摘要
针对实际生产过程中结疤、结垢等原因造成的蒸发器出口物料浓度在线检测不准的问题，在分析蒸发器非线性特性及影响出口物料浓度因素的基础上，建立了基于核偏最小二乘法的出口物料浓度的软测量模型，利用KPLS有效的非线性特征提取功能，建立了相关直接检测变量与出口物料浓度之间的非线性关系，实现了出口浓度的在线测量。基于工业数据的仿真结果证明，该方法比线性偏最小二乘法更有效，模型精度满足实际生产工艺要求。
Considering the difficulty of keeping the accuracy of on-line product concentration measurement in practical process under scaling or scabbing conditions, Kernel partial least square （KPLS） algorithm is proposed to build soft-sensor model for the product concentration based on the analysis of nonlinear characteristic and factors affecting product concentration of the vertical tube falling-film evaporator. KPLS-based model realizes on-line concentration measurement by effectively extracting nonlinear eigenvector of concentration and relevant detection variables. Simulation results show that the KPLS-based model achieved better performance in accuracy than that in the conventional linear partial least squares-based model, and relative error for production concentration measurement can meet the practical requirements.
出处
《计量学报》
CSCD
北大核心
2011年第2期178-181,共4页
Acta Metrologica Sinica
基金
国家自然科学基金（60874069）
湖南省自然科学基金（09J.13122
07JJ6121）
