Fault signatures and bias progression in dissolved oxygen sensors

Abstract

Biofilm fouling is known to impact the data quality of sensors, but little is known about the exact effects. We studied the effects of artificial and real biofilm fouling on dissolved oxygen (DO) sensors in full-scale water resource recovery facilities, and how this can automatically be detected. Biofilm fouling resulted in different drift direction and bias magnitudes for optical (OPT) and electrochemical (MEC) DO sensors. The OPT-sensor was more affected by biofilm fouling compared to the MEC-sensor, especially during summer conditions. A bias of 1 mg/L was detected by analysing the impulse response (IR) of the automatic air cleaning system in the DO sensor. The IR is an effect of a temporal increase in DO concentration during the automatic air cleaning. The IRs received distinct pattern changes that were matched with faults including: biofilm fouling, disturbances in the air supply to the cleaning system, and damaged sensor membrane, which can be used for fault diagnosis. The results highlight the importance of a condition-based sensor maintenance schedule in contrast to fixed cleaning intervals. Further, the results stress the importance of understanding and detecting bias due to biofilm fouling, in order to maintain a robust and resource-efficient process control.

System characterization of dynamic biological cultivations through improved data analysis

“…Generally, the model predictions improve when additional independent measurements are available (42). One on-line measurement that is often available in aerobic bioreactors, but was not used in this model, is the dissolved oxygen (DO) concentration, which yields fast-responding continuous measurements, but suffers from signal drift, nonlinear probe dynamics, and loss in linearity due to fouling (43). de Jonge et al (19) encountered DO related issues where, even in their experiments that lasted a couple of hours and involved rigorous calibrations, the DO probe data could not be used to improve the model performance.…”

Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network

“…Faults in DO, pH and MLSS sensors connected to a treatment plant mostly occur due to fouling. Fouling causes drift and bias faults in the sensors [25]. To get a more practical insight of the sensor faults, bias is introduced in the no fault dataset as [4] where X f is the faulty sensor data, X is the data acquired from the sensor in no fault condition, is a constant offset value.…”

Outlining Process Monitoring and Fault Detection in a Wastewater Treatment and Reuse System

“…The types of sensors that are commonly used vary greatly. Temperature can be measured with thermistors, liquid level with floats, differential pressure transducers, capacitance measurements, and ultrasonic level detection, flow rates are measured with electromagnetic sensors or rotameters depending on the state of the stream, pH with glass electrodes, biomass and suspended solids with optical measurements or ultrasound, [10] and DO with membrane electrochemical or optical fluorescent techniques [16].…”

“…However, contamination and biofouling are of particular concern in the WWT process, with conductivity sensors, suspended solids probes, fluorosensors, and dissolved oxygen (DO) probes being particularly susceptible to biofouling [10]. Samuelsson et al [16] demonstrated that unless sensors are manually cleaned or faults are well detected it is most probable that the DO probes will report incorrect values due to the biofouling.…”