Soft Sensing for On-Line Fault Detection of Ammonium Sensors in Water Resource Recovery Facilities

Abstract

The increasing demand for online sensors applied to advanced control strategies in water resource recovery facilities has resulted in the increasing investigation of fault-detection methods to improve the reliability of sensors installed in harsh environments. The study herein focuses on the fault detection of ammonium sensors, especially for effluent monitoring, given their potential in ammonium-based aeration control applications. An artificial neural network model was built to predict the ammonium content in the effluent by employing the information from five other sensors installed in the activated sludge tank: NH4 +, pH, ORP, DO, and TSS. The residual between the model prediction and the effluent ammonium sensor signal was utilized in a fault-detection mechanism based on principal component analysis and Shewhart monitoring charts. In contrast to previous studies, the present work utilizes typical faults collected from a 1 year historic dataset of an actual sensor setup. Treatment process anomalies, calibration bias faults, and fouling drifts were the most common issues identified from the historic dataset, and they were promptly identified by the proposed fault-detection methodology. Once a fault is detected, the model prediction can be actively used in place of the sensor for process control without affecting the treatment process by utilizing faulty datasets.

Integrating Virus Monitoring Strategies for Safe Non-Potable Water Reuse

“…An early application of ANNs in wastewater treatment demonstrated the superiority of neural networks compared to conventional kinetic models of microbial inactivation during disinfection [145]. In the past quarter-century, there was an increase in the application of ANN to a myriad of contexts, including wastewater process control [146,147], constituent monitoring [148], treatment performance [149,150], and virus disinfection [151] or removal [152] to deal with scaling challenges associated with multi-dimensional data. Yet, applications of such data-driven models to assess viral risk are lacking.…”

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

“…The various FD methods for heating/cooling systems are reviewed in [14]. In [15], by the use of historical data, the outputs of sensors are predicted, and by comparison with the measured data, the fault is detected. The frequency estimation approach is developed in [16] for an FD problem.…”

Data-Driven Drift Detection in Real Process Tanks: Bridging the Gap between Academia and Practice

“…The data were classified according to faulty NH 4 data by an expert and a Long Short-Term Memory Network was developed and outperformed PCA-SVM. Cecconi and Rosso [23] used ANN to predict the NH 4 concentration and used PCA along with Shewhart monitoring charts for detection of the variation between measured values and predicted values. This study was based on more than one year of data from a real plant.…”

“…For testing, three types of faults were introduced in real data. The suggested approach was able to detect the faults and the ANN prediction could be used for process control when a fault was detected [23]. Anter et al [24] used fuzzy swarm intelligence and chaos theory to detect faults in a real data set from 1993 available at the UCI Machine Learning Repository [24]; however, details on the fault types detected are not described.…”

“…Except for Cecconi and Rosso [23] and Mamandipoor and Majd [22], none of the solutions proposed in 2020-2021 reflect contemporary conditions met at WWTPs, and while several papers acknowledge that there is a gap between the solutions in academia and in the real world [4], there is a lack of knowledge when it comes to implementing data-driven approaches in real WWTPs.…”