In response to the goal of Carbon peak Carbon neutral,China′s circulating fluidized bed boilers participate in deep peaking operation on a large scale,resulting in large fluctuation ranges of NO emission concentration in boilers,poor control effect,and difficultyin meeting the demand for ultra-low emission of pollutants,so it is important to accurately model and predict the NO emission concentration in deep peaking. Based on the instantaneous carbon model,the NO generation and reduction mechanism in the furnace was deeply analyzed,and the instantaneous carbon combustion model,O dynamic balance model,CO soft measurement model,NO generation and reduction model were established to complete the calculation of the mechanism of the NO concentration at the entrance of the SNCR. Theamount of coal feed,bed temperature,flue gas temperature and oxygen content,the first and second airflow,and the flow rate of the urea solution were selected as the input variables for the NO emission concentration, and the NO emission concentration was predicted bythe SNCR inlet model. The SNCR inlet NO concentration was used as an extended input variable,and the data set was reconstructedby correlation analysis and delay compensation between all input variables and NO emission concentration. The reconstructed data set wastrained and predicted by using long and short-term memory neural network,and whale optimization algorithm was used for the optimizationof parameters of the long and short-term memory neural network to establish a NO emission concentration model,the mechanism-data hybrid prediction model,for deep peaking of circulating fluidized bed boilers. The simulation validation shows that the hybrid prediction model has good prediction performance and generalization ability under different working conditions,and is able to realize real-time predictionof NO emission concentration in circulating fluidized bed boilers at variable loads,and significantly improves all the error performance indexes compared with other prediction models,with an average absolute error δ up to 2.14 mg/ m,an average relative percentage errorδ up to 5.68%,and a coefficient of determination R up to 0.902 1. The hybrid prediction model can accurately predict the NO emission concentration under deep peaking in circulating fluidized bed boilers,which provides a reference for the design of the ultra-low emission intelligent control system of circulating fluidized bed boilers.

张鹏新(2000—),男,山西大同人,硕士研究生。E-mail:1912179047@qq.com

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ZHANG Pengxin,GAO Mingming,GUO Jiongnan,et al.Prediction of NOx emissions from deep peaking circulating fluidizedbed boilers based on a hybrid modelling approach[J].Clean Coal Technology,2024,30(9):85-94.