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Monitoring of a Sugar Crystallization Process with Fuzzy Logic and Digital Image Processing
Author(s) -
Villanueva Daniel,
Posada Rubén,
González Israel,
García Ángel,
Martínez Albino
Publication year - 2015
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.12122
Subject(s) - crystallization , process (computing) , fuzzy logic , sugar , process engineering , computer science , fingerprint (computing) , artificial intelligence , algorithm , biological system , chemical engineering , chemistry , engineering , organic chemistry , biology , operating system
The sugar crystallization process is a solid–liquid separation where molecules are transferred from a solute dissolved in a liquid phase to a solid phase. The aim of this research is to present a monitoring computational framework to model sugar crystallization process based on expert knowledge using fuzzy logic and digital image techniques from direct samples of crystallization slurries, which determine the size of sugar crystals. The framework acquires images that feed a diffuse network automatically. Therefore, it is determined when displaying an image to various times in the process of crystallization. This allows us to know the growth behavior and to determine the optimum crystallization of the sugar crystals. The results show that using fuzzy logic techniques with digital images can be incorporated as a complementary tool for monitoring a sugar crystallization process. Practical Application In the sugar industry, it is very important to have a monitoring system for the crystallization process of sugar. This kind of system for the monitoring process can generate products of the highest quality and can facilitate energy savings in the industry. Moreover, the image processing‐based systems can also be used as a visual monitoring tool for the sugar crystallization process, allowing to improve the final quality of the products and to optimize the manufacturing process.