Model order reduction by proper orthogonal decomposition for a 500 MWe tangentially fired pulverized coal boiler

Abstract

Reduced order models (ROMs) are constructed by proper orthogonal decomposition (POD) and regression by Kriging and Radial Basis Neural Network (RBFN) for a 500 MWe tangentially fired pulverized coal boiler. POD is performed to extract low-dimensional basis vectors to reproduce 3-D distribution of reacting scalars with respect to the operation parameters of total secondary air (TSA) and burner zone stoichiometric ratio (BSR). The ROMs by Kriging and RBFN both reproduce the scalar fields within 6% averaged relative L2 norm error at three validation points in the parameter space. It is possible to reproduce a 3-D scalar field at any unexplored operation condition within a few seconds through parallel computation of the ROM. It allows fast evaluation of the effects of varying operation parameters in the design stage and real time response of a digital twin based on the ROM for smart operation and maintenance of industrial combustion facilities.

Keywords

• Reduced-order model
• Tangentially fired boiler
• Coal combustion
• Proper orthogonal decomposition
• Kriging
• RBF Neural network