Predicting effects of toxic events to anaerobic granular sludge with quantitative image analysis and principal component analysis

• Autor(a) principal: Costa, J. C.
• Data de Publicação: 2008
• Outros Autores: Alves, M. M., Ferreira, Eugénio C.
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: https://hdl.handle.net/1822/8114
• topic: Detergent; Principal component analysis; Quantitative image analysis; Solvent; Toxic shock load

Detergents and solvents are included in the list of compounds that can be inhibitory or toxic to anaerobic digestion processes. Industrial cleaning stages/processes produce vast amounts of contaminated wastewater. In order to optimize the control of these wastewaters it is important to know and predict the effects on the activity and physical properties of anaerobic aggregates in an early stage. Datasets gathering morphological, physiological and reactor performance information were created from three toxic shock loads (SL1 – 1.6 mgdetergent/L; SL2 – 3.1 mgdetergent/L; SL3 – 40 mgsolvent/L). The use of Principal Component Analysis (PCA) allowed the visualization of the main effects caused by the toxics, by clustering the samples according to its operational phase, exposure or recovery. The morphological parameters showed to be sensitive enough to detect the operational problems even before the COD removal efficiency decreased. Its high loadings in the plane defined by the first and second principal components, which gathers the higher variability in datasets, express the usefulness of monitor the biomass morphology in order to achieve a suitable control of the process. PCA defined a new latent variable t[1], gathering the most relevant variability in dataset, that showed an immediate variation after the toxics were fed to the reactors. t[1] varied 262, 254 and 80%, respectively in SL1, SL2 and SL3. Once more, the high weights of the morphological parameters associated with this new variable express its influence in shock load monitoring and control, and consequently in operational problems recognition.