The performance of model-based control and optimization depends on the accuracy of process models. However, changes in physiochemical and operational conditions can result in a mismatch between the process and its model. This model-plant mismatch (MPM) must be detected and rectified quickly to achieve the desired performance. In this work, we consider model-plant mismatch due to structural and parametric changes in the underlying process model of reactor systems. We formulate MPM detection as a fault detection and identification problem. We propose an online model-plant mismatch detection and model re-identification framework using the concept of the reaction extents and incremental model identification for detecting and isolating the faults and appropriately re-identifying the faulty part of the model. The proposed approach is illustrated through simulation studies of acetoacetylation of pyrrole in a batch, semi-batch and continuous stirred tank reactor configuration for different fault scenarios. (C) 2018 Elsevier Ltd. All rights reserved.