Artificial intelligence-based optimal air damper control system and method for increasing energy efficiency of industrial boilers

An AI-based optimal air damper control system comprising:

• one or more processors configured to:
• collect industrial boiler operational data;
• calculate energy efficiency under a given control condition and an environment by extracting energy efficiency-related data from the collected industrial boiler operational data and analyzing a correlation between corresponding data;
• train an optimal air volume-for-load prediction model which is based on AI, by using the extracted data and the calculated energy efficiency as training data;
• and
• derive an air volume condition that results in peak energy efficiency under a given load, based on the trained optimal air volume-for-load prediction model, and to automatically control an air damper according to the corresponding air volume condition,
• wherein, for the controlling, the one or more processors are configured to, when using the trained optimal air volume-for-load prediction model, fix a quantity of fuel used, a temperature of feed water, a boiler pressure, and change only an air damper input value within an allowable range, and predict boiler efficiency according to a change in the air damper input value, and using an air damper input value based on which peak boiler efficiency is predicted for automatically controlling the air damper.

An AI-based optimal air damper control method comprising:

• training, by a system, an optimal air volume-for-load prediction model which is based on AI, by using energy efficiency-related data and a result of calculating energy efficiency as training data;
• and
• deriving, by the system, an air volume condition that results in peak energy efficiency under a given load, based on the trained optimal air volume-for-load prediction model, and automatically controlling an air damper according to the corresponding air volume condition,
• wherein the controlling comprises, when using the trained optimal air volume-for-load prediction model, fixing a quantity of fuel used, a temperature of feed water, a boiler pressure, and changing only an air damper input value within an allowable range, and predicting boiler efficiency according to a change in the air damper input value, and using an air damper input value based on which peak boiler efficiency is predicted for automatically controlling the air damper.