This work focuses on the design of stochastic Lyapunov-based economic model predictive control (SLEMPC) systems for a broad class of stochastic nonlinear systems with input constraints. Under the assumption of stabilizability of the origin of the stochastic nonlinear system via a stochastic Lyapunov-based control law, an economic model predictive controller is proposed that utilizes suitable constraints based on the stochastic Lyapunov-based controller to ensure economic optimality, feasibility and stability in probability in a well-characterized region of the state-space surrounding the origin. A chemical process example is used to illustrate the application of the approach and demonstrate its economic benefits with respect to an EMPC scheme that treats the disturbances in a deterministic, bounded manner. (c) 2018 American Institute of Chemical Engineers AIChE J, 64: 3312-3322, 2018