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SOFTWARE AND HARDWARE APPLIANCE FOR RESEARCH, MODELLING AND OPTIMISATION OF CYCLIC PROCESSES OF GAS MIXTURE SEPARATION

Evgeniy Akulinin

First published: 2018-06-20https://doi.org/10.5593/sgem2018/4.2/s19.077View metrics

Abstract

A task-oriented software and hardware system is designed for the operational selection of the instrumentation and process design options of pressure swing adsorption (PSA) processes. It will also control physical experiments to study the kinetics and dynamics of adsorption purification and separation of multicomponent gas mixtures and carry out mathematical modeling, optimization of operating cycles and optimal design of energy- and resource-saving PSA units. The possibilities of a task-oriented system are demonstrated on the example of the adsorption separation of atmospheric air in order to obtain oxygen of high purity: 1) the time required to obtain, analyze and process experimental data (isotherms, kinetics and dynamics of adsorption, tuning the operating modes of PSA processes in the separation of gas mixtures, etc.), necessary for designing, identifying and analyzing the adequacy of mathematical models, were reduced by three times; 2) the time spent on mathematical modeling, the formulation and solution of the problems of optimization and optimal design of industrial PSA units in conditions of partial uncertainty of the initial data, as well as an experimental verification of the optimization results of these processes in a test unit , were reduced 8 times.

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Publication details

Title
SOFTWARE AND HARDWARE APPLIANCE FOR RESEARCH, MODELLING AND OPTIMISATION OF CYCLIC PROCESSES OF GAS MIXTURE SEPARATION
Authors
Evgeniy Akulinin
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 18th International Multidisciplinary Scientific GeoConference SGEM2018, Energy and Clean Technologies
Publisher
STEF92 Technology
Year
2018
Pages
599-606
SWS Citekey
Akulinin201819599606
ISSN
1314-2704
ISBN
978-619-7408-45-4
Language
en
Publication type
Conference Paper
Keywords
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