It is widely recognized that the life-time performance of current model-based operation support systems, like Model Predictive Control (MPC), Real-Time Optimization (RTO) and soft-sensors for large-scale complex dynamic processes is rather limited, particularly due to the fact that the underlying dynamic models need to be adapted/calibrated regularly, requiring dedicated measurement campaigns executed by highly specialized engineers.  In this project, we aimed to develop technology in order to bring the current model-based operation support technology  to a higher level of autonomy. Such a technology requires developments in experiment design, performance monitoring and diagnosis, closed loop identification, autotuning so that it can optimize plant performance under varying operational conditions and adapting to changing circumstances.