Fractal Analysis of Temperature Time Series From Batch Sugarcane Crystallization

Abstract

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.

Two‐phase flow‐patterns identification in oil/gas pipelines based on fractal analysis

“…[ 22 ] Furthermore, several authors have reported that these methodologies applied to process data can associate the multiscale indexes variation with different phenomena characteristics of a system at a specific time scale. [ 23,24 ] This because the fractal analysis permits the detection of long‐range correlations embedded in a time series by the presence of one or more power‐law behaviours. [ 25 ] Besides, it is easy to implement, with low computational cost, and due to its robustness to noise measurements it is suitable for operation in harsh environments, which makes it an attractive technique for flow‐pattern identification in comparison with other statistic methodologies.…”

Diagnosis of the Cane Sugar Crystallization Process by Multifractal Analysis of Temperature Time Series

“…However, imagebased multiscale analysis generates delays due to its acquisition and processing. In that sense, Campos-Domı ´nguez et al [19] performed a study by processing time series acquired from a batch cooling crystallization process by applying the rescaled range analysis (R/S) method. Results in that paper show that at characteristic scales, it is possible to identify zones related to the different phenomena involved in the process.…”