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This paper deals with the problem of optimizing the performance of a process using Real Time Optimization (RTO) considering the unavoidable errors in the process models. It implements a new architecture within the modifier-adaptation methodology, presenting a nested optimization problem with two layers. With this methodology, it is possible to find a point that satisfies the KKT conditions of a process using an inaccurate model in the optimization, without the need to estimate directly the experimental gradients of the process. The suggested methodology has been tested in a continuous bioreactor example that present a washout closer to the real optimum of the simulated process. The results show that the proposed methodology is able to find the optimum of the process smoothly, avoiding unstable operating points.