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

## TL;DR

This paper presents a real-time fault detection and diagnosis method for bioreactors using dedicated observers to improve safety and efficiency in bioprocessing.

## Contribution

The novelty lies in the use of robust adaptive full-order observers for real-time sensor fault diagnosis in nonlinear bioreactor systems.

## Key findings

- The proposed method achieved over 90% overall accuracy in diagnosing abrupt sensor failures.
- The FDD system can reconstruct substrate and ethanol dynamics in real time even when key sensors fail.
- Experimental validation confirmed the effectiveness of the observer-based approach compared to simulation data.

## Abstract

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.