In recent years, many researchers have demonstrated the desirability of applying optimal control techniques to chemical processes. However, most of the experimental results were obtained by using computer simulations of the controlled system. The research reported herein is an attempt to "bridge the gap" between theory and practice by applying optimal control techniques to the actual system rather than a computer simulation of the system. A linear discrete time model of the process was used to obtain the optimal control law. The optimal control law was obtained by a dynamic programming solution procedure. The control is an explicit function of the state of the system and of future upsets to the system. Statistical models were developed which allow nonstationary stochastic upsets to be considered.