基于主元相似度的间歇过程操作曲线递推优化

针对一类较长周期的间歇过程操作优化问题，提出了一种基于正常运行批次的数据驱动型操作曲线递推优化方法。首先采用分段离散化方法将原非线性优化问题转化为线性优化问题，再利用主元分析对离散化后的高维时段变量进行降维处理，然后在降维后的主元平面中，基于时段变量与最终产品指标间的余弦相似度，实现对原操作曲线的摄动优化。考虑到时段变量方差和相似度随批次会发生变化，建立了递推算法以实现操作曲线的递推更新。最后将该方法应用于某化工产品的间歇结晶过程中，结果验证了所提方法的有效性。

A recursive optimization method for batch process trajectories based on similarity of principal components

A recursive optimization method for data-driven operating trajectory, which was based on daily normal operation batches, was proposed for long cycle batch processes. First, original nonlinear optimization was simplified to a high dimensional linear optimization by segmented discretization and high dimensional segmented variables were transformed into lower dimensional one by PCA (principal component analysis) algorithm. Then, snapshot optimization for original operating trajectory was performed according to cosine similarity between time-segmented variables in dimensionality-reduced principal element plane and performance index of the final products. Finally, recursive algorithm for trajectory optimization was developed by consideration of changes in square errors and similarity of time-segmented variables between batches. Application to a batch crystallization process has illustrated effectiveness of this method.