We propose an adaptive algorithm for constructing reduced-order models (ROMs) of nonlinear systems based on proper orthogonal decomposition (POD) combined with the discrete empirical interpolation method (DEIM). Using an efficient output error estimation, the reduced basis and the DEIM interpolation basis are adaptively adjusted to derive a small, yet accurate ROM. The adaptive algorithm is further explored for a population balance system of a crystallization process. Simulation results show that much smaller and reliable ROMs can be adaptively obtained using the algorithm with ignorable extra computational load as compared with the standard POD-DEIM method. (C) 2017 American Institute of Chemical Engineers