STAGED COMBUSTOR CONTROL USING ARTIFICIALNEURAL NETWORK-BASED PROCESS MODELS

ABSTRACT

Process controllers using trained, feed-forward, multi-layer-perceptron (FMLP) neural networks as complex process models have been successfully demonstrated for the active, on-line control of selected species emitted from a two-stage combustion reactor. In the first case, as compared to a proportional-integral-derivative controller, faster control of exhaust oxygen content with nearly no offset was achieved using a proportional controller with a variable bias value as determined by an FMLP. In the second case, effective and rapid control of exhaust nitrogen oxide, after a separate feed stream disturbance and a set point change, was achieved using a controller comprised of two clusters of FMLP neural networks. The first cluster identified the process disturbance and adjusted the manipulated variable. The second cluster served as a Smith time-delay compensator. All the FMLP networks used were trained off-line using steady-state data obtained from both experiments and from direct combustor simulations based on detailed chemical reactions.

Keywords:

• Combustion
• Neural network
• Process control

Acknowledgments

The authors extend their appreciation for financial support of this work to the Otto York Center for Environmental Engineering & Science at NJIT, and to the U.S. EPA-funded Northeast Hazardous Substances Research Center, also at NJIT.