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Mechanism and data knowledge-driven process monitoring method for neutral leaching in zinc hydro-metallurgical
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 4, Pages: 477-490(2022)
- 作者机构：
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
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202236
CLC： TP277-
Published Online：2022-12，
Published：15 December 2022
- 稿件说明：
移动端阅览
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.
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. DOI： 10.11959/j.issn.2096-6652.202236.
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。
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.
关键词
Keywords
references
LONG S , LI W J , YANG W , et al . pH prediction of a neutral leaching process using adaptive-network-based fuzzy inference system and reaction kinetics [J ] . IFAC-PapersOnLine , 2020 , 53 ( 2 ): 11901 - 11906 .
熊富强 , 桂卫华 , 阳春华 . 针铁矿法沉铁过程铁离子浓度集成预测模型 [J ] . 控制与决策 , 2012 , 27 ( 3 ): 329 - 334 , 342 .
XIONG F Q , GUI W H , YANG C H . Integrated prediction model of iron concentration in goethite method to remove iron process [J ] . Control and Decision , 2012 , 27 ( 3 ): 329 - 334 , 342 .
刘学斌 . 基于规则提取与预测控制的锌浸出过程分段控制方法 [D ] . 长沙:中南大学 , 2021 .
LIU X B . Segmented control method of zinc leaching process based on rule extraction and predictive control [D ] . Changsha:Central South University , 2021 .
李维剑 . 基于机理特征的锌冶炼浸出过程pH值在线估计 [D ] . 长沙:中南大学 , 2021 .
LI W J . Online estimation of pH value in leaching process of zinc hydrometallurgy based on mechanism features [D ] . Changsha:Central South University , 2021 .
FIGUEROA-ESTRADA J C , NERIA-GONZÁLEZ M I , VÁZQUEZ R R , et al . Controlling a continuous stirred tank reactor for zinc leaching [J ] . Minerals Engineering , 2020 , 157 : 106549 .
COELHO F E B , BALARINI J C , ARAÚJO E M R , , et al . Roasted zinc concentrate leaching:population balance modeling and validation [J ] . Hydrometallurgy , 2018 , 175 : 208 - 217 .
CRUNDWELL F K , PREEZ N D , LLOYD J M . Dynamics of particle-size distributions in continuous leaching reactors and autoclaves [J ] . Hydrometallurgy , 2013 , 133 : 44 - 50 .
ZHANG B , YANG C H , ZHU H Q , et al . Kinetic modeling and parameter estimation for competing reactions in copper removal process from zinc sulfate solution [J ] . Industrial ＆ Engineering Chemistry Research , 2013 , 52 ( 48 ): 17074 - 17086 .
YUAN X F , LI L , WANG Y L . Nonlinear dynamic soft sensor modeling with supervised long short-term memory network [J ] . IEEE Transactions on Industrial Informatics , 2020 , 16 ( 5 ): 3168 - 3176 .
HURTADO F J , KAISER A S , ZAMORA B . Fluid dynamic analysis of a continuous stirred tank reactor for technical optimization of wastewater digestion [J ] . Water Research , 2015 , 71 : 282 - 293 .
谢世文 , 谢永芳 , 阳春华 , 等 . 针铁矿法沉铁过程亚铁离子浓度预测 [J ] . 自动化学报 , 2014 , 40 ( 5 ): 830 - 837 .
XIE S W , XIE Y F , YANG C H , et al . A ferrous iron concentration prediction model for the process of iron precipitation by goethite [J ] . Acta Automatica Sinica , 2014 , 40 ( 5 ): 830 - 837 .
ZHANG B , YANG C H , LI Y G , et al . Additive requirement ratio prediction using trend distribution features for hydrometallurgical purification processes [J ] . Control Engineering Practice , 2016 , 46 : 10 - 25 .
张斌 . 不确定信息下湿法冶锌除铜过程建模与控制 [D ] . 长沙:中南大学 , 2016 .
ZHANG B . Modeling and control of copper removal process of Zinc hydrometallurgy with uncertainty [D ] . Changsha:Central South University , 2016 .
孙备 . 锌湿法冶炼砷盐除钴过程优化控制 [D ] . 长沙:中南大学 , 2015 .
SUN B . Optimization and control of arsenic trioxide activated cobalt removal process in Zinc hydrometallugy [D ] . Changsha:Central South University , 2015 .
熊伟丽 , 郭校根 . 一种基于多工况识别的过程在线监测方法 [J ] . 控制与决策 , 2018 , 33 ( 3 ): 403 - 412 .
XIONG W L , GUO X G . A process on-line monitoring method based on multi-mode identification [J ] . Control and Decision , 2018 , 33 ( 3 ): 403 - 412 .
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