Generalized modeling of the sludge volume index and suspended solids to monitor activated sludge bioflocculation using image analysis

• Autorzy: Khan Muhammad Burhan , Nisar Humaira , Ng Choon Aun

Identyfikatory

DOI

10.37190/epe200302

• Języki publikacji: EN

Abstrakty

EN

The performance of an activated sludge wastewater treatment plant depends on bioflocculation that is monitored by physical measurements such as the sludge volume index (SVI) and mixed liquor suspended solids (MLSS). The estimation of SVI and MLSS has been proposed using image analysis based modeling which is time-efficient and valid for multiple plants operating in different states. The methodology includes the sequence of image acquisition using bright-field microscopy, a robust segmentation of flocs, partitioning of flocs based on different ranges of their equivalent diameters, extraction of morphological features, and modeling of SVI and MLSS using the features. It is proposed that bright-field microscopy at lower magnification to capture the flocs is sufficient to model SVI and MLSS. A robust approach for image segmentation is adopted by integrating state-of-the-art image segmentation algorithms. It is hypothesized that flocs in different ranges of equivalent diameter respond differently to the variation in the operating state. Hence, flocs and their respective image analysis features are categorized based on the range of equivalent diameter. Finally, stepwise regression is used for feature selection and model identification to explore the feasibility of generalization of models to multiple plants in different states regarding SVI and MLSS.

Słowa kluczowe

EN

activated sludge; flocs; bioflocculation; image analysis

PL

osad czynny; kłaczki; bioflokulacja; analiza obrazu

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Environment Protection Engineering
• Rocznik: 2020
• Tom: Vol. 46, nr 3
• Strony: 17--37
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Khan Muhammad Burhan

• Department of Electronic Engineering, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
• Department of Electrical Engineering, National University of Computer and Emerging Sciences, Karachi, Pakistan

autor

Nisar Humaira

• Department of Electronic Engineering, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia

autor

Ng Choon Aun

• Department of Environmental Engineering, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia

Bibliografia

• [1] JENKINS D., RICHARD M.G., DAIGGER G.T., Manual on the causes and control of activated sludge bulking, foaming, and other solids separation problems, CRC Press, 2003.
• [2] BITTON G., Wastewater Microbiology, Wiley, New Jersey 2005.
• [3] KHAN M.B., NISAR H., NG C.A., LO P.K., YAP V.V., Generalized classification modeling of activated sludge process based on microscopic image analysis, Environ. Technol., 2018, 39 (1), 24–34.
• [4] KHAN M.B., NISAR H., NG C.A., LO P.K., Estimation of sludge volume index (SVI) using bright field activated sludge images, IEEE International Instrumentation and Measurement Technology Conference, Taipei, Taiwan, 2016, 407–411.
• [5] SIKORA M., SMOLKA B., Feature analysis of activated sludge based on microscopic images, Canadian Conference on Electrical and Computer Engineering, Toronto, Ontario, 2001, 2, 1309–1314.
• [6] JENNE R., CENENS C., GEERAERD A.H., IMPE J.F., Towards on-line quantification of flocs and filaments by image analysis, Biotechnol. Lett., 2002, 931–935.
• [7] HEINE W., SEKOULOV I., BURKHARDT H., BERGEN L., BEHRENDT J., Early warning system for operation failures in biological stages of WWTPs by online image analysis, Water Sci. Technol., 2002, 46 (4–5), 117–124.
• [8] PEREZ Y.G., LEITE S.G.F., COELHO M.A.Z., Activated sludge morphology characterisation through an image analysis procedure, Brazilian J. Chem. Eng., 2006, 319–330.
• [9] KHAN M.B., NISAR H., NG C.A., LO P.K., A vignetting correction algorithm for bright-field microscopic images of activated sludge, International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gpld Coast, QLD, Australia, 2016, 1–4.
• [10] FISHER R., PERKINS S., WALKER A., WOLFART E., Hypermedia Image Processing Reference (HIPR), John Wiley & Sons, 1996.
• [11] KHAN M.B., NISAR H., NG C.A., LO P.K., YAP V.V., Local adaptive approach toward segmentation of microscopic images of activated sludge flocs, J. Electron. Imaging, 2015, 24 (6), 061102-10.
• [12] MESQUITA D.P., AMARAL A.L., FERREIRA E.C., Estimation of effluent quality parameters from an activated sludge system using quantitative image analysis, Chem. Eng. J., 2016, 285, 349–357.
• [13] AMARAL A.L., MESQUITA D.P., FERREIRA E.C., Automatic identification of activated sludge disturbances and assessment of operational parameters, Chemosphere, 2013, 91 (5), 705–710.
• [14] BOZTOPRAK H., ÖZBAY Y., GÜÇLÜ D., KÜÇÜKHEMEK M., Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant, Desalin. Water Treat., 2015, 57 (37), 17195–17205.
• [15] TOMPERI J., KOIVURANTA E., KUOKKANEN A., JUUSO E., LEIVISKÄ K., Real-time optical monitoring of the wastewater treatment process, Environ. Technol., 2016, 37 (3), 344–351.
• [16] TOMPERI J., LEIVISKÄ K., Comparison of modeling accuracy with and without exploiting automated optical monitoring information in predicting the treated wastewater quality, Environ. Technol., 2018, 39 (11),1442–1449.
• [17] HONGGUI H., YING L., JUNFEI Q., A fuzzy neural network approach for online fault detection in wastewater treatment process, Comput. Electr. Eng., 2014, 40 (7), 2216–2226.
• [18] TOET S., LOGTESTIJN R.S.P., KAMPF R., SCHREIJER M., VERHOEVEN J.T.A., The effect of hydraulicretention time on the removal of pollutants from sewage treatment plant effluent in a surface-flowwetland system, Wetlands, 2005, 25 (2), 375–391.
• [19] KHAN M.B., NISAR H., NG C.A., LO P.K., Illumination compensated segmentation of microscopicimages of activated sludge flocs, International Conference on Digital Image Computing: Techniquesand Applications (DICTA), Adelaide, SA, Australia, 2015, 1–5.
• [20] RUSS J.C., The image processing handbook, CRC Press, Boca Raton, FL, 2011.
• [21] OBERT M., Numerical estimates of the fractal dimension D and the lacunarity L by the mass radius relation, Fractals, 1993, 1 (3), 711–721.
• [22] DRAPER N.R., SMITH H., Applied Regression Analysis, John Wiley & Sons, 1998.