The paper presents a novel model calibration and validation strategy of membrane bioreactor (MBR) for wastewater treatment. The approach is based on a dynamic model of the activated sludge process and it consists simultaneously on estimating the model's parameters and computing the dissolved oxygen control input. Activated sludge model No. 1 (ASM1) has been widely used to describe the biological process of activated sludge processes. However, most system states and parameters within ASM1 are not easily obtained, hence not applicable for model calibration and validation. In this work, a reduced-order model presented herein serves as a tool for predicting the dynamic behavior of the MBR plant. The model contains only 4 measurable states, where 13 parameters need to be identified. To reduce the complexity of the calibration, the static sensitivity analysis is performed to select the sensitive parameters. The selected parameters are identified based on directly measurable real-time data obtained from the plant. In addition, the dissolved oxygen is also maintained at a certain level to mimic the real-time control behavior. Model calibration is achieved based on a sliding window optimization problem, which searches for the optimal parameters set and control variables during each identification cycle. Different datasets sampled for the same MBR plant have been used for model validation. Both calibration and validation results are evaluated by several performance indexes, which indicates an acceptable correspondence with the experimental data. The developed model can be employed for process state estimation and control purpose as well as design issues for MBR systems.