In the present work, a control Lyapunov-barrier function (CLBF)-based economic model predictive control (EMPC) system is designed to optimize process economics, and ensure stability and operational safety simultaneously based on a prediction model using an ensemble of recurrent neural network (RNN) models. As accurate first-principles models are not available for many industrial processes, RNN models are utilized in this work to approximate the dynamics of a general class of nonlinear systems in an operating region. The ensemble of RNN models are incorporated in the design of CLBF-EMPC, under which guaranteed closed loop stability and process operational safety are achieved for the nonlinear systems with two types of unsafe regions, i.e., bounded and unbounded sets. The application of the proposed RNN-based CLBF-EMPC method is demonstrated through a chemical process example with the case studies of a bounded and an of unbounded unsafe region, respectively. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.