Current real time optimization (RTO) techniques rely on waiting for steady state conditions then implementing new set points based on optimization of a steady state model of the plant. Industrial applications of steady state RTO have been reported in the literature, especially in oil refining, and these studies show that significant increases in profit may be achieved. Although the various steady state online optimization strategies are somewhat different in terms of their algorithmic details, almost all attain the same benefit in terms of profit. Processes with slow dynamics, frequent disturbances and high uncertainty in model parameters are not good candidates for the application of steady state RTO due to the infrequent execution of the RTO loop. This paper presents a comparative evaluation, with a discussion of the limitations and advantages, of current steady state online optimization techniques. The real time evolution (RTE) technique (Sequeira et al. 2004, Comput. Chem. Eng. 28(5):661–672), which is presented in the literature as an alternative to conventional steady state RTO, has also been explored. RTE results were compared with a standard RTO strategy and model predictive control (MPC). The results with a revised MPC design show that an improvement in operational profit can be made by careful selection of the control strategy and that fast tracking of set points might be infeasible in some scenarios.