基于函数型主成分分析的过程批次响应序贯建模

Sequential Modeling of Process Batch Response Based on Functional Principal Component Analysis

针对间歇生化过程操作条件的批次响应建模问题,结合试验设计方法,提出一种基于函数型主成分分析的序贯建模策略。首先,使用B样条基函数平滑法将离散的批次响应序列转化为连续的响应函数曲线;然后,运用函数型主成分分析得到响应函数的均值曲线、主成分函数和主成分得分;最后,构建主成分得分与操作条件之间的Kriging模型,用于预测试验区域内任意操作条件所对应的主成分得分,从而建立批次响应关于操作条件的模型。为了提高模型预测精度,依据改进的收敛条件,采用序贯设计迭代更新模型。通过生化反应网络试验仿真,验证了该建模策略的有效性,且仿真结果表明该建模策略具有较好的数据可视化和模型解释能力。

Combined with the method of experiment design,a sequential modeling strategy based on functional principal component analysis(FPCA)was proposed for the batch response modeling of operation conditions in biochemical processes.Firstly,having B-spline basis function smoothing method adopted to transform discrete batch response sequence into a continuous response function curve;then,having FPCA employed to analyze and obtain response function’s mean curve,principal component function and principal component score;finally,having Kriging model between the principal component score and operating conditions constructed to predict the principal component score corresponding to any operating conditions in the experiment region so as to establish the model of batch response on operating conditions.For purpose of improving prediction accuracy of the model,having sequential design used to update the model according to the improved convergence condition was implemented,including having effectiveness of the proposed modeling strategy verified by biochemical reaction network experiment simulation.The simulation results show that,the proposed modeling strategy has better data visualization and model interpretation ability.