In this work, the problem of driving a batch process to a desired final product quality using data-driven model based midcourse correction (MCC) is described. Specifically, we adapt a sequential and orthogonalized partial least squares (SO-PLS) method to calibrate the inferential quality model, which takes into account the serial nature of the input batch data matrices and could retain the reliable information as much as possible when it is used to perform online quality prediction. Since the process variable trajectories that are necessary to predict the final quality are incomplete at a certain decision point, known data regression (KDR) is used to estimate the future trajectories, and the causal relationship of the initial conditions and the future candidate manipulated variables in determining the future process variable trajectories is also considered. Finally, taking the advantage of the latent variable model, the indicators that consider only the degrees of freedom are employed as hard constraints to confine the SO-PLS model only used in a valid region. The efficacy of the proposed approach is demonstrated through a virtual batch process and a cobalt oxalate synthesis process.