In this paper, we propose a conceptual framework for nonlinear systems to integrate real-time optimization (RTO) and model predictive control (MPC) under time-varying economic environments. In the RTO layer, we introduce a lookup table including a large number of steady-state points of the nonlinear system, which are predetermined offline. Once the parameters of the economic cost function are varied, we are able to take a quick online search on the lookup table to find a point for satisfied economic performance and then send it to the MPC layer as a temporary control target. The temporary target is also employed as the initial solution for solving the optimization of the RTO layer. When the optimal target is calculated by RTO, it will replace the temporary one as the new control target of MPC. Compared to the two-layer framework which suffers from long waiting time to get the optimal operating points, the lookup-table-based RTO (LT-RTO) framework provides a quick-produced suboptimal target for MPC. It avoids unnecessary economic losses if MPC is still tracking outdated target even parameters of the cost function have already changed. We demonstrate the effectiveness through a chemical process model that the LT-RTO framework makes an improvement of the economic performance. Published by Elsevier Ltd.