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Modelling dynamics of strongly coupled air paths in pneumatic transport system for milling product

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

In the milling circuit with an electromagnetic mill, a pneumatic system is used to transport the material and to maintain several parameters of the process. The pneumatic system contains three air inlets with controllable flaps and each inlet stream serves a different purpose, however, they are strongly physically coupled through the rest of the installation. Thus, position of each control flap strongly and nonlinearly affects the operating point of each inlet stream. Dynamics of air velocity within the main inlet pipe with respect to the setpoint of the main flap's position has been identified for multiple operating points of the installation, and the estimated models were checked and assessed. The resultant models may then be used to tune controller parameters for subsequent operating points. Identification and verification of the derived models was performed on data measured at an experimental milling installation.

Original languageEnglish
Title of host publication2017 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages843-848
Number of pages6
ISBN (Electronic)9781538624029
DOIs
Publication statusPublished - 19 Sept 2017
Event22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017 - Miedzyzdroje, Poland
Duration: 28 Aug 201731 Aug 2017

Publication series

Name2017 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017

Conference

Conference22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017
Country/TerritoryPoland
CityMiedzyzdroje
Period28/08/1731/08/17

Keywords

  • dynamic models
  • identification
  • optimization
  • pneumatic systems
  • step responses

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Optimization
  • Modeling and Simulation

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