| 1. |
- Ivan, Heidi Lynn, 1996-, et al.
(författare)
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Exploring the effects of faults on the performance of a biological wastewater treatment process
- 2024
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Ingår i: Water Science and Technology. - : IWA Publishing. - 0273-1223 .- 1996-9732.
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Tidskriftsartikel (refereegranskat)abstract
- To prioritise which faults should be detected in a biological wastewater treatment process, and with what level of urgency, it is necessary to understand the effect that they have on the process. Using the Benchmark Simulation Model No. 1 and 2. (BSM1 and BSM2), several process and sensor faults were considered and their impacts on various cost, quality, and controller performance evaluation metrics analysed. Both the cost of treating the wastewater and the quality of the effluent were impacted in varying degrees of severity by the faults tested. The most influential faults in both models were decreases to autotrophic and heterotrophic growth rates, decreases to the heterotrophic death rate, and the inhabitation fault. It was shown that only larger fault sizes were significant, and the required speed of detection is dependent on the fault profile. Prioritising detection of the most influential faults was shown to have significant effects on monitoring requirements for fault detection and the subsequent complexity required of a fault detection system. A valuable takeaway was the similarity of results from BSM1 and BSM2; the consistency of the influential process faults suggests that systems that can be described by these models are likely affected by the same faults.
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| 2. |
- Ivan, Heidi Lynn, 1996-
(författare)
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Fault Detection in Wastewater Treatment : Process Supervision to Improve Wastewater Reuse
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As wastewater treatment plants transition to water resource recovery facilities, the need for improved control and consequently supervision increases. Despite the large volume of research that has been performed on this topic, the use in industry is scarce. Practical implementation is challenging due to the nature of the process, and a lack of standardisation in the research results in uncertainty as to the state of the art. This is one of the main challenges identified. Experimental work is performed using the Benchmark Simulation Model No. 1 to identify monitoring requirements and evaluate the performance of univariate fault detection methods. For the former, residual based process fault signatures are used to determine minimal sensor requirements based on detectability and isolability goals. Sensor faults are the focus of the latter issue, using the Shewhart, cumulative sum, and exponentially weighted moving average control charts to detect bias and drift faults in a controlled variable sensor. The use of a standard model and known fault detection methods is useful to establish a baseline for future work. Given the lack of standardised use in industry this is considered critical. Both proposed methods emphasise ease of visualisation which is beneficial for industrial implementation.
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| 3. |
- Marais, Heidi Lynn, 1996-, et al.
(författare)
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Detectability of Fault Signatures in a Wastewater Treatment Process
- 2021
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Ingår i: Proceedings of The First SIMS EUROSIM Conference on Modelling and Simulation, SIMS EUROSIM 2021, and 62nd International Conference of Scandinavian Simulation Society, SIMS 2021. - : Scandinavian Simulation Society and Linköping University Electronic Press. - 9789179292195 ; , s. 418-423
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Konferensbidrag (refereegranskat)abstract
- In a wastewater treatment plant reliable fault detection is an integral component of process supervision and ensuring safe operation of the process. Detecting and isolating process faults requires that sensors in the process can be used to uniquely identify such faults. However, sensors in the wastewater treatment process operate in hostile environments and often require expensive equipment and maintenance. This work addresses this problem by identifying a minimal set of sensors which can detect and isolate these faults in the Benchmark Simulation Model No. 1.Residual-based fault signatures are used to determine this sensor set using a graph-based approach; these fault signatures can be used in future work developing fault detection methods. It is recommended that further work investigate what sizes of faults are critical to detect based on their potential effects on the process, as well as ways to select an optimal sensor set from multiple valid configurations.
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| 4. |
- Moretti, A., et al.
(författare)
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A review of the state-of-the-art wastewater quality characterization and measurement technologies. Is the shift to real-time monitoring nowadays feasible?
- 2024
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Ingår i: Journal of Water Process Engineering. - : Elsevier Ltd. - 2214-7144. ; 60
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Tidskriftsartikel (refereegranskat)abstract
- Efficient characterization of wastewater stream quality is vital to ensure the safe discharge or reuse of treated wastewater (WW). There are numerous parameters employed to characterize water quality, some required by directives (e.g. biological oxygen demand (BOD), total nitrogen (TN), total phosphates (TP)), while others used for process controls (e.g. flow, temperature, pH). Well-accepted methods to assess these parameters have traditionally been laboratory-based, taking place either off-line or at-line, and presenting a significant delay between sampling and result. Alternative characterization methods can run in-line or on-line, generally being more cost-effective. Unfortunately, these methods are often not accepted when providing information to regulatory bodies. The current review aims to describe available laboratory-based approaches and compare them with innovative real-time (RT) solutions. Transitioning from laboratory-based to RT measurements means obtaining valuable process data, avoiding time delays, and the possibility to optimize the (WW) treatment management. A variety of sensor categories are examined to illustrate a general framework in which RT applications can replace longer conventional processes, with an eye toward potential drawbacks. A significant enhancement in the RT measurements can be achieved through the employment of advanced soft-sensing techniques and the Internet of Things (IoT), coupled with machine learning (ML) and artificial intelligence (AI).
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