Advanced Monitoring and Supervision of Biological Treatment of Complex Dairy Effluents in a Full-Scale Plant

Carrasco, Eugenio F.; Omil, F.; Garrido, Juan Manuel Méndez; Arrojo, B.; Méndez, R.

doi:10.1021/bp034211u

Cited by 9 publications

(1 citation statement)

References 18 publications

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“…In such situations, the operator may not understand the process sufficiently to maintain stable operation. If properly treated, however, this process data can provide a wealth of information that can assist plant operators in understanding the process status and enable them to take appropriate action when an abnormality is detected (Carrasco et al, 2004;Vanrolleghem, 2003, Rosen andSaarinen et al, 2003;Xiao and Luong, 2003;Yoo et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Multi‐model statistical process monitoring and diagnosis of a sequencing batch reactor

Yoo

Villez

Lee

et al. 2006

Biotech & Bioengineering

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Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.

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