| 引用本文: | 曹斌芳,谢永芳,阳春华,桂卫华,王晓丽.基于多源数据的铝土矿浮选生产指标集成建模方法[J].控制理论与应用,2014,31(9):1252~1261.[点击复制] |
| CAO Bin-fang,XIE Yong-fang,YANG Chun-hua,GUI Wei-hua,WANG Xiao-li.Integrated modeling for production index of bauxite flotation based on multi-source data[J].Control Theory and Technology,2014,31(9):1252~1261.[点击复制] |
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| 基于多源数据的铝土矿浮选生产指标集成建模方法 |
| Integrated modeling for production index of bauxite flotation based on multi-source data |
| 摘要点击 2157 全文点击 1721 投稿时间：2014-01-07 修订日期：2014-05-16 |
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| DOI编号 10.7641/CTA.2014.40010 |
| 2014,31(9):1252-1261 |
| 中文关键词 泡沫特征 生产指标 集成建模 偏最小二乘 正则极限学习机 |
| 英文关键词 froth features production index integrated model partial least squares regular extreme learning machine |
| 基金项目 国家自然科学基金重点资助项目(61134006); 国家创新研究群体科学基金资助项目(61321003); 国家自然科学基金资助项目(61473318, 61304126); 高等学校博士学科点专项基金博导类资助课题(20120162110076); 湖南省研究生科研创新项目(CX2014B077). |
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| 中文摘要 |
| 在长流程浮选过程中, 生产指标难以在线检测, 造成操作不及时, 影响系统的稳定运行. 本文提出了一种基 于多源数据的铝土矿浮选过程生产指标集成建模方法. 首先结合浮选机理和现场工人经验, 分析影响和反映生产 指标的多源数据(生产数据和泡沫图像特征数据); 然后分别建立各生产指标预测子模型和同步误差补偿子模型; 最 后采用信息熵和智能协调策略分别构建精矿品位和尾矿品位的集成预测模型. 工业验证和工况分析表明, 本文集 成建模方法具有良好的预测性能和较强的泛化性, 为基于生产指标的浮选过程操作参数控制和全流程优化奠定基 础. |
| 英文摘要 |
| Because of the difficulty in online detecting the production index in a long-time flotation process, we are unable to take the counterpart operation timely to keep the production process stable. To deal with this problem, we propose an integrated prediction model for the production index of bauxite flotation based on multi-source data. Firstly, we make use of the flotation mechanism and the experiences of operators to analyze the multi-source data (production data and froth image features) that affects the production index. Then, the prediction sub-model and the corresponding compensation sub-model for each production index are built, and their parameters are optimized. Then, the integrated prediction models of concentrate grade and tailing grade are established by using the information entropy and coordination strategy. Industrial validation results show that the proposed method improves the prediction accuracy and adaptability, which lays a foundation for controlling the operating parameters of the flotation and the total process optimization. |
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