Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms
F. Si, C. E. Romero, Z. Yao, E. Schuster, Z. Xu, R. L. Morey and B. N. Liebowitz
Fuel, 88 (2009) 806-816
Abstract
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An integrated combustion optimization approach is presented for the combined considering the trade
offs in optimization of coal-fired boiler and selective catalyst reaction (SCR) system, to balance the unit
thermal efficiency, SCR reagent consumption and NOx emissions. Field tests were performed at a 160 MW
coal-fired unit to investigate the relationships between process controllable variables, and optimization
targets and constraints. Based on the test data, a modified on-line support vector regression model was
proposed for characteristic function approximation, in which the model parameters can be continuously
adapted for changes in coal quality and other conditions of plant equipment. The optimization scheme
was implemented by a genetic algorithm in two stages. Firstly, the multi-objective combustion optimization
problem was solved to achieve an optimal Pareto front, which contains optimal solutions for lowest
unit heat rate and lowest NOx emissions. Secondly, best operating settings for the boiler, and SCR
system and air preheater were obtained for lowest operating cost under the constraints of NOx emissions
limit and air preheater ammonium bisulfate deposition depth.