Multi-objective optimization of pressure oxidative selective leaching of copper smelting slag by response surface methodology

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

Abstract

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

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

CLC Number:

TQ02

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.

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.

Figures/Tables 15

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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