In-furnace Temperature Information Included Combustion Optimization Of A Utility Boiler

Coal will remain China's main primary energy in the foreseeable few decades. Coal-fired thermal power dominates the country's power industry under the coal-based energy structure. Normally, boiler combustion system optimization focuses on important control parameters, spotting the optimization strategy on a specific operating point, but lacks general adaptability. Model prediction and multi-objective optimization based combustion optimization system can achieve system closed-loop control, and the multiple optimization objectives of economic and environmental, thus it has been widely applied.In this paper, the furnace combustion temperature field detection system was used to detect combustion experiment under boiler's multiple working conditions, and temperature distribution along the high degree cross-section of the furnace, combustion efficiency and NOx emission data were obtained. Adding the furnace cross-section temperature information to the multiple combustion detects results, the combustion efficiency and NOx emission prediction BP neural network model was established. The relative error between the combustion efficiency and NOx emissions predicted by the model and detect value was less than 1% and 5%. Based on the established prediction model, the genetic algorithm and particle swarm optimization algorithm were respectively used for the higher boiler combustion efficiency and lower NOx emission combustion optimization with single and multi-objective. Multi-objective optimization results show that adding information of the furnace temperature prediction model is more realistic; under 134MW, 154MW, 172MW, the optimized combustion efficiency rose by 0.84%, 1.37% and 2.62%, NOx emission decreased by 18%, 6%, 18% respectively. Finally, the optimization process and results were verified by numerical simulation method. The error of cross-section average temperature between simulation results and the detect were less than 8%, indicating that the numerical simulation can accurately simulate the combustion process. Boiler NOx emissions and combustion efficiency of the numerical simulation going in good agreement with the trend of the results of the optimization value also shows that the optimization results are reasonable. This study showed that the neural network predict model of combustion based on furnace combustion temperature field visual system, detecting the furnace combustion temperature information, can predict the combustion process more accurately, and guide the boiler combustion optimization, and also lay the foundation for establishing on line combustion optimization system.