Data-based fault-tolerant model predictive controller an application to a complex dearomatization process

The tightening global competition during the last few decades has been the driving force for the optimisation of industrial plant operations through the use of advanced control methods, such as model predictive control (MPC). As the occurrence of faults in the process measurements and actuators has become more common due to the increase in the complexity of the control systems, the need for fault-tolerant control (FTC) to prevent the degradation of the controller performance, and therefore the better optimisation of the plant operations, has increased. Traditionally, the most actively studied fault detection and diagnosis (FDD) components of the FTC strategies have been based on model-based approaches. In the modern process industries, however, there is a need for the data-based FDD components due to the complexity and limited availability of mechanistic models. Recently, active FTC strategies using fault accommodation and controller reconfiguration have become popular due to the increased computation capacity, easier adaptability and lower overall implementation costs of the active FTC strategies. The main focus of this thesis is on the development of an active data-based fault-tolerant MPC (FTMPC) for an industrial dearomatization process. Three different parallel-running FTC strategies are developed that utilise the data-based FDD methods and the fault accommodation- and controller reconfiguration-based FTC methods. The performances of three data-based FDD methods are first compared within an acknowledged testing environment. Based on the preliminary performance testing, the best FDD method is selected for the final FTMPC. Next, the performance of the FTMPC is validated with the simulation model of the industrial dearomatization process and finally, the profitability of the FTMPC is evaluated based on the results of the evaluation. According to the testing, the FTMPC performs efficiently and detects and prevents the effects of the most common faults in the analyser, flow and temperature measurements, and the controller actuators. The reliability of the MPC is increased and the profitability of the dearomatization process is enhanced due to the lower off-spec production.