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ENVIRONMENTAL PROTECTION THROUGH AUTOMATED MONITORING OF STEAM TEMPERATURE IN THE WORK INSTALLATIONS OF COMPOUND FEED FACTORIES

Cristian Vasile, Mihnea Glodeanu, Tudor Alexandru

First published: 2017-06-20https://doi.org/10.5593/sgem2017/51/s20.051View metrics

Abstract

To comply with European UnionпїЅs rules regarding the environmental protection it is necessary for the all compound feed factories (CFF) to be fitted with modern and performant working equipments, with a high degree of mechanization, automation and computerization, with high productivity and low specific consumption, but also with opportunities for rigorous monitoring of pollutants and dust removed. Taking into account the complexity of the technological flow that takes place in a compound feed factory (CFF), this article analyzes the thermal parameters of the work installation, in the area where is made the homogenization and sterilization of the combined forage granules using the pressurized steam at very high temperatures, of over 150пїЅC. Experimental measurements performed in different critical points of the technological flow of the compound feed factories (CFF) and, also, for different recipes of compound feed allowed the collection of conclusive evidence that serves to justify the advantage of using an automatized working installations, which allow functioning optimally and in the same time fully respecting the European Union rules on environmental protection.

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Publication details

Title
ENVIRONMENTAL PROTECTION THROUGH AUTOMATED MONITORING OF STEAM TEMPERATURE IN THE WORK INSTALLATIONS OF COMPOUND FEED FACTORIES
Authors
Cristian Vasile, Mihnea Glodeanu, Tudor Alexandru
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 17th International Multidisciplinary Scientific GeoConference SGEM2017, Ecology, Economics, Education and Legislation
Publisher
STEF92 Technology
Year
2017
Pages
387-394
SWS Citekey
VASILE201720387394
ISSN
1314-2704
ISBN
978-619-7408-08-9
Language
en
Publication type
Conference Paper
Keywords
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