In this paper, the on-line optimization of batch reactors under parametric uncertainty is considered. A method is presented that estimates the likely economic performance of the on-line optimizer. The method of orthogonal collocation is employed to convert the differential algebraic optimization problem (DAOP) of the dynamic optimization into a nonlinear program (NLP) and determine the nominal optimum. Based on the resulting NLP, the optimization steps are approximated by neighbouring extremal problems and the average deviation from the true process optimum is determined dependent on the measurement error and the parametric uncertainty. A back off from the active path and endpoint constraints is determined at each optimization step which ensures the feasible operation of the process. The method of the average deviation from optimum is developed for time optimal problems. The theory is demonstrated on an example.