机理与数据知识驱动的湿法冶锌中性浸出过程监测方法

doi:10.11959/j.issn.2096-6652.202236

智能科学与技术学报 ›› 2022, Vol. 4 ›› Issue (4): 477-490.doi: 10.11959/j.issn.2096-6652.202236

机理与数据知识驱动的湿法冶锌中性浸出过程监测方法

任浩¹, 孙备¹^,², 梁骁俊¹, 阳春华¹^,²

¹ 鹏城实验室数学与理论部工业智能基础研究室，广东深圳 518055
² 中南大学自动化学院，湖南长沙 410083

修回日期:2022-07-20 出版日期:2022-12-15 发布日期:2022-12-01
作者简介:任浩（1990− ），男，博士，鹏城实验室数学与理论部工业智能基础研究室助理研究员，主要研究方向为大数据分析、工业知识自动化和过程监测等
孙备（1988− ），男，博士，中南大学自动化学院副教授，主要研究方向为复杂工业过程建模、流程工业智能优化制造、工业大数据分析、模式识别与机器学习等
梁骁俊（1990− ），男，博士，鹏城实验室数学与理论部工业智能基础研究室助理研究员，主要研究方向为力学建模与分析、工业智能系统、机理与数据融合的冶金工业控制优化技术等
阳春华（1965− ），女，博士，中南大学自动化学院院长、教授、博士生导师，主要研究方向为复杂工程过程建模与优化控制、智能感知与自动化装置、工业大数据分析与深度学习、流程工业智能优化制造等
基金资助:
国家自然科学基金资助项目(62103207);鹏城实验室重大攻关项目(PCL2021A09)

Mechanism and data knowledge-driven process monitoring method for neutral leaching in zinc hydro-metallurgical

Hao REN¹, Bei SUN¹^,², Xiaojun LIANG¹, Chunhua YANG¹^,²

¹ Industrial Intelligence Basic Studio in Department of Mathematics and Theories, Peng Cheng Laboratory, Shenzhen 518055, China
² School of Automation, Central South University, Changsha 410083, China

Revised:2022-07-20 Online:2022-12-15 Published:2022-12-01
Supported by:
The National Natural Science Foundation of China(62103207);The Major key Project of Peng Cheng Laboratory(PCL2021A09)

摘要/Abstract

摘要：

中性浸出是湿法冶锌中溶解锌焙砂得到锌电解液的关键工序，不同外界环境和扰动等影响了中性浸出生产过程的运行状态，为此，提出了一种机理与数据知识驱动的湿法冶锌中性浸出过程监测方法。该方法首先从浸出的物理化学反应机理和工艺机理知识出发，挖掘机理参数与监测变量之间的关联关系，实现知识驱动的关键监测变量选择；其次，结合一阶、二阶关键变量的趋势变化特征，实现数据驱动的浸出过程监测；最后，将所提方法应用于某实际中性浸出过程监测。结果表明，所提方法能够有效地实现湿法冶锌浸出过程监测，提高中性浸出过程的生产稳定性。

关键词: 湿法冶锌, 中性浸出过程, 过程监测, 数据与知识融合

Abstract:

Neutral leaching can be regarded as the key process of dissolving zinc-calcine in zinc hydro-metallurgy to obtain the zinc-electrolyte, and the variation of external environments and disturbances affect the operation states of the neutral leaching process.To this end, a mechanism and data knowledge-driven process monitoring method for neutral leaching in zinc hydro-metallurgical was proposed.This method firstly started from the physical-chemical reaction mechanism and process mechanism of the neutral leaching process, which can be used to excavate the correlation between the mechanical parameters and the monitoring variables to realize the knowledge-driven selection of key monitoring variables.Secondly, the trend change characteristics of the first-order and second-order key variables were combined to realize the data-driven process monitoring.Finally, this proposed method was applied to the monitoring of the practical neutral leaching process.The results show that this method can effectively realize the monitoring of the zinc hydro-metallurgy neutral leaching process, which can be used to improve the process stability of the neutral leaching process.

Key words: zinc hydro-metallurgy, neutral leaching process, process monitoring, data and knowledge fusion

中图分类号:

TP277

任浩, 孙备, 梁骁俊, 等. 机理与数据知识驱动的湿法冶锌中性浸出过程监测方法[J]. 智能科学与技术学报, 2022, 4(4): 477-490.

Hao REN, Bei SUN, Xiaojun LIANG, et al. Mechanism and data knowledge-driven process monitoring method for neutral leaching in zinc hydro-metallurgical[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 477-490.

图/表 11

图1

表1

图2

图3

图4

表2

图5

图6

图7

图8

表3

某锌冶炼厂14组状态变化时间感知检测率（FR，DR）"

组号	测试样本（平稳:波动）	PCA方法		本文方法
组号	测试样本（平稳:波动）	T²	Q	本文方法
1	68:237	0.0%，78.9%	5.9%，81.0%	$1 . 5 %, 100 . 0 %$
2	74:196	0.0%，43.9%	0.0%，64.8%	$1 . 4 %, 100 . 0 %$
3	195:272	26.2%，84.2%	7.2%，76.8%	$0 . 5 %, 96 . 69 %$
4	75:290	100.0%，100.0%	12.0%，59.6%	$2 . 7 %, 86 . 05 %$
5	138:361	100.0%，88.4%	2.2%，6.4%	$4 . 3 %, 80 . 89 %$
6	236:273	2.8%，57.1%	0.8%，41.4%	$14 . 4 %, 78 . 39 %$
7	254:99	0.0%，53.5%	6.3%，88.9%	$0 . 8 %, 96 . 97 %$
8	97:376	100.0%，99.2%	2.1%,16.8%	$3 . 1 %, 89 . 10 %$
9	206:324	100.0%，100.0%	41.7%，4.0%	$1 . 5 %, 66 . 67 %$
10	193:277	100.0%，100.0%	75.7%，28.9%	$8 . 3 %, 83 . 71 %$
11	81:324	100.0%，23.1%	1.2%，0.0%	$6 . 1 %, 100 . 0 %$
12	71:321	100.0%，100.0%	36.6%，100.0%	$5 . 7 %, 76 . 53 %$
13	242:264	100.0%，100.0%	0.0%，39.0%	$2 . 5 %, 83 . 64 %$
14	264:213	100.0%，100.0%	22.0%，92.0%	$1 . 4 %, 84 . 11 %$

表3

参考文献 15

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变量	物理含义
v₁	向1#浸出槽加入第一个亚热酸的流量
v₂	向1#浸出槽加入第二个亚热酸的流量
v₃	向1#浸出槽加入混合液的流量
v₄	向1#浸出槽加入锰矿浆的流量
v₅	向1#浸出槽加入废酸的流量
v₆	向1#浸出槽加入锌焙砂的投料量
v₇	向2#浸出槽加入废酸的流量
v₈	向2#浸出槽加入锌焙砂的投料量
v₉	向3#浸出槽加入锌焙砂的投料量
v₁₀	向4#浸出槽加入锌焙砂的投料量
v₁₁	向5#浸出槽加入氧气的流量
v₁₂	向5#浸出槽加入废酸的流量
v₁₃	5#浸出槽锌液的温度
v₁₄	向5#浸出槽加入锌焙砂的投料量
v₁₅	向6#浸出槽加入氧气的流量
v₁₆	向6#浸出槽加入锌焙砂的投料量

变量	均值	标准差	正四分位	正众数	负众数	负四分位
v₁	112.01	3.12	0.73	0.43	-0.46	-0.74
v₂	156.30	4.32	0.54	0.31	-0.22	-0.41
v₃	135.25	4.14	0.61	0.38	-0.53	-0.81
v₄	41.23	1.04	1.56	0.88	-1.00	-1.61
v₅	109.73	2.07	0.98	0.60	-0.41	-0.72
v₆	5.87	2.24	0.85	0.53	-0.48	-0.83
v₇	36.72	1.00	1.76	0.99	-0.99	-1.59
v₈	10.01	1.02	1.66	1.01	-1.00	-1.54
v₉	9.93	1.04	1.57	0.87	-1.09	-1.67
v₁₀	-0.02	0.93	1.68	0.97	-1.00	-1.75
v₁₁	2.30	1.05	1.82	0.93	-1.05	-1.82
v₁₂	0.01	0.92	1.60	0.88	-0.85	-1.56
v₁₃	80.81	0.97	1.58	0.92	-0.98	-1.60
v₁₄	1.89	0.99	1.55	0.93	-1.08	-1.62
v₁₅	2.73	1.04	1.56	0.97	-1.02	-1.76
v₁₆	-0.09	0.99	1.47	0.94	-0.86	-1.55

机理与数据知识驱动的湿法冶锌中性浸出过程监测方法

Mechanism and data knowledge-driven process monitoring method for neutral leaching in zinc hydro-metallurgical

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