Dissolved oxygen control and simultaneous estimation of oxygen uptake rate in activated‐sludge plants

Suescun, J.; Irizar, I.; Ostolaza, J.X.; Ayesa, E.

doi:10.2175/106143098x124948

Cited by 33 publications

(15 citation statements)

References 6 publications

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“…For the particular case of on/off DO control strategies (see Figure 1), it can be easily deduced that, when aeration is OUR estimation, linear regression methods are also valid to estimate K L a; however in this case they must be combined with iterative procedures (Suescun et al 1998).…”

Section: Description Of the Identification Algorithms: Theoretical Basismentioning

confidence: 99%

Online monitoring of OUR, KLa and OTE indicators: practical implementation in full-scale industrial WWTPs

Irizar

Zambrano

Montoya³

et al. 2009

Water Science and Technology

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Based on on/off aeration strategies, this paper describes all the steps involved in the development and implementation of three identification algorithms aimed at monitoring the oxygen uptake rate (OUR), the oxygen mass-transfer coefficient (K(L)a), and oxygen transfer efficiency (OTE) in aerated biological reactors. Firstly, a detailed explanation of the theoretical background behind every algorithm is given. In addition, practical issues have also been taken into account in order to guarantee the quality of estimations. Finally, the three algorithms have been implemented and validated in a full-scale industrial wastewater treatment plant with satisfactory results. Although short-term noise has been observed in the estimated data (especially at high OURs), the medium and long-term data trajectories have been correctly reproduced.

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Section: Description Of the Identification Algorithms: Theoretical Basismentioning

confidence: 99%

Online monitoring of OUR, KLa and OTE indicators: practical implementation in full-scale industrial WWTPs

Irizar

Zambrano

Montoya³

et al. 2009

Water Science and Technology

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“…Significant increase of control quality is usually achieved through application of model based control. Such approach requires good quality models and therefore involves precise identification of models' parameters [12,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Probe dynamics influence on determination of volumetric oxygen transfer coefficient

Niedzwiedz

Laszczyk

2014

2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)

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This paper deals with practical aspects of volumetric oxygen transfer coefficient determination. The attention was paid to influence of dissolved oxygen probe dynamics on measurements. A new identification method of volumetric oxygen transfer coefficient was developed. This method involves numerical integral calculation. The new method and three other methods (graphical, logarithmic and regression) of determining volumetric oxygen transfer coefficient value were applied. The comparative simulation research was done using dissolved oxygen concentration model. All four methods were finally aplied to real biotechnological wastewater process with the activated sludge.

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“…By introducing this feature, the SSPA has taken into account control Nevertheless, in these methods the quality of estimations is very sensitive to inherent noise and delays from industrial DO probes as well as process non-linearities. Thus, the processing of the DO signal (sampling, filtering, estimation of derivatives, correction of delays, etc) becomes decisive on the reliability of OUR and K L a measurements (Suescun et al 1998). All these considerations are brought together to develop a robust prototype for the continuous monitoring of OUR and K L a that is detailed in Ayesa & Salterain (2002) and whose performance is successfully verified in two Spanish WWTPs treating wastewater from a food industry (Pepsico S.A.) and a paper industry (Zubialde S.A.).…”

Section: Introductionmentioning

confidence: 99%

“…The DO f data vector has been sized to define a two minutes moving time-window with the last 60 measurements. A case-specific module corrects the effect of sensor delays to reconstruct the real dynamics of the estimated dissolved oxygen (DO e ) (Suescun et al 1998).…”

Section: Introductionmentioning

confidence: 99%

Standard signal processing using enriched sensor information for WWTP monitoring and control

Irizar

Alferes²,

Larrea³

et al. 2008

Water Science and Technology

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Important indicators for monitoring and control of wastewater treatment plants (WWTP) often have to be obtained from the processing of on-line signal trajectories. Therefore, the quality of sensor instantaneous measurements can be improved significantly if they are complemented with valuable information about the geometric features of their trajectories. The present paper describes the design and implementation of a Standard Signal Processing Architecture (SSPA) from which enriched sensor information is generated automatically. The SSPA has been made up of three complementary modules: the pre-processing module, the storage module and the post-processing module. Moreover, the SSPA has been parameterised so as to allow its adaptation to the specifications of every signal. By performing basic calculations on pre-processed signal trajectories, the storage module produces enriched vectors which collect information of the first and second time derivatives, average and variance values, peak values, linear regression parameters, curvature, etc. Then, the enriched information vectors can be exploited to implement customised monitoring and control tools. In this respect, the effectiveness of the SSPA has been demonstrated in three different practical cases: (1) OUR and KLa identification algorithms; (2) processing of measurements for real-time controllers; and, (3) detection of bend-points in on-line signals of SBR processes.

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Dissolved oxygen control and simultaneous estimation of oxygen uptake rate in activated‐sludge plants

Cited by 33 publications

References 6 publications

Online monitoring of OUR, KLa and OTE indicators: practical implementation in full-scale industrial WWTPs

Online monitoring of OUR, KLa and OTE indicators: practical implementation in full-scale industrial WWTPs

Probe dynamics influence on determination of volumetric oxygen transfer coefficient

Standard signal processing using enriched sensor information for WWTP monitoring and control

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