Changes in process parameters and measured/unmeasured disturbances shift the optimal point at which the economic benefits are maximized. Combinations of real time optimization (RTO) techniques, which periodically determine the economically optimum operating point using steady state optimization, and model predictive control (MPC) have been widely employed in the process industry for operating process plants optimally in the face of drifting disturbances and/or parameters. Due to the long wait time between two successive RTO invocations and model inconsistency between the RTO and MPC layers, the conventional RTO schemes can end up operating the plant suboptimally if the process parameters/unmeasured disturbances change significantly during the wait time. Recently proposed frequent RTO approaches attempt to address this difficulty by increasing the frequency of RTO invocation. An online update of the steady state model employed by the RTO layer using dynamic operating data is a major concern in implementation of frequent RTO. Use of a dynamic model based state and parameter estimation for maintenance of the model used in frequent RTO has not received much attention in the literature. In this work, it is proposed to develop a novel integrated frequent RTO and adaptive nonlinear MPC (NMPC) approach for operating a unit operation in a economically optimal manner. A dynamic mechanistic model based simultaneous state and parameter estimation scheme is used as a common link between the RTO and the NMPC components. Estimates of the drifting unmeasured disturbances/parameters generated by the state and parameter estimator are used to update the steady state model used for frequent RTO and the dynamic model used for predictions in NMPC. Use of a single model for carrying out RTO as well as control tasks eliminates difficulties that arise due to the mismatch between models used for RTO and control. The efficacy of the proposed integrated optimizing control scheme is demonstrated by conducting simulation studies on benchmark systems from the literature. Analysis of simulation results reveals that the proposed integrated online optimizing control scheme maintains these systems at their respective economically optimal operating points in the presence of drifting disturbances/parameters.