In industry, it may be difficult in many applications to obtain a first-principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state-space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error-triggered on-line model identification approach is introduced for closed-loop systems under model-based feedback control strategies. The linear models are re-identified on-line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re-identification on-line when necessary. The proposed approach is demonstrated through two chemical process examples using a model-based feedback control strategy termed Lyapunov-based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error-triggered on-line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed-loop stability of the process under LEMPC. (c) 2016 American Institute of Chemical Engineers AIChE J, 63: 949-966, 2017