摘要
为了降低递推部分最小二乘(RPLS)建模方法的模型校正频率,开发了一种基于模型性能评估的RPLS(MPA-RPLS)模型。首先,根据过程的初始特性,自动生成模型的置信限,以均方根误差(RMSEP)为性能指标,评估模型性能;依据模型性能的评估结果,选择性地启动模型校正和置信限校正。然后,引入滑动平均滤波器消除过程变量中的噪声,探讨噪声对模型性能的影响程度。最后,将MPA-RPLS模型应用于一个化学反应过程——C8芳烃临氢异构化过程,基于大量工业数据,进行仿真验证。仿真结果表明:本文开发的模型仅以微小的精度损失换取了模型计算效率的大幅提高(即模型校正频率大幅下降);滑动平均滤波器可有效地处理变量的噪声,改善模型的预测精度。
In order to reduce the model updating frequency of recursive partial least squares （RPLS） modeling methods, a RPLS model based on the model performance assessment （MPA-RPLS） is developed. Firstly, a confidence limit of the model is generated automatically based on the initial behavior of a process. And a root mean squared error of prediction （RMSEP） is used as a performance index to evaluate the model. Base on the results of the model performance assessment, the model updating is selectively activated, in the meanwhile, the confidence limit is also updated. Subsequently, a moving average filter is integrated into the model to eliminate the noise embbeded in variables, and the effect of the noise on the model performance is then investigated. At last, the developed model is applied to a chemical reaction process, hydro-isomerization process of Cs-aromatics. Simulation is run based on a large number of industrial data. The simulation results show that the computational efficiency is improved greatly （model updating frequency is reduced greatly） by the developed model, while a minor loss of the prediction accuracy is found. The noise embedded in variables could be dealt with effectively by the moving average filter, hence the prediction accuracy is improved.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2014年第12期4875-4882,共8页
CIESC Journal
基金
国家自然科学基金项目(61203133
61203072
61304125)~~
