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HÜCRESEL SİNİR AĞLARI VE DALGACIK DÖNÜŞÜMÜ KULLANARAK FLOK VE FİLAMENTLERİN BÖLÜTLENMESİ

Yıl 2015, Cilt: 7 Sayı: 1, 38 - 49, 01.03.2015

Öz

Flok ve filamentlerin morfolojik karakterlerin incelenmesi, aktif çamur durumunun değerlendirilmesinde önemli bir rol oynamaktadır. Özellikle flokların dokusal özellikleri ve serbest halde bulunan filament miktarlarının incelenmesi gerekmektedir. Bu yüzden bölütleme aşamasının ayrı bir önemi vardır ve bu konuda yapılmış birçok çalışma bulunmaktadır. Bu çalışmada, Hücresel Sinir Ağları (HSA) kullanılmıştır. HSA’da sabit bir şablon kullanılmış, sadece iterasyon değeri görüntüye göre güncellenmiştir. İterasyon değerinin belirlenmesinde dalgacık metodu kullanılmıştır. Haar dalgacık filtresi kullanılarak ikinci seviyede ayrıştırma yapılmıştır. Bu ayrıştırma sonucu elde edilen alt bantların uzaysal frekans değerleri kullanılarak iterasyon değeri hesaplanmıştır. Çamur içinde serbest halde bulunan filementlerin miktarı, çamurun özelliği acısından önemlidir. Görüntülerde, filamentler ve floklar iç içe yer alabilmektedir. Bu yüzden görüntüde serbest halde veya floklarla teması bulunan filamentlerin görüntüden ayrıştırılması gerekmektedir. Dolayısıyla HSA işleminden sonra görüntüye bir dizi morfolojik işlemler uygulanmıştır. HSA bölütleme sonucu elde edilen görüntü, kenar çıkarma ve tophat dönüşümü uygulanan görüntüler ile piksel olarak karşılaştırılmıştır. Sonuç olarak, flok ve filamentler görüntünün özelliklerine göre ve çalışmanın amacına uygun olarak bölütlenmiştir.

Kaynakça

  • Amaral A. L., and Ferreira E. C., 2005, Activated sludge monitoring of a wastewater treatment plant using image analysis and partial least squares regression, Analytica Chimica Acta 544.1: 246-253.
  • Banadda E. N., Smets I. Y., Jenne R., Van Impe J. F., 2005, Predicting the anset of filamentous bulking in biological wastewaster treatment systems by exploiting image analysis information, Bioprocess and Biosystems Engineering, 27:339-348.
  • Boztoprak H., Özbay Y., A new method for segmentation of microscopic images on activated sludge, Journal of Electrical Engineering & Computer Sciences, DOI: 10.3906/elk- 1307-9.
  • Boztoprak H., Özbay Y., Güçlü D., Küçükhemek M., 2010, Determination of activated sludge floc characteristics using image processing, International Sustainable Water and Wastewater Management Symposium, 26-28 October 2010, Konya.
  • Chua L. O., Yang L., 1988a, Cellular neural networks: Theory, IEEE Trans. Circuits Syst., 35, 1257–1272.
  • Ernst B., Neser S., O’Brien E., Hoeger S. J., Dietrich D. R., 2006, Determination of the filamentous cyanobacteria Planktothrix rubescens in environmental water samples using an image processing system, Harmful Algae, 5, 281-289.
  • Grijspeerdt K., Verstraete W., 1997, Image analysis to estimate the settleability and concentration of activated sludge, Wat. Res., 31(5), 1126-1134.
  • Haliki A., Özdemir G., Uzel A., 2004, Aktif çamur sistemlerinde sorun yaratan filamentli mikroorganizmaların izolasyonu ve kontrol stratejileri üzerinde bir araştırma, E. Ü. Journal of Fisheries & Aquatic Sciences Issue, (3-4), 275-207.
  • Heine, W., et al., 2002, Early warning-system for operation-failures in biological stages of WWTPs by on-line image analysis, Water Science & Technology, 46.4-5: 117-124.
  • Jenkins D., Richard M.G., Daigger G. T., 1993, Manuel On The Causes And Control Of Activated Sludge Bulking And Foaming, 2.nd Edition, Lewis Publishers, Chelsea, Michigan.
  • Kilander J., Blomström S., and Rasmuson A., 2006, Spatial and temporal evolution of floc size distribution in a stirred square tank investigated using PIV and image analysis, Chemical Engineering Science 61.23: 7651-7667.
  • Liwarska E. B., 2005, Application of image analysis techniques in activated sludge wastewater treatment processes, Biotechnology Letters 27, 1427–1433.
  • Liwarska-Bizukojc E., & Bizukojc M., 2006, Effect of selected anionic surfactants on activated sludge flocs, Enzyme And Microbial Technology, 39(4), 660-668.
  • Liwarska-Bizukojca E., Bizukojc M., 2005, Digital image analysis to estimate the influence of sodium dodecyl sulphate on activated sludge flocs, Process Biochemistry, 40(6), 2067– 2072.
  • Lopez C., Pons M. N., & Morgenroth E., 2005, Evaluation of microscopic techniques (epifluorescence microscopy, CLSM, TPE-LSM) as a basis for the quantitative image analysis of activated sludge, Water research, 39(2), 456-468.
  • Mesquita D. P., Amaral A. L, Ferreira E. C., 2011, Characterization of activated sludge abnormalities by image analysis and chemometric techniques, Analytica Chimica Acta 2011, 705, 235-242.
  • Mesquita D. P., Amaral A. L., Ferreira E. C., & Coelho M. A., 2008, Study of saline wastewater influence on activated sludge flocs through automated image analysis. Journal of Chemical Technology And Biotechnology, 84(4), 554-560.
  • Mesquita D. P., Dias O., Amaral A. L., Ferreira E. C., 2009, Monitoring of activated sludge settling ability through image analysis: validation on full-scale wastewater treatment plants, Bioprocess Biosyst Eng., 32, 361–367.
  • Motta M. L. P. Amaral C., 2001, Characterisation of activated sludge by automated image analysis: validation on full-scale plants, IFAC Computer Applications in Biotechnology, Québec City-Canada.
  • Nisar H., Yong L. X., Ho Y. K., Voon Y. V., Siang S. C., 2012, Application of imaging techniques for monitoring flocs in activated sludge, International Conference on Biomedical Engineering (ICoBE), 27-28 February, Penang-Malaysia.
  • Övez S., 2010, Biyolojik atıksu arıtma tesisleri: kaçınılmaz sonuç çamur kabarma ve köpük problemi, İTÜ XII. Endüstriyel Kirlenme Kontrolü Sempozyumu, 16-18 Haziran, İstanbul.
  • Perez Y. G., Leite S. G. F., Coelho M. A. Z., 2006, Activated sludge morphology characterization through an image analysis procedure, Brazilian Journal of Chemical Engineering, 23.3 319-330.
  • Rika J., Ephraim N. B., Ilse S., Jeroen D., and Jan V. I., 2007, Detection of filamentous bulking problems: developing an image analysis system for sludge composition monitoring, Microscopy and Microanalysis, 13, 36–41.
  • Sevgen S., 2009, Hücresel sinir ağları için kararlı şablon tasarımı ve görüntü işleme uygulamaları, İstanbul Üniversitesi Fen Bilimleri, Enstitüsü, Doktora Tezi, Bilgisayar Mühendisliği Anabilim Dalı, İstanbul.
  • Sezgin, M., Jenkins, D., & Parker, D. S., 1978, A unified theory of filamentous activated sludge bulking, Journal (Water Pollution Control Federation), 362-381.

SEGMENTATION OF FLOC AND FILAMENTS USING CELLULAR NEURAL NETWORKS AND WAVELET TRANSFORM

Yıl 2015, Cilt: 7 Sayı: 1, 38 - 49, 01.03.2015

Öz

Examination of morphological characteristics of flocs and filaments plays an important role for activated sludge. An examination should be conducted especially on textural features and free filament amounts. This is why the segmentation stage has a particular importance and there are many studies in this regard. In this study, cellular neural networks (CNN) were used. A constant template was used and it was only the iteration value that was updated according to image. Wavelet method was employed to determine the iteration value. Second level decomposition was made with Haar wavelet filter. Iteration value was calculated with spatial frequency values of subbands acquired from decomposition process. The free filament amount in sludge is substantial in terms of activated sludge features. Filaments and flocs in images may appear one within the other. Therefore, filaments that are present free or in contact with flocs should be degraded from the image. Hence, a series of morphological processes were applied on the image after CNN process. A comparison was made between the image acquired from CNN segmentation process and images acquired from edge extraction and top-hat transform. Consequently, flocs and filaments were segmented parallel to features of the image and aim of the study.

Kaynakça

  • Amaral A. L., and Ferreira E. C., 2005, Activated sludge monitoring of a wastewater treatment plant using image analysis and partial least squares regression, Analytica Chimica Acta 544.1: 246-253.
  • Banadda E. N., Smets I. Y., Jenne R., Van Impe J. F., 2005, Predicting the anset of filamentous bulking in biological wastewaster treatment systems by exploiting image analysis information, Bioprocess and Biosystems Engineering, 27:339-348.
  • Boztoprak H., Özbay Y., A new method for segmentation of microscopic images on activated sludge, Journal of Electrical Engineering & Computer Sciences, DOI: 10.3906/elk- 1307-9.
  • Boztoprak H., Özbay Y., Güçlü D., Küçükhemek M., 2010, Determination of activated sludge floc characteristics using image processing, International Sustainable Water and Wastewater Management Symposium, 26-28 October 2010, Konya.
  • Chua L. O., Yang L., 1988a, Cellular neural networks: Theory, IEEE Trans. Circuits Syst., 35, 1257–1272.
  • Ernst B., Neser S., O’Brien E., Hoeger S. J., Dietrich D. R., 2006, Determination of the filamentous cyanobacteria Planktothrix rubescens in environmental water samples using an image processing system, Harmful Algae, 5, 281-289.
  • Grijspeerdt K., Verstraete W., 1997, Image analysis to estimate the settleability and concentration of activated sludge, Wat. Res., 31(5), 1126-1134.
  • Haliki A., Özdemir G., Uzel A., 2004, Aktif çamur sistemlerinde sorun yaratan filamentli mikroorganizmaların izolasyonu ve kontrol stratejileri üzerinde bir araştırma, E. Ü. Journal of Fisheries & Aquatic Sciences Issue, (3-4), 275-207.
  • Heine, W., et al., 2002, Early warning-system for operation-failures in biological stages of WWTPs by on-line image analysis, Water Science & Technology, 46.4-5: 117-124.
  • Jenkins D., Richard M.G., Daigger G. T., 1993, Manuel On The Causes And Control Of Activated Sludge Bulking And Foaming, 2.nd Edition, Lewis Publishers, Chelsea, Michigan.
  • Kilander J., Blomström S., and Rasmuson A., 2006, Spatial and temporal evolution of floc size distribution in a stirred square tank investigated using PIV and image analysis, Chemical Engineering Science 61.23: 7651-7667.
  • Liwarska E. B., 2005, Application of image analysis techniques in activated sludge wastewater treatment processes, Biotechnology Letters 27, 1427–1433.
  • Liwarska-Bizukojc E., & Bizukojc M., 2006, Effect of selected anionic surfactants on activated sludge flocs, Enzyme And Microbial Technology, 39(4), 660-668.
  • Liwarska-Bizukojca E., Bizukojc M., 2005, Digital image analysis to estimate the influence of sodium dodecyl sulphate on activated sludge flocs, Process Biochemistry, 40(6), 2067– 2072.
  • Lopez C., Pons M. N., & Morgenroth E., 2005, Evaluation of microscopic techniques (epifluorescence microscopy, CLSM, TPE-LSM) as a basis for the quantitative image analysis of activated sludge, Water research, 39(2), 456-468.
  • Mesquita D. P., Amaral A. L, Ferreira E. C., 2011, Characterization of activated sludge abnormalities by image analysis and chemometric techniques, Analytica Chimica Acta 2011, 705, 235-242.
  • Mesquita D. P., Amaral A. L., Ferreira E. C., & Coelho M. A., 2008, Study of saline wastewater influence on activated sludge flocs through automated image analysis. Journal of Chemical Technology And Biotechnology, 84(4), 554-560.
  • Mesquita D. P., Dias O., Amaral A. L., Ferreira E. C., 2009, Monitoring of activated sludge settling ability through image analysis: validation on full-scale wastewater treatment plants, Bioprocess Biosyst Eng., 32, 361–367.
  • Motta M. L. P. Amaral C., 2001, Characterisation of activated sludge by automated image analysis: validation on full-scale plants, IFAC Computer Applications in Biotechnology, Québec City-Canada.
  • Nisar H., Yong L. X., Ho Y. K., Voon Y. V., Siang S. C., 2012, Application of imaging techniques for monitoring flocs in activated sludge, International Conference on Biomedical Engineering (ICoBE), 27-28 February, Penang-Malaysia.
  • Övez S., 2010, Biyolojik atıksu arıtma tesisleri: kaçınılmaz sonuç çamur kabarma ve köpük problemi, İTÜ XII. Endüstriyel Kirlenme Kontrolü Sempozyumu, 16-18 Haziran, İstanbul.
  • Perez Y. G., Leite S. G. F., Coelho M. A. Z., 2006, Activated sludge morphology characterization through an image analysis procedure, Brazilian Journal of Chemical Engineering, 23.3 319-330.
  • Rika J., Ephraim N. B., Ilse S., Jeroen D., and Jan V. I., 2007, Detection of filamentous bulking problems: developing an image analysis system for sludge composition monitoring, Microscopy and Microanalysis, 13, 36–41.
  • Sevgen S., 2009, Hücresel sinir ağları için kararlı şablon tasarımı ve görüntü işleme uygulamaları, İstanbul Üniversitesi Fen Bilimleri, Enstitüsü, Doktora Tezi, Bilgisayar Mühendisliği Anabilim Dalı, İstanbul.
  • Sezgin, M., Jenkins, D., & Parker, D. S., 1978, A unified theory of filamentous activated sludge bulking, Journal (Water Pollution Control Federation), 362-381.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA42JT24PH
Bölüm Araştırma Makalesi
Yazarlar

Halime Boztoprak Bu kişi benim

Yayımlanma Tarihi 1 Mart 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 7 Sayı: 1

Kaynak Göster

IEEE H. Boztoprak, “HÜCRESEL SİNİR AĞLARI VE DALGACIK DÖNÜŞÜMÜ KULLANARAK FLOK VE FİLAMENTLERİN BÖLÜTLENMESİ”, UTBD, c. 7, sy. 1, ss. 38–49, 2015.

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