A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent “time” variables, namely, the run time during a batch and the batch counter. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This entry describes the various control and optimization strategies available for the operation of batch processes. These include conventional feedback control, predictive control, iterative learning control, and run-to-run control on the one hand and model-based repeated optimization and model-free self-optimizing schemes on the other.