Technologies

Based on boiler combustion data modeling and machine learning algorithms, real-time prediction and dynamic optimization of combustion status are achieved, realizing intelligent collaborative control of combustion intensity, air coal ratio, and load changes, reducing fuel consumption and improving combustion efficiency.

Through multi parameter fusion modeling and five impulse control strategy, the steam drum water level and steam flow rate are synergistically adjusted to achieve stable control of the steam water system under rapidly changing load conditions, ensuring the safe and stable operation of the boiler.

Based on the gas balance model and the closed-loop control model of exhaust temperature, the collaborative optimization of air supply, induced draft, and exhaust processes is carried out to achieve stable control of furnace negative pressure and continuous reduction of exhaust heat loss.

Establish a furnace condition prediction model based on historical operating data, continuously improve the thermal efficiency of boiler operation through key variable identification and parameter optimization, and achieve long-term stable operation of the boiler in the high-efficiency range.

Establish a standardized control system for unit start-up, grid connection, load increase, load decrease, disconnection, shutdown, turning and auxiliary machine shutdown, reduce manual step-by-step operations, and improve the consistency and traceability of the start stop process. Through functional group sequential control, breakpoint confirmation, abnormal pause, and independent security monitoring, the system can improve the level of low personnel on duty while ensuring effective security protection.

The fluctuation of flue gas concentration at the inlet is frequent, and the manual adjustment of slurry supply lags behind, resulting in unstable desulfurization efficiency and prominent problems of excessive consumption of chemicals. The system is based on a multivariate prediction model and a feedforward feedback composite control strategy to intelligently optimize the pulp making, pulp supply, and absorption tower operation processes, achieving stable desulfurization efficiency and continuous reduction of chemical consumption.

The amount of ammonia injection is difficult to accurately match in real-time based on the concentration of flue gas. Excessive ammonia injection poses a risk of ammonia escape, while insufficient ammonia injection leads to excessive emissions. The stable control of SCR systems under complex operating conditions relies on human experience for a long time. The system is based on an AI prediction model and a multivariable collaborative control algorithm to intelligently optimize the SCR flue gas temperature, ammonia injection rate, and GGH operating status, achieving collaborative optimization of denitrification efficiency, operational stability, and ammonia consumption.