Abstract After the implementation of ultra-low emission policies, the pollutant emissions from the coal-fired power generation systems in China have been further reduced, which creates more critical requirements for the control accuracies of denitrification systems using selective catalytic reduction technology and controls of ammonia slip. This article presents big data-based technologies for controlling ammonia slip, through precise ammonia injections, which were applied and demonstrated for a flue gas denitrification system of a Chinese coal-fired power plant. Through examinations and analyses of the basic operating conditions of the unit and parameters, such as NOx emission control efficiency, ammonia injection amount of the denitrification system, and non-uniformity of NOx concentrations in the denitrification zone were compared before and after the implementation of these technologies, the outcome proves that this artificial intelligence algorithm based on big data can effectively solve the automatic control problem of denitrification system under complex working conditions such as varied unit loads, day and night operation changed coal source. In addition to the effective controls of the ammonia slip of the system, the controls of NOx emission of the system become more stabilized, creating a successful example for wider applications of these precise ammonia injection control technologies in the future. Analyses show that NOx non-uniformities can be reduced by more than 50% under both stable and variable load conditions. NOx fluctuations at the unit outlet are tightly controlled within ±10 mg/Nm3 under variable load conditions and within ±5 mg/Nm3 under stable conditions. The average ammonia injection amounts under various load conditions have decreased by 15.7%.
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