基于专家经验引导的浮选泡沫图像表征学习

张进; 黄嘉豪; 艾明曦; 唐朝晖; 谢永芳; 马军

doi:10.16383/j.aas.c250628

基于专家经验引导的浮选泡沫图像表征学习

doi: 10.16383/j.aas.c250628 cstr: 32138.14.j.aas.c250628

张进^1,,
黄嘉豪^1,,
艾明曦^2,,
唐朝晖^3,,
谢永芳^3,,
马军^1,

1.
昆明理工大学信息工程与自动化学院昆明 650500
2.
云南大学信息学院昆明650500
3.
中南大学自动化学院长沙 410083

基金项目: 国家自然科学基金(62303201, 62303404, 62171476, 62233018), 云南省基础研究计划项目 (202401CF070111, 202401CF070171, 202301BE070001-049)资助

详细信息

作者简介:
张进：昆明理工大学信息工程与自动化学院副教授. 2022 年获得中南大学控制科学与工程专业博士学位. 主要研究方向为深度学习, 数据高效建模. Email: j.zhang@kust.edu.cn

黄嘉豪：昆明理工大学信息工程与自动化学院硕士研究生. 主要研究方向为基于机器视觉的复杂工业过程智能监测与优化. Email: jiahao_huang@stu.kust.edu.cn

艾明曦：科学与工程专业博士学位. 主要研究方向为深度学习, 流程工业智能感知与建模. 本文通信作者 Email: mingxi_ai@ynu.edu.cn

唐朝晖：中南大学自动化学院教授. 2008 年获得中南大学控制科学与工程专业博士学位. 主要研究方向为深度学习, 图像处理. Email: zhtang@csu.edu.cn

谢永芳：中南大学自动化学院教授. 1999 年获得中南大学控制科学与工程专业博士学位. 主要研究方向为复杂工业过程建模、优化与控制. Email: yfxie@csu.edu.cn

马军：昆明理工大学信息工程与自动化学院教授. 2016年获得昆明理工大学冶金工程控制专业博士学位. 主要研究方向为故障诊断. Email: mjun@kust.edu.cn

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出版历程
- 收稿日期: 2025-11-13
- 录用日期: 2026-01-28
- 网络出版日期: 2026-03-04

Expert Experience Informed Froth Image Representation Learning

1.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500
2.
School of Information Science and Engineering, Yunnan University, Kunming 650500
3.
School of Automation, Central South University, Changsha 410083

Funds: Supported by National Natural Science Foundation of China (62303201, 62303404, 62171476, 62233018), and Yunnan Fundamental Research Projects (202401CF070111, 202401CF070171, 202301BE070001-049)

More Information

Author Bio:
ZHANG Jin　Associate professor at the Faculty of Information Engineering and Automation, Kun- ming University of Science and Technology. He received his Ph.D. degree in control science and engineering from Central South University in 2022. His research interests include deep learning and data efficient modeling

HUANG Jia-Hao　A Master student at the Faculty of Information Engineering and Automation, Kunming University of Science and Technology. His main research interest is intelligent monitoring and optimization of complex industrial processes based on machine vision

AI Ming-Xi　Lecture at the School of Information Science and Engineering, Yunnan University. She received her Ph.D. degree in control science and engineering from Central South University in 2022. Her research interests include deep learning, intelligent perception and modeling in industrial processes. Corresponding author of this paper

TANG Zhao-Hui　Professor at the School of Automation, Central South University. He received his Ph.D. degree in control science and engineering from Central South University in 2008. His main research interests include deep learning and image processing

XIE Yong-Fang　Professor at the School of Automation, Central South University. He received his Ph.D. degree in control science and engineering from Central South University in 1999. His research interests include modeling, optimization and control of complicated industrial processes

MA Jun　Professor at the Faculty of Information Engineering and Automation, Kunming University of Science and Technology. He received his Ph.D. degree in metallurgical engineering control from Kunming University of Science and Technology in 2016. His research interest is fault diagnosis

摘要

摘要: 矿浆品位等关键指标难以通过传感器在线直接测量, 研究利用易获取的过程运行数据与泡沫图像间接估计矿浆品位的软测量方法具有重要工程意义. 针对传统泡沫图像表征方法表征能力不足且泛化性差的问题, 提出专家经验引导的泡沫图像表征学习方法. 该方法由分布特征隔离与经验引导表征两部分构成: 前者将泡沫图像映射至尺寸、颜色、纹理等具有明确工艺解释意义的视觉属性子空间, 以及用于补充隐性判别信息的数据特征子空间, 实现结构化的视觉属性表征; 后者通过构造模拟人工视觉判断过程的对比学习机制, 引导模型在各子空间中学习与专家经验一致的判别特征, 建立视觉属性子空间与专家知识之间的显式对应关系. 基于中国某铅锌选厂的工业数据实验结果表明, 所提方法在锌、铅、铁底流品位软测量中的决定系数较近期提出的软测量模型DEFIE分别提升 3.97%、1.97%和 2.40%, 预测区间覆盖率提升15.00%.
- 泡沫浮选 /
- 软测量 /
- 表征学习 /
- 知识嵌入
Abstract: Soft-sensing approaches that indirectly estimate slurry grade by integrating easily obtainable process data with froth images are of considerable engineering importance. To address the limitations of insufficient feature representation and poor generalization of conventional froth image representation methods, this paper proposes an expert experience informed froth image representation learning method. It consists of distributed feature isolation and experience informed representation learning. The former projects froth images into visual attribute subspaces with explicit kinetic meanings, including size, color, and texture, together with a data feature subspace that complements implicit discriminative information, thereby achieving structured visual pattern representations. The latter introduces an observation-mimicking contrastive learning strategy, which guides subspace learning to produce discriminative responses consistent with observation experience, thus establishing correspondence between learned subspaces and expert experience. Extensive experiments based on industrial data from a flotation plant in southern China demonstrate that, compared to the latest proposed soft sensing DEFIE method, the proposed method improves the coefficient of determination for Zn, Pb, and Fe in zinc tailing grade estimation by 3.97%, 1.97%, and 2.40%, respectively, and enhances the prediction interval coverage probability by 15.00%.
- froth flotation /
- soft sensing /
- representation learning /
- knowledge embedding

HTML全文

图 1 基于专家经验引导的泡沫图像表征学习框图, 包括分布特征隔离网络与经验引导表征学习机制

Fig. 1 Framework of EI-FRIL, which consists of distributed feature isolation network and experience informed representation learning mechanism

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图 2 经验引导表征学习示例 ((a) ~ (c)分别对应尺寸、颜色、纹理特异性学习示例)

Fig. 2 Illustrative examples for experience informed representation learning ((a) ~ (c) are the learning instances corresponding to size, color, and texture, respectively)

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图 3 铅锌浮选槽及数据采集

Fig. 3 Lead-zinc flotation bank and data collection

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图 4 浮选监测模型对比实验结果残差图及散点图

Fig. 4 Comparison experiment results of flotation monitoring models shown by scatter plots and residual plots

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图 5 浮选监测模型预测不确定性度量

Fig. 5 Prediction uncertainty evaluation of flotation monitoring models

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图 6 浮选监测模型可解释方差得分度量

Fig. 6 Explained variance score evaluation of flotation monitoring models

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图 7 知识嵌入模型对比实验结果残差图及散点图

Fig. 7 Comparison experiment results of knowledge embedding models shown by scatter plots and residual plots

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图 8 知识嵌入模型预测不确定性度量

Fig. 8 Prediction uncertainty evaluation of knowledge embedding models

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图 9 知识嵌入模型可解释方差得分度量

Fig. 9 Explained variance score evaluation of knowledge embedding models

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图 10 锑浮选五类典型工况图像示例 ((a) ~ (e)对应低品位、偏低品位、适中品位、偏高品位、高品位工况)

Fig. 10 Froth images captured from five different working conditions ((a) ~ (e) correspond to low grade, slightly low-grade, medium-grade, slightly high-grade, and high-grade conditions, respectively)

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表 1 浮选监测模型对比量化实验结果

Table 1 Quantitative comparison experiment results of flotation monitoring models

方法	成分	参数(M)	FLOPs(G)	指标
方法	成分	参数(M)	FLOPs(G)	MAE	RMSE	R²
TCN^[37]	Zn	20.2	3.5	0.2876	0.4394	0.8103
	Pb			0.2856	0.3969	0.8401
	Fe			0.2421	0.3706	0.8621
MSFE^[22]	Zn	24.5	5.8	0.2415	0.4117	0.8335
	Pb			0.2704	0.3773	0.8555
	Fe			0.2365	0.3586	0.8709
RLN^[38]	Zn	24.0	7.7	0.2535	0.4029	0.8366
	Pb			0.2686	0.3702	0.8609
	Fe			0.2149	0.3052	0.9605
ViT^[39]	Zn	86.8	17.6	0.2716	0.4233	0.8240
	Pb			0.2756	0.3728	0.8589
	Fe			0.1991	0.2694	0.9202
DEFIE^[4]	Zn	27.8	8.3	0.2349	0.3909	0.8499
	Pb			0.2489	0.3558	0.8720
	Fe			0.1842	0.2721	0.9257
EI-FIRL	Zn	35.9	14.8	0.2156	0.3488	0.8836
	Pb			0.2394	0.3338	0.8892
	Fe			0.1658	0.2281	0.9479

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表 2 知识嵌入模型对比量化实验结果

Table 2 Quantitative experiment results of knowledge embedding models

方法	成分	指标
方法	成分	MAE	RMSE	R²
ConcFus^[24]	Zn	0.2638	0.3934	0.8507
	Pb	0.2893	0.3881	0.8477
	Fe	0.2287	0.3281	0.8922
PriKnoInfo^[30]	Zn	0.2442	0.3830	0.8585
	Pb	0.2595	0.3540	0.8733
	Fe	0.2156	0.3146	0.9009
PriKnoDisti^[13]	Zn	0.2291	0.3772	0.8643
	Pb	0.2406	0.3310	0.8826
	Fe	0.2007	0.2530	0.9366
EI-FIRL	Zn	0.2156	0.3488	0.8836
	Pb	0.2394	0.3338	0.8892
	Fe	0.1658	0.2281	0.9479

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表 3 消融实验量化结果

Table 3 Quantitative results of ablation study

设置	成分	指标
设置	成分	MAE	RMSE	R²
变体1	Zn	0.2264	0.3679	0.8684
	Pb	0.2445	0.3369	0.8839
	Fe	0.1985	0.2640	0.9388
变体2	Zn	0.2499	0.3917	0.8520
	Pb	0.2606	0.3666	0.8641
	Fe	0.1988	0.2685	0.9334
变体3	Zn	0.2245	0.3600	0.8720
	Pb	0.2412	0.3345	0.8842
	Fe	0.1903	0.2477	0.9400
变体4	Zn	0.2234	0.3561	0.8776
	Pb	0.2387	0.3336	0.8875
	Fe	0.1731	0.2404	0.9421
变体5	Zn	0.2198	0.3627	0.8731
	Pb	0.2445	0.3369	0.8839
	Fe	0.1875	0.2473	0.9401
EI-FIRL	Zn	0.2156	0.3488	0.8836
	Pb	0.2394	0.3338	0.8892
	Fe	0.1658	0.2281	0.9479

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表 4 锑浮选工况分类验证实验

Table 4 Antimony working condition recognition validation experiments

方法	指标(%)
方法	Accuracy	Precision	Recall	F1
TCN^[37]	81.28	81.73	81.50	81.31
MSFE^[22]	83.07	83.46	83.41	83.09
RLN^[38]	84.07	84.40	84.30	84.05
ViT^[39]	82.18	82.53	82.38	82.15
DEFIE^[4]	85.31	85.28	85.75	85.17
ConcFus^[24]	85.18	85.22	85.57	85.11
PriKnoInfo^[30]	85.40	85.75	85.67	85.45
PriKnoDisti^[13]	86.14	86.28	86.65	86.12
EI-FIRL	89.41	89.71	89.48	89.36

下载: 导出CSV

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