Process control of a laboratory combustor using artificial neural networks

T. Slanvetpan, R. B. Barat, J. G. Stevens

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Active process control of nitric oxide (NO) emissions from a two-stage combustor burning ethylene (doped with ammonia) in air is demonstrated using two clusters of feed-forward multi-layer-perceptron neural networks. Steady-state experimental data are used for static back-propagation network training. The first cluster consists of two neural networks. The first network identifies the amount of ammonia in the feed. Based on that value and the NO set point, the second network adjusts the first-stage fuel equivalence ratio φ1. The second cluster also consists of two neural networks. It is the process emulator and serves as a Smith time-delay compensator. A VISUAL BASIC interface control program accepts incoming concentration and flow rate data signals, accesses the neural networks, and outputs feedback control signals to selected electronic valves. Closed-loop results are compared to the open-loop results. The neural network-based controller successfully brought NO emissions into control after a step disturbance in the feed composition stream (ammonia dopant). The neural network-based controller shows a superior performance over the conventional proportional-integral-derivative controller.

Original languageEnglish (US)
Pages (from-to)1605-1616
Number of pages12
JournalComputers and Chemical Engineering
Issue number11
StatePublished - Nov 15 2003

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering
  • Computer Science Applications


  • Combustion system
  • Feed-forward neural networks
  • Model-based control
  • Process control
  • Time-delay compensation


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