Chemical oxidation is a key technique used in dye wastewater treatment via the formation of hydroxyl radicals.To obtain optimal treatment effects, it is critical to understand the interaction of the molecular structure of the dye with the hydroxyl radical. We evaluated fluorescence excitation-emission matrix spectroscopy to study the decay of an azo-dye (Procion Red MX-5B) by a hydroxyl radical generated from catalytic Fe (III) on H2O2. Results showed that fluorescence signal reliably indicated the variations of the chemical groups and components during degradation, and the degradation could be divided into three stages: initial degradation (decolorisation), rapid intermediate degradation, and final degradation. Under control of uncorrected matrix correlation, the fluorescence fractions could be fitted successfully by parallel factor model (PARAFAC) model: two fluorescence components in initial degradation including mono substituted benzene and mono substituted naphthalene, three components as multi substituted benzene in rapid degradation, and no components could be resolved in the final degradation. The results from the study demonstrate the utility fluorescence characterization of dye degradation mechanisms and enhance the understanding of the degradation mechanisms.
The increasing quantities of polluted waters are calling for advanced purification methods. Flocculation is an essential component of the water purification process, yet flocculation is commonly not optimal due to our poor understanding of the flocculation process. In particular, there is little knowledge on the mechanisms ruling the migration of pollutants during treatment. Here we have created the first tensor diagram, a mathematical framework for the flocculation process, analyzed its properties with a deep learning model, and developed a classification scheme for its relationship with pollutants. The tensor was constructed by combining pixel matrices from a variety of floc images, each with a particular flocculation period. Changing the factors used to make flocs images, such as coagulant dose and pH, resulted in tensors, which were used to generate matrices, that is the tensor diagram. Our deep learning algorithm employed a tensor diagram to identify pollution levels. Results show tensor map attributes with over 98% of sample images correctly classified. This approach offers potential to reduce the time delay of feedback from the flocculation process with deep learning categorization based on its clustering capabilities. The advantage of the tensor data from the flocculation process improves the efficiency and speed of response for commercial water treatment.
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