| 1. |
- Marais, Heidi Lynn, 1996-, et al.
(författare)
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Comparing statistical process control charts for fault detection in wastewater treatment
- 2022
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Ingår i: Water Science and Technology. - : IWA Publishing. - 0273-1223 .- 1996-9732. ; 85:4, s. 1250-1262
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Tidskriftsartikel (refereegranskat)abstract
- 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 setpoint tracking hides faults on controlled variables; lower missed detection rates are observed using manipulated variables.
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| 2. |
- Zlatkovikj, Milan, et al.
(författare)
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Development of feed-forward model predictive control for applications in biomass bubbling fluidized bed boilers
- 2022
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Ingår i: Journal of Process Control. - : ELSEVIER SCI LTD. - 0959-1524 .- 1873-2771. ; 115, s. 167-180
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Tidskriftsartikel (refereegranskat)abstract
- In order to accommodate more intermittent renewable energy sources, biomass fueled combined heat and power plants (bio-CHPs) can contribute towards sustainable and flexible energy systems. However, the varying properties of biomass, such as moisture contents and heating values, can clearly affect the combustion in boilers, which further affects the flexibility provided by bio-CHPs. In order to achieve better control, this paper proposes a feed-forward model predictive controller (FF MPC) to handle the variation of biomass properties. A dynamic model was built in Dymola to simulate the performance of a bubbling fluidized bed boiler, which was validated against the real operation data. Based on the simulation, the key manipulated variables were optimized for the given controlled variables. The advantages of the proposed FF MPC were demonstrated through comparisons with proportional- integral (PI), FF PI and MPC. The results of FF MPC show the best performance, such as the lowest magnitude of fluctuations for 3 outputs (thermal load, steam and fluidized bed temperature), and the most stable operation. Consequently, FF MPC can potentially increase the electricity generation and further lead to an economic benefit. Using one week in winter as an example, compared to PI, FF PI and MPC, FF MPC can generate more electricity and improve revenues by 14.77 MWh/590 =C, 4.1 MWh/164 =C and 5.03 MWh/211.2 =C respectively.
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