Knowledge of the state of an industrial chemical process is a key-point for process control. Indeed, if hazards or operating changes occur, it is necessary to be able to detect their presence, their nature and possibly their amplitudes in order to maintain the process as close as possible to the target state. However, the uncertainties and errors affecting the collected measurements often make it difficult to estimate the process state. Specific treatment of the measured data is necessary to determine a consistent data set.

The proposed thesis consists in methodological developments aiming to extend existing results in order to take into account the dynamic regime of the process and its non-linear operation for its diagnosis. Two complementary aspects are to be developed, the first related to the data validation and the second dedicated to the detection and analysis of operating changes of a system.

The final tool would be able to follow the process at all times and able to detect any operational changes, like a real “digital twin” of the process. Such a tool would open up very interesting innovative perspectives for process control, as the state of the process would be known precisely at all times.

The candidate must hold a Bac+5 degree in chemical engineering, automatic control or mathematics (statistics). This thesis will enable him to acquire skills in these three areas, which will be greatly appreciated by companies, in particular to develop advanced monitoring and control tools.