PRBS based identification and conditional control for an optimal operation of a pilot plant binary distillation column

Eadala Sarath Yadav, Thirunavukkarasu Indiran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Input signal design is a crucial part in data driven based system identification. Input is set to excite in such a way that the effect of output should be larger than those responses caused by sensor noise. Input signal generation should contain amplitude, rate of input change (Frequency), bias and variance. One such signal is Pseudo Random Binary Sequence (PRBS) input signal. This papers deals with the system identification of a binary distillation column with temperature as variable of interest, heater voltage and reflux flow rate as its manipulated inputs. Based on the requirement of manipulated input, conditional switch is assigned such that manipulated inputs (Heater and Reflux) switches as a function of error. It is observed that this approach improves the system performance and reduces the operational cost. Result depicts the efficiency of the methodology and control loop structure.

Original languageEnglish
Title of host publication2019 8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538676844
DOIs
Publication statusPublished - 04-2019
Event8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019 - Manama, Bahrain
Duration: 15-04-201917-04-2019

Publication series

Name2019 8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019

Conference

Conference8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019
Country/TerritoryBahrain
CityManama
Period15-04-1917-04-19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Modelling and Simulation
  • Health Informatics

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