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在湿法炼锌的中性浸出过程中，pH的稳定性直接影响产品的生产质量。针对传统人工控制方法在应对溶剂浓度波动和大时滞所带来的pH稳定控制难题，提出了一种基于主动学习分类的模糊规则提取策略。通过深入分析过程机理，将中性浸出过程细分为多个典型工况。利用近似线性依赖方法和主动学习算法，从大量历史数据中精确筛选出代表典型工况的信息样本。随后，采用支持向量机算法，在不同的工况下提取支持向量，并构建模糊规则集。通过实际工业数据的案例进行仿真验证，结果表明，该方法在不同工况下的pH控制合格率高达93.17%，方差低至0.011，充分验证了算法的有效性与实用性。该成果为中性浸出过程的pH稳定控制提供了坚实而有效的支撑。
Abstract：In the neutral leaching process of zinc hydrometallurgy, the stability of pH value directly affects the quality of the product. A fuzzy rule extraction strategy based on active learning classification was proposed to address the challenge of pH value stability control caused by solvent concentration fluctuations and large time delays in traditional manual control methods. The neutral leaching process was subdivided into multiple typical operating conditions by in-depth analysis of the process mechanism. Employing the approximate linear dependency method and active learning algorithm, the information samples representing typical working conditions were accurately screened from a large number of historical data. Then, the support vector machine algorithm was used to extract support vectors under different operating conditions and a fuzzy rule set was constructed. The results show that the qualified rate of pH value control under different working conditions is as high as 93. 17%, and the variance is as low as 0. 011, which fully verifies the effectiveness and practicability of the algorithm. The results provide a solid and effective support for pH stability control in neutral leaching process.
[1]SUN B,GUI W H,WANG Y L,et al.A gradient optimization scheme for solution purification process[J].Control Engineering Practice,2015,44:89-103.
[2]毛家怡,尤文.专家模糊控制的氩氧精炼炉口喷溅抑制系统研究[J].现代电子技术,2021,44(1):117-121.MAO J Y,YOU W.Juventus research on expert fuzzy control for splash suppression system at the mouth ofargon oxygen refining furnace[J].Modern Electronic Technology,2021,44(1):117-121.
[3]樊毅,冯兴隆,管孝强,等.基于模糊控制技术的磨机控制系统设计应用研究[J].矿冶,2020,29(3):89-93.FAN Y,FENG X L,GUAN X Q,et al.Research on the design and application of mill control system based on fuzzy control technology[J].Mining and Metallurgy,2020,29(3):89-93.
[4]邓鹏.基于规则提取的铜闪速熔炼过程Fe_3O_4含量控制方法研究[D].长沙:中南大学,2023.DENG P.Research on the control method of Fe_3O_4content in copper flash smelting process based on rule extraction[D].Changsha:Central South University,2023.
[5]邢亚英,付丽琴,段晓亮,等.基于人工智能神经网络的浮选回收率软测量模型研究[J].有色金属工程,2021,11(9):95-99.XING Y Y,FU L Q,DUAN X L,et al.Research on soft sensing model of flotation recovery rate based on artificial intelligence neural network[J].Nonferrous Metals Engineering,2021,11(9):95-99.
[6]任永功,张云鹏,张志鹏.基于粗糙集规则提取的协同过滤推荐算法[J].通信学报,2020,41(1):76-83.REN Y G,ZHANG Y P,ZHANG Z P.Collaborative filtering recommendation algorithm based on rough set rule extraction[J].Journal of Communications,2020,41(1):76-83.
[7]刘雁文,胡炎,邰能灵.基于决策树的智能变电站运维专家系统规则提取方法[J].电力科学与技术学报,2019,34(1):123-128.LIU Y W,HU Y,TAI N L.Rule extraction method for intelligent substation operation and maintenance expert system based on decision tree[J].Journal of ElectricPower Science and Technology,2019,34(1):123-128.
[8]赵彦涛,陈宇,陈英豪,等.基于BFCM-iWM模糊规则自提取的水泥分解炉温度控制[J].控制与决策,2019,34(2):383-389.ZHAO Y T,CHEN Y,CHEN Y H,et al.Temperature control of cement decomposition furnace based on BFCM-IWM fuzzy rule self extraction[J].Control and Decision,2019,34(2):383-389.
[9]徐立,白金牛,孟海东.基于概念格的云推理分类预测算法研究[J].控制工程,2020,27(11):1892-1900.XU L,BAI J N,MENG H D.Research on cloud inference classification prediction algorithm based on concept lattice[J].Control Engineering,2020,27(11):1892-1900.
[10]张中伟,梅红岩,周军,等.基于多目标协同进化遗传算法的规则提取方法[J].山东大学学报(工学版),2019,49(2):122-130.ZHANG Z W,MEI H Y,ZHOU J,et al.Rule extraction method based on multi-objective co evolutionary genetic algorithm[J].Journal of Shandong University(Engineering Edition),2019,49(2):122-130.
[11]GAO C H,GE Q H,JIAN L.Rule extraction from fuzzy-based blast furnace SVM multiclassifier for decision-making[J].IEEE Transactions on Fuzzy Systems,2014,22(3):586-596.
[12]SINGH N,SINGH P,BHAGAT D.A rule extraction approach from support vector machines for diagnosing hypertension among diabetics[J].Expert Systems with Applications,2019,130:188-205.
[13]白锐,佟绍成,柴天佑.从数据中提取生料浆配料过程运行控制的模糊规则[J].控制与决策,2010,25(7):1015-1020.BAI R,TONG S C,CHAI T Y.Extracting fuzzy rules for operating control of raw material slurry mixing process from data[J].Control and Decision,2010,25(7):1015-1020.
[14]GE Z Q.Active learning strategy for smart soft sensor development under a small number of labeled data samples[J].Journal of Process Control,2014,24(9):1454-1461.
[15]YAN Z B,CHEN J H.Enhancing quality of statistic monitoring models by training set design with active learning approach[J].Chemometrics and IntelligentLaboratory Systems,2016,151:201-218.
[16]王越,李勇,张文静.面向可解释性的软件缺陷预测主动学习方法[J].现代电子技术,2024,47(20):101-108.WANG Y,LI Y,ZHANG W J.Active learning method for software defect prediction based on interpretability[J].Modern Electronic Technology,2024,47(20):101-108.
[17]陈宇,刘学斌,劳佳锋,等.融合浸出过程流程特性的梯形LSTM终点pH值模型预测控制[J].有色金属工程,2023,13(11):49-55.CHEN Y,LIU X B,LAO J F,et al.A trapezoidal LSTM endpoint pH model predictive control based on the characteristics of the leaching process flow[J].Nonferrous Metals Engineering,2023,13(11):49-55.
[18]曹念,孙华芬,史朝阳,等.基于鲸鱼算法优化支持向量机的露天煤矿边坡稳定性预测[J].矿冶,2023,32(6):9-14.CAO N,SUN H F,SHI C Y,et al.Optimization of support vector machine for slope stability prediction of open pit coal mines based on whale algorithm[J].Mining and Metallurgy,2023,32(6):9-14.
[19]ZHANG B,YANG C H,ZHU H Q,et al.Controllable-domain-based fuzzy rule extraction for copper removal process control[J].IEEE Transaction on Fuzzy Systems,2018,26(3):1744-1756.
[20]陈浩,吴沐宸,陈江涛,等.证据理论框架下主动学习代理模型驱动的CFD模拟不确定性量化方法[J].空气动力学学报,2024,42(9):86-99.CHEN H,WU M C,CHEN J T,et al.Quantitative method for uncertainty in CFD simulation driven by active learning agent model under the framework of evidence theory[J].Journal of Aerodynamics,2024,42(9):86-99.
[21]SHI X D,XIONG W L.Approximate linear dependence criteria with active learning for smart soft sensor design[J].Chemometrics and Intelligent Laboratory Systems,2018,180:88-95.
[22]FEZAI R,MANSOURI M,OKBA T,et al.Online reduced kernel principal component analysis for process monitoring[J].Journal of Process Control,2018,61(1):1-11.
[23]FENG Z X,LI Y G,SUN B,et al.A trend-based event-triggering fuzzy controller for the stabilizing control of a large-scale zinc roaster[J].Journal of Process Control,2021,97:59-71.
基本信息:
DOI：10.20237/j.issn.1007-7545.2025.05.004
中图分类号:TF813;TP18
引用信息:
[1]陈宇,黄煜栋,刘学斌,等.基于主动学习的浸出过程pH稳定控制研究[J].有色金属(冶炼部分),2025(05):27-35.DOI:10.20237/j.issn.1007-7545.2025.05.004.
基金信息:
浙江省自然科学基金杭州区域创新发展联合基金资助项目（LHZY24A010006）; 浙江省教育厅一般项目（Y202455590,Y202352510）
2024-10-18
2024
2024
2024-12-25
2024-12-26
1
2025-04-25
2025-04-25