MATHEMATICAL MODELS FOR THE FUNCTIONING OF THE BEET-PROCESSING DEPARTMENT IN THE SUGAR PRODUCTION FACTORY
Journal Title: Computer Science Information Technology Automation - Year 2018, Vol 1, Issue 8
Abstract
Purpose. The article deals with the technological object of the beet processing department of the sugar factory. The mathematical models aimed to further increase the facility efficiency by using the effective algorithms have been developed. Methods. Existing research and mathematical models have been analyzed; their shortcomings as variables dependences and perturbations, which were not adequately addressed or implemented in previous works, have been highlighted. Scientific novelty. Serial presentation of the subsystems of the beet processing department in the differential equations is presented in the article. A mathematical model for the processing of beet pulp has been developed, which takes into account the dependence of the raw material consumption, the network current load and transport units. The mathematical presentation of the technological complex of washing, beet processing and transportation is overviewed. The descriptive analysis of the time series of the beet processing department and statistical processing for the development of intelligent control systems are used. The practical significance of the results allows authors to use the outputs aimed to develop identification systems for these technological processes. They can also be used for analysis based on the time series methodology and mathematical modeling, thus, as models to form real technological complexes. In addition, they can be offered as educational material for students in the course of mathematical analysis of sugar mills. Research outcomes can be used when identifying a technological object of beet processing department; when developing mathematical models of subsystems for studying the characteristics of technological processes, synthesis of systems for forecasting and management of beet processing department. Moreover, they can be introduced with new science methodologies like Bayesian nets, Neural Networks, Statistical Analysis, Deep Learning etc.
Authors and Affiliations
A. P. Ladaniuk, A. O. Bezuhlov, R. O. Boiko
Keywords
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