Control of Nitric Oxide emissions from a laboratory combustor using artificial neural networks

T. Slanvetpan, Robert Barat

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

An active control system based on statically trained, feed-forward, multilayer-perceptron neural networks was designed and demonstrated, by experiment and simulation, for NO and CO2 from a two-stage laboratory combustor operated under staged-air conditions. The neural networks are arranged in two clusters for feed-forward/feedback control. The first cluster is a neural-network-based, model-predictive controller (NMPC) and is used to identify the process disturbance and adjust the manipulated variables. The second cluster is a neural-network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional integral derivative controller. The novel controller has also been demonstrated on a neural-network-based combustor process simulator.

Original languageEnglish (US)
Pages (from-to)1761-1782
Number of pages22
JournalCombustion Science and Technology
Volume175
Issue number10
DOIs
StatePublished - Jan 1 2003

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Physics and Astronomy(all)

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