A recursive optimization strategy using load cosine similarity in unitized latent variable space was proposed to address information loss problem of variable correlation in batch process when optimized by principal component similarity. An extension matrix of time-segment and index variables was broken down by principal component analysis. The information redistribution generated orthogonal unitized latent variable space of principal components and a non-unitized load matrix containing much more variable information. Load cosine similarity between time-segment and index variables in the latent variable space as well as index increment between each time segment were calculated to recursively correct operation trajectory. The non-unitized load matrix by principal component decomposition not only reduced information loss of variable correlation in the latent variable space but also simplified recursive algorithm for updating operation trajectory. Finally

the effectiveness of the proposed method was verified by batch process optimization of one chemical crystal purification.