摘要
及时测定化工过程变量, 对确保生产过程稳定、有效控制产品质量具有重要意义. 基于实时样本数据,采用偏最小二乘方法, 以分块递归的方式, 为过程变量建立软测量模型. 在分析时序数据特性的基础上, 引入加权策略, 并提出选定相关参数的方法步骤, 推导构建了加权分块递归偏最小二乘回归方法 (WBRPLSR). 将该法实际应用于某公司PTA装置溶剂脱水塔, 为塔釡排出液 H2O含量建立软测量模型, 效果良好. 与已有方法相比, 它提高了建模效率, 改进了预测性能.
It is well known that to measure and estimate the chemical process variables in time has vital significance in ensuring process stabilization and effectively controlling its product quality. A soft sensor model of a chemical process was established by partial least squares method based on its time series data, and the model could be adjusted in the block-wise recursive way in the presence of new sample data. With a view to the time series data characteristics, a strategy of allotting different weight coefficients to the time series data was introduced, and an approach of how to ascertain the weight coefficients was provided in the meantime. Subsequently, the weighted block-wise recursive partial least squares regression (WBRPLSR) algorithm was developed and used to model the water content of solvent dehydration tower bottom drainage in a commercial purified terephthalic acid (PTA) unit. The experimental result showed that the algorithm was rapid and effective. Compared with some other methods, the WBRPLSR method increased modeling efficiency and improved prediction performance.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2005年第2期291-295,共5页
CIESC Journal
基金
国家自然科学基金项目 (20276063).~~
关键词
加权分块递归
偏最小二乘回归
PTA装置
化工过程建模
软测量
Algorithms
Dehydration
Least squares approximations
Mathematical models
Recursive functions
Regression analysis
Time series analysis
