摘要
针对氧化铝蒸发过程实时检测数据存在随机误差或显著误差、出口物料浓度检测滞后大,导致实测数据难以真实反映实际生产状态的现状,本文在蒸发过程多点稳态检测研究的基础上,利用基于核偏最小二乘法的出口物料浓度软测量结果提供的有效信息,建立以污染正态分布的鲁棒估计函数为优化目标的数据协调模型,并采用遗传算法求解获得蒸发过程的协调数据.实际计算结果表明,该协调模型能避免显著误差的影响,实现测量数据的可靠在线协调计算,为过程操作调节提供依据.
It is difficult to reflect the practical production status of alumina evaporation process by field data due to random error,gross error and the lag-time measurement of output concentration.For this problem,taking the robust estimation function of contaminated normal distribution as the optimization objective,a data reconciliation（DR） model is established through studying multi-point steady state detection method for evaporation system and utilizing output concentration soft measurement results produced by kernel partial least squares method.GA（genetic algorithm） is applied to solving the DR model to obtain the reconciliation data of evaporation process.The actual computation results show that the proposed DR model is robust to gross error,and it can effectively reconcile data online and provide guidance for evaporation process operation.
出处
《信息与控制》
CSCD
北大核心
2010年第6期732-737,共6页
Information and Control
基金
国家自然科学基金资助项目(60874069)
湖南省自然科学基金资助项目(09JJ3122
07JJ6121)
关键词
鲁棒估计
数据协调
稳态检测
核偏最小二乘
蒸发过程
robust estimation
data reconciliation
steady state detection
kernel partial least squares
evaporation process
