Dedicated Observers for Sensors Fault Detection and Diagnosis in Real Time for Bioreactors

Due to the increasing demand for greater safety and ease of scale bioprocessing, fault detection and diagnosis (FDD) is becoming an effective method to avoid breakdowns and disasters. Therefore, this work focuses on developing a dedicated observer-based fault diagnosis for nonlinear systems. To solve this, the FDD scheme is needed to make it perform satisfactorily even in a faulty situation. A case study on bioethanol production is proposed to illustrate and demonstrate the proposed techniques in real time. Single faults and different sensor faults are considered. The effectiveness of the proposed model is proved by comparing its performance obtained by simulation with the experimental data. In order to supervise the change of the possible faulty parameter, robust adaptive full-order observers that focus not only on the state estimation but also on the parameter change are applied to the considered bioreactor. In order to achieve the desired outcome of sensor fault detection, we propose a residual evaluation function, given by the root-mean-square (RMS) value of the residual and a practical threshold for the bioreactor. Experimental results show that sensor faults can be well diagnosed by the proposed observer-based FDD method. The precision, recall rate, and overall accuracy of three diagnostic metrics for abrupt failures were compared. The diagnostic approach was successful, achieving an overall accuracy rate of over 90% for each of the three abrupt failure scenarios in every sensor. Finally, even if the biomass or CO2 sensors fail, the FDD system can reconstruct the substrate and ethanol dynamics that are typically quantified offline in bioprocesses in real time.