Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. This work presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes, with high values for the overall species recognition with the exception of sessile protozoa. In terms of the wastewater conditions assessment the obtained results were found to be suitable for the prediction of these conditions. Finally, the discriminant analysis and neural networks results were found to be quite similar whereas the decision trees technique was less appropriate.

A procedure for the semi-automatic identification of the main protozoa and metazoa species present in the activated sludge of wastewater treatment plants was developed. This procedure was based on both image processing and multivariable statistical methodologies, leading to the use of the image analysis morphological descriptors by discriminant analysis and neural network techniques. The image analysis program written in Matlab has proved to be adequate in terms of protozoa and metazoa recognition, as well as for the operating conditions assessment.

Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.

No presente trabalho, realizado em escala piloto, foi avaliada a influência do pH de coagulação e a da dose de sulfato de alumínio na morfologia dos flocos formados durante o processo de coagulação bem como na remoção de oocistos de Cryptosporidium por filtração direta descendente. As condições de coagulação testadas incluíram a dose ótima e superdose de coagulante em duas faixas distintas de pH. Os resultados mostraram que a eficiência de remoção dos oocistos de Cryptosporidium variou de 1,4 a 3,2 log durante a operação estável do filtro. Os experimentos realizados em valores de pH de coagulação mais baixo, em torno de 5, promoveram as melhores eficiências de remoção de oocistos de Cryptosporidium. A análise morfológica dos flocos mostrou uma tendência à formação de flocos mais porosos e irregulares ao se adotar superdosagem de coagulante.

Different protozoa and metazoa populations develop in the activated sludge wastewater treatment processes and are highly dependent on the operating conditions. In the current work the protozoa and metazoa groups and species most frequent in wastewater treatment plants were studied, mainly the flagellate, sarcodine, and ciliate protozoa as well as the rotifer, gastrotrichia, and oligotrichia metazoa. The work is centered on the survey of the wastewater treatment plant conditions by protozoa and metazoa population using image analysis, discriminant analysis (DA), and neural networks (NNs) techniques, and its main objective was set on the evaluation of the importance of raw data pre-processing techniques in the final results. The main pre-processing techniques herein studied were the raw parameters reduction set by a joint cross-correlation and decision trees (DTs) procedure and two data normalization techniques: logarithmic normalization and standard deviation normalization. Regarding the parameters reduction methodology, the use of a joint DTs and correlation analysis (CA) procedure resulted in 28 and 30% reductions in terms of the initial parameters set for the stalked and non-stalked microorganisms, respectively. Consequently, the use of the reduced parameters set has proven to be a suitable starting point for both the DA and NNs methodologies, although for the DA an initial logarithmic normalization step is advisable. For the NNs analysis a standard deviation normalization procedure could be considered for the non-stalked microorganisms regarding the operating parameters assessment.