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Abstract:Nanoparticles and nanopores of iron oxide were synthesized by electrochemical anodization, in an electrolytic medium of ammonium fluoride (NH4F), deionized water and ethylene glycol. After anodization, the Fe foils were annealed at 450 °C for 2 hours. Different anodization times and two concentrations of NH4F (0.1 M and 1.2 M) were evaluated, under static conditions at room temperature. Scanning Electron Microscopy showed nanopores (0.1 M) and nanoparticles (1.2 M). Eight vibration modes characteristic of α-Fe2O3 were found with Raman spectroscopy technique. Relationship between the modes Eu(LO) and 2Eu(LO) was found, therefore, their association with the disorder in the crystalline structure can be determined and it was also found that 2Eu(LO) intensity mode at a concentration of 1.2 M is larger than 0.1 M nanostructures, the FWHM of the A1g mode at 227 cm-1 corresponding to the Fe3+ ions and the Eg at 293 cm-1 mode caused by the O2- ions was also analyzed and founded that the crystalline structure of hematite can be determined by the A1g mode at 227 cm-1.

Batch crystallization is an important process in many industries, for example, fine chemicals, foods, and pharmaceuticals. Monitoring of the main process variables is essential for process understanding, diagnosis, and for product quality control. It is known that temperature has a critical effect on crystallization. Temperature measurements from crystallization systems display fluctuations with apparently random and complex behavior. Fractal analysis of complex time series has received significant attention in the last few years due to its capability for extraction of hidden useful information of the underlying phenomena behind the time-series complexity. In this work, the potential of fractal analysis of time series for diagnosis of industrial crystallization processes is investigated using temperature measurements from a typical batch sugarcane crystallization system. The crystallizer was operated at different cooling profiles, finding that fractal index is directly related to crystal mean diameter dynamics. Thus, we establish that fractal analysis is a simple and robust alternative for the characterization of batch crystallization.

Due to the increasing demands of quality products, efficient monitoring systems in the current control and operation of industrial processes are essentials. However, in particulate processes as cane sugar crystallization, accurate, inexpensive and suitable sensors for the online monitoring of key process variables are not available. In this work, an alternative using the image analysis of micrographs captured in batch cooling crystallizer is presented. The propose is based on a combined treatment between fractal analysis and conventional binarization techniques, obtaining a normalized fractal index (NFI) that allow the dynamic monitoring of crystal mean diameter , D(4,3). In order to evaluate the monitoring system, the crystallizer was operated at different cooling profiles, finding that the methodology proposed can be used as an alternative technique, inexpensive and easy to implement, for monitoring crystal growth.