引用本文:孔玲爽,阳春华,桂卫华,王雅琳.一种解决蕴含不确定性信息的氧化铝配料问题的智能优化方法[J].控制理论与应用,2009,26(9):1051~1055.[点击复制]
KONG Ling-shuang,YANG Chun-hua,GUI Wei-hua,WANG Ya-lin.Intelligent optimization of raw material blending for alumina production with information uncertainty[J].Control Theory and Technology,2009,26(9):1051~1055.[点击复制]
一种解决蕴含不确定性信息的氧化铝配料问题的智能优化方法
Intelligent optimization of raw material blending for alumina production with information uncertainty
摘要点击 1824  全文点击 1222  投稿时间：2008-06-04  修订日期：2008-12-01
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DOI编号  10.7641/j.issn.1000-8152.2009.9.CCTA080573
  2009,26(9):1051-1055
中文关键词  氧化铝  配料  不确定  智能优化  专家分级推理  遗传算法
英文关键词  alumina  raw material blending  uncertainty  intelligent optimization  expert hierarchical reasoning  genetic algorithm
基金项目  国家自然科学基金资助项目(60634020, 60804037, 60874069).
作者单位E-mail
孔玲爽* 中南大学信息科学与工程学院 lshkong@mail.csu.edu.cn 
阳春华 中南大学信息科学与工程学院  
桂卫华 中南大学信息科学与工程学院  
王雅琳 中南大学信息科学与工程学院  
中文摘要
      针对原料质量不稳定和成分检测大滞后带来的信息不确定性, 提出了一种两级智能优化方法实现氧化铝配料过程中生料浆质量的优化控制. 该方法通过引入中间优化目标, 将优化问题分解为原料配比优化和料浆调配优化, 逐步弱化不确定信息对生料浆质量的影响. 配比优化基于入槽生料浆质量预测模型, 设计了专家分级推理机制,实现多质量指标约束条件下的配比优化设定; 调配优化将不确定的生料浆质量信息引入调配优化模型约束中, 采用改进遗传算法求解最优调配方案, 配制高质量的生料浆. 将提出的方法应用于国内某厂氧化铝配料过程, 实现了生料浆质量指标的优化控制, 简化了工艺流程, 为存在信息不确定的长流程工业过程的优化控制提供了范例.
英文摘要
      Considering the uncertainty of raw material quality and time-lagging in composition measurement, a twostage intelligent optimization method is proposed to realize the optimal control of slurry quality for the raw material blending process in alumina production. By introducing an intermediate optimization objective, the blending optimization problem is decomposed into two stages, i.e. the optimization of the mixture ratio and the optimization of slurry combination, to reduce the effect of uncertainty step by step. In the mixture-ratio optimization, an expert hierarchical reasoning strategy based on the quality prediction model is proposed to optimize the mixture ratio with multi-index constraints. Then, an optimal combination model with uncertainty is built by incorporating the uncertainty of raw slurry quality into constraints, and an improved genetic algorithm is used to solve it. The proposed approach has been applied to the blending process of an alumina factory of China, and the optimal control of slurry quality is realized and the blending process is simplified.