Global optimality of dynamic operations ensures maximum economic benefits over time. We introduce Dynamic Real Time Optimization (D-RTO) method which uses the same model for D-RTO and for Nonlinear Model Predictive Controller (NMPC), thereby eliminating the model mismatch between the D-RTO and the NMPC. An integration framework based on two different predictive horizons for time-scale decomposition is proposed. Fast updates and reoptimization of the dynamic trajectories ensure that the dynamic operation is optimal over time. Proposed global optimization algorithm is a variation of normalized multi parametric disaggregation (NMDT) where NMDT is modified by the use of a bivariate partitioning to solve the D-RTO problem. Elimination of the mismatch between D-RTO and NMPC models significantly reduces the effort required to eliminate the model errors between D-RTO, NMPC and the plant. Three chemical process examples are included to illustrate the proposed framework. (C) 2017 Elsevier Ltd. All rights reserved.