基于分布机器视觉的铝土矿选矿过程协调优化方法研究

项目名称： 基于分布机器视觉的铝土矿选矿过程协调优化方法研究

项目编号： No.61304126

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 王晓丽

作者单位： 中南大学

项目金额： 26万元

中文摘要： 我国自主创新的选矿-拜耳法氧化铝生产新工艺为我国铝工业的可持续发展开辟了新途径，但铝土矿选矿过程流程长、过程变量及作业间耦合关系复杂且存在不确定性使过程建模和优化控制困难，主要通过人工判断泡沫状态凭经验控制，工况波动大、资源利用率低。为此，本项目在铝土矿选矿流程中多点布置机器视觉，研究机器视觉敏感特征分布提取方法和基于分布机器视觉图像序列敏感特征重构的级联作业关联建模，揭示级联作业间的内在关联关系；建立基于机器视觉敏感特征分布变化趋势融合的过程指标预测模型；基于过程模型，研究面向复杂矿源的浮选过程总目标主从分解-协调方法和全流程多操作变量主从协调优化设定方法；将所提方法应用于铝土矿选矿过程验证其有效性，形成选矿过程主从协调优化理论和方法。本项目对提高铝土矿选矿产品质量和节能降耗具有重要意义，并为高效选矿奠定理论和方法基础，具有重要的工业应用价值和科学价值。

中文关键词： 泡沫浮选；分布机器视觉；集成建模；协调优化；

英文摘要： Mineral processing-Bayer process for alumina production is a new technology developted only in our country to process high silica bauxite,which provides a new way to the sustainable development of aluminum industry in our country. But the mineral processing process is long . There are strong couplings between the flotation banks and between the process variables with uncertainties, which cause difficulty in modeling and optimal control of the process, so that the process is mainly controlled by operators experiencely according to the surface features of the froths.These cause fluctuation of the whole process and low recovery of valuable minerals. Therefore, multiple machine visions are fixed up distributedly in the process to collect distributed froth images in this project. Extraction method of the sensitive froth features-distribution from the distributed machine visions, and correlation model for the cascade banks based on the feature-reconstruction of distributed froth image series are studied to disclose the internal relationship between the banks. Prediction model for process indices based on the fusion of variation trends of the sensitive froth features-distribution is proposed. Based on these models, complex ore feed-oriented master-slave decomposition & coordination of the final object of the whole flot

英文关键词： Froth flotation；distributed multiple machine vision；integrated modeling；coordinative optimization；