of pressure oxidative acid leaching of nickel converter slag by response surface methodology[J]. Research of Environmental Sciences, 2016, 29(6): 894-899.
10	廖亚龙, 黄斐荣, 周娟, 等. 低冰镍转炉渣中钴的氧压酸浸行为及其动力学[J]. 化工学报, 2015, 66(10): 3971-3978.
	LIAO Yalong, HUANG Feirong, ZHOU Juan, et al. Kinetics and behavior of cobalt extraction from low nickel matte converter slag by pressure oxidative leaching with sulfuric acid[J]. CIESC Journal, 2015, 66(10): 3971-3978.
11	MONTGOMERY D C. Design and analysis of experiments, 8th ed.[J]. Environmental Progress & Sustainable Energy, 2013, 32(1): 8-10.
12	王安琪, 王永良, 叶礼平, 等. 响应曲面法优化氧化铜渣浮选提铜工艺[J]. 化工学报, 2018, 69(7): 3018-3028.
	WANG Anqi, WANG Yongliang, YE Liping, et al. Optimization of recycling copper from copper oxide smelting slag by response surface methodology[J]. CIESC Journal, 2018, 69(7): 3018-3028.
13	曹磊, 廖亚龙, 史公初, 等. 锌置换渣加压酸性浸出过程铁的物相转化及其影响[J]. 中国有色金属学报, 2019, 29(2): 404-414.
	CAO Lei, LIAO Yalong, SHI Gongchu, et al. Phase transformation of iron and its effect in pressure acid leaching process for zinc replacement residue[J]. The Chinese Journal of Nonferrous Metals, 2019, 29(2): 404-414.
14	赵艺文. 结合型罗布麻茶化学成分及其抗氧化活性研究[D]. 南京: 南京农业大学, 2017.
	ZHAO Yiwen. Studies on bound chemical components and their antioxidant activity of Apocynum venetum tea [D]. Nanjing: Nanjing Agricultural University, 2017.
15	VITANOV V I, JAVAID N, STEPHENSON D J. Application of response surface methodology for the optimization of micro friction surfacing process[J]. Surface & Coatings Technology, 2010, 204(21): 3501-3508.
16	DAS P K, ZHENG Y. Cumulative formation of response surface and its use in reliability analysis[J]. Probabilistic Engineering Mechanics, 2000, 15(4): 309-315.
17	黄斐荣. 镍转炉渣氧压硫酸浸出提取钴、镍、铜的研究[D]. 昆明: 昆明理工大学, 2016.
	HUANG Feirong. Extraction of cobalt, nickel and copper from nickel converter slag by oxygen pressure sulfuric acid leaching [D]. Kunming: Kunming University of Science and Technology, 2016.
18	KEHOE S, ARDHAOUI M, STOKES J. Design of experiments study of hydroxyapatite synthesis for orthopedic application using fractional factorial design[J]. Journal of Material Engineering and Performance, 2011, 20(8): 1423-1437.
19	BARI M N, ALAM M Z, MUYIBI S A. Improvement of production of citric acid from oil palm empty fruit bunches: optimization of media by statistical experimental designs[J]. Bioresoure Technology, 2009, 100(12): 3115-3120.
20	ARBIU I, PEREZ C J L. Surface roughness prediction by factorial design of experiments in turning processes[J]. Journal of Materials Processing Technology, 2003, 143/144(1): 390-396.
21	SINGH A, BISHONI N R. Optimization of ethanol production from microwave alkali pretreated rice straw using statistical experimental designs by Saccharomyces cerevisiae[J]. Industrial Crops and Products, 2012, 37(1): 334-341.
22	葛宜元. 试验设计方法与Design-Expert软件应用[M]. 哈尔滨: 哈尔滨工业大学出版社, 2015: 14-18.
	GE Yiyuan. Test design method and design-expert software application[M]. Harbin: Harbin Institute of Technology Press, 2015: 14-18.
23	GONZALEZ G A. Two level factorial experimental designs based on multiple linear regression models: a tutorial digest illustrated by case studies[J]. Analytica Chimica Acta, 1998, 360(1/2/3): 227-241.
24	张烘州, 明伟伟, 安庆龙, 等. 响应曲面法在表面粗糙度预测模型及参数优化中的应用[J]. 上海交通大学学报, 2010, 44(4): 447-451.
	ZHANG Hongzhou, MING Weiwei, AN Qinglong, et al. Application of response surface methodology in surface roughness prediction model and parameter optimization[J]. Journal of Shanghai Jiaotong University, 2010, 44(4): 447-451.
25	HE S M, WANG J K, YAN J F. Pressure leaching of synthetic zinc silicate in sulfuric acid medium[J]. Hydrometallurgy, 2011, 108(3/4): 171-176.
26	HUANG Feirong, LIAO Yalong, ZHOU Juan, et al. Selective recovery of valuable metals from nickel converter slag at elevated temperature with sulfuric acid solution[J]. Separation and Purification Technology, 2015, 156: 572-581.
27	DUFRESNE R E. Quick leach of siliceous zinc ore[J]. Journal of Metals, 1976, 28(2): 8-12.
28	XU H S, WEI C, LI C X, et al. Selective recovery of valuable metals from partial silicated sphalerite at elevated temperature with sulfuric acid solution[J]. Journal of Industrial and Engineering Chemistry, 2014, 20(4): 1373-1381.
29	HE Shanming, WANG Jikun, YAN Jiangfeng. Pressure leaching of high silica Pb-Zn oxide ore in sulfuric acid medium[J]. Hydrometallurgy, 2010, 104(2): 235-240.
30	ZHANG L, TANG Y, HUA C, et al. A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques[J]. Applied Soft Computing, 2015, 28: 138-149.
31	EBERHART, SHI Y. Particle swarm optimization: developments, applications and resources[C]//MINJEA T, CORNE D. Proceeding of the 2001 Congress on Evolutionary Computation. Seoul: IEEE, 2001: 81-86.
32	石松宁, 王大志, 时统宇. 基于RSM的永磁驱动器偏心磁极的多目标优化[J]. 仪器仪表学报, 2014, 35(9): 1963-1971.
	SHI Songning, WANG Dazhi, SHI Tongyu. Multi-objective optimization of eccentric magnet pole for permanent magnet drive based on response surface methodology[J]. Chinese Journal of Scientific Instrument, 2014, 35(9): 1963-1971.
33	吴加同. 复合材料舵固有频率与重量的优化设计[D]. 武汉: 华中科技大学, 2018.
	WU Jiatong. Optimization for natural frequency and weight of laminated composite rudder[D]. Wuhan: Huazhong University of Science and Technology, 2018.
34	BRATTON D, KENNEDY J. Defining a standard for particle swarm optimization[C]//SHI Y, MARCO D. 2007 IEEE Swarm Intelligence Symposium. Honolulu: IEEE, 2007, 120-127.
35	朱爱军, 李智, 许川佩. 三维IP核测试封装扫描链多目标优化设计[J]. 电子测量与仪器学报, 2014, 28(4): 373-380.
	ZHU Aijun, LI Zhi, XU Chuanpei. 3D IP core test wrapper scanning chain design based on multi-objective algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2014, 28(4): 373-380.
36	高飞. MATLAB智能算法超级学习手册[M]. 北京: 人民邮电出版社, 2014: 224-248.
	GAO Fei. MATLAB & simulink books[M]. Beijing: Posts and Telecommunications Press, 2014: 224-248.
37	ARSLAN C, ARSLAN F. Recovery of copper, cobalt, and zinc from copper smelter and converter slags[J]. Hydrometallurgy, 2002, 67(1/2/3): 1-7.

因素	编码	单位			水平			梯步值
因素	编码	单位	-1.682(-α)	-1	0	+1	+1.682(+α)	梯步值
温度	x₁	°C	183	190	200	210	217	15
硫酸浓度	x₂	mol·L^-1	0.23	0.3	0.4	0.5	0.57	0.1
液固比	x₃	mL·g^-1	4.3	5	6	7	7.7	1

因素	编码	单位			水平			梯步值
因素	编码	单位	-1.682(-α)	-1	0	+1	+1.682(+α)	梯步值
温度	x₁	°C	183	190	200	210	217	15
硫酸浓度	x₂	mol·L^-1	0.23	0.3	0.4	0.5	0.57	0.1
液固比	x₃	mL·g^-1	4.3	5	6	7	7.7	1

编号	编码			浸出率/%			选择性浸出率Y₁/%	过滤速率Y₂/L·m^-2·h^-1
编号	x₁	x₂	x₃	Cu	Fe	Si	选择性浸出率Y₁/%	过滤速率Y₂/L·m^-2·h^-1
1	0	α	0	97.49	1.97	1.41	94.11	374.64
2	-1	1	1	96.86	1.59	1.05	94.22	356.59
3	0	0	0	97.39	1.23	1.32	94.84	391.20
4	0	0	0	97.63	1.07	1.72	94.84	388.85
5	1	1	1	98.32	1.68	0.81	95.83	403.25
6	0	0	0	97.63	1.03	1.80	94.80	395.14
7	0	0	0	97.63	1.13	1.73	94.77	392.11
8	-α	0	0	95.09	0.87	1.23	92.99	316.27
9	α	0	0	98.12	0.92	1.63	95.57	370.03
10	1	-1	1	95.81	0.53	1.52	93.76	349.44
11	0	0	-α	93.28	1.09	2.46	89.73	268.61
12	-1	1	-1	96.79	2.31	2.45	92.03	296.88
13	0	0	0	96.92	1.03	1.02	94.87	394.61
14	-1	-1	1	94.15	1.28	2.87	90.00	316.13
15	1	-1	-1	90.80	2.71	1.13	86.96	302.45
16	-1	-1	-1	88.17	1.19	1.76	85.22	297.84
17	0	0	0	98.08	1.13	2.12	94.83	393.65
18	1	1	-1	94.94	1.22	1.51	92.21	320.40
19	0	0	α	97.54	1.26	1.33	94.95	379.97
20	0	-α	0	86.99	0.42	2.04	84.53	335.28

编号	编码			浸出率/%			选择性浸出率Y₁/%	过滤速率Y₂/L·m^-2·h^-1
编号	x₁	x₂	x₃	Cu	Fe	Si	选择性浸出率Y₁/%	过滤速率Y₂/L·m^-2·h^-1
1	0	α	0	97.49	1.97	1.41	94.11	374.64
2	-1	1	1	96.86	1.59	1.05	94.22	356.59
3	0	0	0	97.39	1.23	1.32	94.84	391.20
4	0	0	0	97.63	1.07	1.72	94.84	388.85
5	1	1	1	98.32	1.68	0.81	95.83	403.25
6	0	0	0	97.63	1.03	1.80	94.80	395.14
7	0	0	0	97.63	1.13	1.73	94.77	392.11
8	-α	0	0	95.09	0.87	1.23	92.99	316.27
9	α	0	0	98.12	0.92	1.63	95.57	370.03
10	1	-1	1	95.81	0.53	1.52	93.76	349.44
11	0	0	-α	93.28	1.09	2.46	89.73	268.61
12	-1	1	-1	96.79	2.31	2.45	92.03	296.88
13	0	0	0	96.92	1.03	1.02	94.87	394.61
14	-1	-1	1	94.15	1.28	2.87	90.00	316.13
15	1	-1	-1	90.80	2.71	1.13	86.96	302.45
16	-1	-1	-1	88.17	1.19	1.76	85.22	297.84
17	0	0	0	98.08	1.13	2.12	94.83	393.65
18	1	1	-1	94.94	1.22	1.51	92.21	320.40
19	0	0	α	97.54	1.26	1.33	94.95	379.97
20	0	-α	0	86.99	0.42	2.04	84.53	335.28

方差来源	平方和	自由度	均方	F值	p值	显著水平
模型	224.22	9	24.91	75.26	< 0.0001	显著
x₁	9.90	1	9.90	29.91	0.0003
x₂	86.96	1	86.96	262.71	< 0.0001
x₃	50.14	1	50.14	151.49	< 0.0001
x₁x₂	1.72	1	1.72	5.20	0.0458
x₁x₃	1.49	1	1.49	4.49	0.0600
x₂x₃	4.16	1	4.16	12.57	0.0053
x₁²	1.31	1	1.31	3.97	0.0743
x₂²	60.89	1	60.89	183.96	< 0.0001
x₃²	14.06	1	14.06	42.49	< 0.0001
残差	3.31	10	0.33
失拟项	3.31	5	0.66
纯误差	0.72	5	0.14
总合	227.53	19
R²	0.9855
Adj R²	0.9724