Specialty chemical production makes up an important sector of the chemical processing industry. Examples include armaceutical and high quality polymer products. In these processes, economic gains are realized by achieving tightly specified product quality. This and additional factors, such as the ability to produce a wide range of product grades and the high cost of raw materials, favors using batch reactors for these products. While batch reactors provide benefits, they also introduce a variety of difficulties for closed loop control. In this work we address some of those issues and present a novel, data-driven, model predictive control scheme where the objective is explicitly formed to reach product quality. Furthermore, we demonstrate the ability of our method to incorporate time as a decision variable and appropriately implement corrective discrete events such as mid-batch additions. In a typical batch process, first the reactor is charged with some ingredients then a transforming process is carried out. As the transformation proceeds, the reactor contents transform exposing a wide range of operating states. Since dynamic behavior of chemical systems is dependent on state, these processes are characterized by transient dynamic behavior. Clearly, the traditional control objective of stabilizing operation around a steady-state condition is not meaningful. Instead, the control objective for this class of processes is to reach a desired product quality by the end of the batch. This objective is particularly important to reject feed stock variations that introduce variance which, left uncorrected, would propagate to the product quality. However, direct feedback control on quality is complicated by a number of factors. Foremost of these, is that the desired quality measurements are often not available online. Furthermore, development of reliable first principal models for these processes is often impracticable because of the complex, nonlinear behavior and the wide range of operating conditions. Traditionally then, indirect approaches such as trajectory tracking have been adopted. is demonstrated through quality set-point changes and feed stock disturbance rejection.