Data-driven techniques for fault detection in anaerobic digestion process

Kazemi, Pezhman; Bengoa, Christophe; Steyer, Jean‐Philippe; Giralt, Jaume

doi:10.1016/j.psep.2020.12.016

Cited by 65 publications

(23 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sánchez-Fernández et al (2018) considered dissolved oxygen sensor faults in the Benchmark Simulation Model No.2 (BSM2) and found that the univariate EWMA chart on residuals performed as well, or better, than multivariate monitoring using principal component analysis (PCA) with T 2 and Q statistics. Similarly, Kazemi et al (2021) reached the same conclusion but for the univariate CUSUM chart when monitoring residuals; they tested a process fault, as well as bias and drift faults on a pH sensor, and showed that the CUSUM chart outperformed the multivariate monitoring for small fault sizes, and in the cases with missing data. Finally, Riss et al (2021) used a modified CUSUM chart, based on the median, and combined it with the random forest method to successfully perform fault detection on real data from a WWTP.…”

Section: Introductionmentioning

confidence: 60%

Comparing statistical process control charts for fault detection in wastewater treatment

Marais

Zaccaria

Odlare

2022

Water Science and Technology

View full text Add to dashboard Cite

Fault detection is an important part of process supervision, especially in processes where there are strict requirements on the process outputs like in wastewater treatment. Statistical control charts such as Shewhart charts, cumulative sum (CUSUM) charts, and exponentially weighted moving average (EWMA) charts are common univariate fault detection methods. These methods have different strengths and weaknesses that are dependent on the characteristics of the fault. To account for this the methods in their base forms were tested with drift and bias sensor faults of different sizes to determine the overall performance of each method. Additionally, the faults were detected using two different sensors in the system to see how the presence of active process control influenced fault detectability. The EWMA method performed best for both fault types, specifically the drift faults, with a low false alarm rate and good detection time in comparison to the other methods. It was shown that decreasing the detection time can effectively reduce excess energy consumption caused by sensor faults. Additionally, it was shown that monitoring a manipulated variable has advantages over monitoring a controlled variable as set-point tracking hides faults on controlled variables; lower missed detection rates are observed using manipulated variables.

show abstract

Section: Introductionmentioning

confidence: 60%

Comparing statistical process control charts for fault detection in wastewater treatment

Marais

Zaccaria

Odlare

2022

Water Science and Technology

View full text Add to dashboard Cite

show abstract

“…The development of an appropriate mathematical model for the AD process has been the focus of much research due to its critical role in improving laboratory studies, more effective process performance through automated process control, and optimization of AD system design and control strategies [90]. In contrast, the AD process is a highly structured, complex, dynamic, non-linear system, making it difficult to model [91]. However, in 2002, the most predominant, highly structured, and generic model which described the overall AD process was established by the International Water Association (IWA) task group, i.e., ADM1.…”

Section: Mathematical Modeling Of the Ad Systemmentioning

confidence: 99%

Recovery of Household Waste by Generation of Biogas as Energy and Compost as Bio-Fertilizer—A Review

et al. 2021

View full text Add to dashboard Cite

Nowadays, organic waste and especially household waste represents a significant global issue due to population growth. The anaerobic digestion (AD) process is an essential operation contributing powerfully to the valorization of organic waste including food waste in terms of renewable energy generation (biogas) and the rich-nutrient residue that can be utilized as bio-fertilizer. Thus, this process (AD) allows for good recovery of household waste by generating biogas and compost. However, the AD operation has been affected by several key factors. In this paper, we aim to involve different critical parameters influencing the AD process, including temperature, pH, organic loading rate (OLR), carbon to nitrogen ratio (C/N), and total solid content (TS(%)). Further, the paper highlights the inhibition caused by the excessive accumulation of volatile fatty acids (VFAs) and ammoniac, which exhibits the positive effects of co-digestion, pretreatment methods, and mixing techniques for maintaining process stability and enhancing biogas production. We analyze some current mathematical models explored in the literature, such as distinct generic, non-structural, combined, and kinetic first-order models. Finally, the study discusses challenges, provides some possible solutions, and a future perspective that promises to be a highly useful resource for researchers working in the field of household waste recovery for the generation of biogas.

show abstract

“…The method was evaluated on simulated dry weather data and the authors state that the method was superior to existing methods and can reduce operating costs and improve the monitoring of the influent [15]. Kazemi et al [16] showed that incremental PCA was able to distinguish between time varying events and faults in simulated data, while Kazemi et al [17] investigated a number of technics including Support Vector Machine, Ensemble Neural Network and Extreme Learning and found that they performed better than a PCA based method after testing on simulated data. Luca et al [18] applied PCA and statistic for fault detection in DO sensors in simulated data and stated that the method was successful in detecting the faults.…”

Section: Introductionmentioning

confidence: 99%

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

Hansen

Hansen²,

Moeslund

et al. 2022

Water

View full text Add to dashboard Cite

Sensor drift in Wastewater Treatment Plants (WWTPs) reduces the efficiency of the plants and needs to be handled. Several studies have investigated anomaly detection and fault detection in WWTPs. However, these solutions often remain as academic projects. In this study, the gap between academia and practice is investigated by applying suggested algorithms on real WWTP data. The results show that it is difficult to detect drift in the data to a sufficient level due to missing and imprecise logs, ad hoc changes in control settings, low data quality and the equality in the patterns of some fault types and optimal operation. The challenges related to data quality raise the question of whether the data-driven approach for drift detection is the best solution, as this requires a high-quality data set. Several recommendations are suggested for utilities that wish to bridge the gap between academia and practice regarding drift detection. These include storing data and select data parameters at resolutions which positively contribute to this purpose. Furthermore, the data should be accompanied by sufficient logging of factors affecting the patterns of the data, such as changes in control settings.

show abstract

Data-driven techniques for fault detection in anaerobic digestion process

Cited by 65 publications

References 33 publications

Comparing statistical process control charts for fault detection in wastewater treatment

Comparing statistical process control charts for fault detection in wastewater treatment

Recovery of Household Waste by Generation of Biogas as Energy and Compost as Bio-Fertilizer—A Review

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

Contact Info

Product

Resources

About