Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms

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.