Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for midbatch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a poly(methyl methacrylate) (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number-average molecular weight, weight-average molecular weight, and conversion to their respective set-points. Specifically, mean absolute percentage errors (MAPE) in these variables are reduced from 8.66%, 7.87%, and 6.13% under traditional PI control to 1.61%, 1.90%, and 1.67%, respectively.