2300-20-02-00 Cogeneration industry boiler operation core pain points

Circulating fluidized bed boiler combustion in the traditional cogeneration industry generally has the following pain points:

The traditional control based on conventional algorithms has limitations in the scenario of strong coupling, large inertia, large lag and multi-variable control of boiler combustion, and the load demand response is not timely.
The combustion condition is complex and changeable, and the traditional control mode is easy to cause insufficient combustion or large exhaust smoke loss, resulting in low thermal efficiency.
In the traditional operation mode, the boiler load is distributed manually, which cannot be distribu2300-20-02-00 ted according to the boiler energy efficiency, load condition and combustion state, thus affecting the overall boiler combustion efficiency.
Conventional boiler NOx regulation has a large inertia lag, in order to avoid NOx exceeding the standard, usually take the way to increase the amount of ammonia injection, resulting in a higher ammonia escape problem.
These factors directly or indirectly cause the boiler low automatic control rate, large fluctuation of operating parameters, adjustment lag, insufficient combustion, large exhaust smoke loss, low thermal efficiency problems.

Built on Transformer architecture

AI combustion optimization control system

Boiler combustion optimization control solution based on industrial AI realizes big data optimization control, and constructs intelligent optimization control system through Transformer architecture, integrates time series prediction model, cascade recommendation algorithm, and feedforward + feedback control mode to optimize and control key parameters, making boiler combustion more stable. By adopting multi-objective cooperative optimization, the stable combustion state and the optimal bed temperature of the boiler are maintained without overtemperature and coking during the combustion optimization process, which makes the operation more efficient and improves the combustion efficiency of the boiler.

AI boiler combustion optimization control architecture

Help Jiande Thermal Power Plant “improve human efficiency, stable operation and increase income”

The boiler combustion intelligent optimization control system based on industrial AI has undergone multiple ro2300-20-02-00 unds and long cycle alternating verification tests with remarkable results. In the boiler combustion optimization project of Jiande Thermal Power Plant, the automatic control rate of the system reached more than 95%, the average fluctuation range of key operating parameters was reduced by more than 30%, and the index of coal consumption per ton of steam was reduced by more than 1% compared with manual operation. The key parameters of boiler combustion were used as characteristic feedforward, and ammonia escape was reduced by more than 20% compared with manual control when nitrogen oxides were not exceeded. Effectively reduce the work intensity of the operator, improve the boiler combustion efficiency, and effectively help Jiande thermal power plant “improve human efficiency, stable operation and increase income”.