Publicação
Characterization of activated sludge abnormalities by image analysis and chemometric techniques
| Resumo: | his work focuses on the use of chemometric techniques for identifying activated sludge process abnormalities. Chemometric methods combined with image analysis can improve activated sludge systems monitoring and minimize the need of analytical measurements. For that purpose data was collected from aggregated and filamentous biomass, biomass composition on Gram-positive/Gram-negative bacteria and viable/damaged bacteria, and operational parameters. Principal component analysis (PCA) was subsequently applied to identify activated sludge abnormalities, allowing the identification of several disturbances, namely filamentous bulking, pinpoint flocs formation, and zoogleal bulking as well as normal conditions by grouping the collected samples in corresponding clusters. |
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| Autores principais: | Mesquita, D. P. |
| Outros Autores: | Amaral, A. L.; Ferreira, Eugénio C. |
| Assunto: | Activated sludge image Analysis Morphology Physiology CheMometric techniques Activated sludge Image analysis |
| Ano: | 2011 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | his work focuses on the use of chemometric techniques for identifying activated sludge process abnormalities. Chemometric methods combined with image analysis can improve activated sludge systems monitoring and minimize the need of analytical measurements. For that purpose data was collected from aggregated and filamentous biomass, biomass composition on Gram-positive/Gram-negative bacteria and viable/damaged bacteria, and operational parameters. Principal component analysis (PCA) was subsequently applied to identify activated sludge abnormalities, allowing the identification of several disturbances, namely filamentous bulking, pinpoint flocs formation, and zoogleal bulking as well as normal conditions by grouping the collected samples in corresponding clusters. |
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