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针对间歇过程多批次、多时段特性,提出一种多批次融合线性变参数(LPV)建模方法.传统间歇过程LPV建模是连续过程LPV建模方法的简单复制,仅仅利用了单个批次的信息,模型的扩展性较差.多批次融合建模方法考虑间歇过程多批次特性,依据调度变量将所有批次数据划分为不同阶段,在各阶段建立子模型,最终采用高斯权重函数融合各子模型.在该模型中,多批次的数据通过EM(expectationmaximization)算法融入到模型参数的辨识中,能够克服间歇过程初始值波动给模型带来的影响.最后对青霉素发酵过程进行仿真,通过单个批次数据建模与多个批次建模的比较结果可知,多批次融合LPV建模方法精度更高,适用性更广泛.“,”Due to the multi-batch characteristics of batch processes,a linear variable parameter (LPV) model,based on multiple batch data,is proposed to describe the batch process.The generic LPV modeling method for batch processes is a simple duplication of that for continuous processes,it only uses the information from a single batch and is subject to poor scalability.The proposed LPV method fuses the date of multiple batches and divides all the data into different stages according to the scheduling variables.At each stage,local models are established and then combined according to an exponential weighting function.In this model,an EM algorithm is employed for the fusion of data from multiple batches into parameter estimation,which avoids the influence of the initial value fluctuation of batches.The proposed method is evaluated using a simulated penicillin fermentation process.