Automatic Combustion Control System (ACC)


This solution aims to improve the stability and automation rate of incinerator combustion control,stabilize the main steam flow rate,ensure a balanced waste supply,minimize the LOI(Loss on Ignition),reduce the emission of pollutants,and prevent abnormal conditions in the boiler,thus extending the lifespan of related equipment.


ACC can reduce the workload of operating personnel by over 30%compared to manual operation.


The stability of key operating parameters of the incinerator,such as main steam flow,waste layer thickness,oxygen concentration,and furnace temperature,has been improved.The fluctuation range of the main control parameter,main steam flow,is controlled within 5%.




Boiler main steam flow control:Boiler main steam flow control is the primary control loop of the ACC system,maintaining the boiler's main steam flow at the set point by adjusting the combustion air flow,grate movement speed,and other parameters.


Waste Thickness Control:The movement speed of the feeder grate is adjusted based on the measured thickness of the waste layer to maintain the waste layer thickness at the set point.


Garbage combustion position control:By adjusting the grate speed based on the temperature over the burnout grate and the garbage layer thickness,the system ensures the garbage combustion position remains within a suitable range.


Loss on Ignition Minimization Control:This control adjusts the air flow and grate movement speed to minimize the LOI。


Furnace temperature control:Control the flue gas temperature in the first gas flue duct of the incinerator to be at least 850°C,with flue gas retention time at least 2 seconds,to minimize pollutant emissions.


Flue gas oxygen content control:Adjust the secondary air to ensure the oxygen content in the flue gas remains within a suitable range.





This solution aims to improve the stability and automation rate of incinerator combustion control,stabilize the main steam flow rate,ensure a balanced waste supply,minimize the LOI(Loss on Ignition),reduce the emission of pollutants,and prevent abnormal conditions in the boiler,thus extending the lifespan of related equipment.


ACC can reduce the workload of operating personnel by over 30%compared to manual operation.


The stability of key operating parameters of the incinerator,such as main steam flow,waste layer thickness,oxygen concentration,and furnace temperature,has been improved.The fluctuation range of the main control parameter,main steam flow,is controlled within 5%.




Boiler main steam flow control:Boiler main steam flow control is the primary control loop of the ACC system,maintaining the boiler's main steam flow at the set point by adjusting the combustion air flow,grate movement speed,and other parameters.


Waste Thickness Control:The movement speed of the feeder grate is adjusted based on the measured thickness of the waste layer to maintain the waste layer thickness at the set point.


Garbage combustion position control:By adjusting the grate speed based on the temperature over the burnout grate and the garbage layer thickness,the system ensures the garbage combustion position remains within a suitable range.


Loss on Ignition Minimization Control:This control adjusts the air flow and grate movement speed to minimize the LOI。


Furnace temperature control:Control the flue gas temperature in the first gas flue duct of the incinerator to be at least 850°C,with flue gas retention time at least 2 seconds,to minimize pollutant emissions.


Flue gas oxygen content control:Adjust the secondary air to ensure the oxygen content in the flue gas remains within a suitable range.




ACC++AI

Basic Automatic Combustion Control + Additional process systems + Advanced monitoring systems + Intelligent computing systems = ACC++ AI

Additional process systems
  • Exhaust gas recirculation/EGR
Advanced monitoring systems
  • Acoustic Temperature Measurement System
  • Flame condition assessment system
Intelligent Computing System
  • Cloud platform
  • MPC model predictive control
  • Neural network deep reinforcement learning
  • Fuzzy control
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