Artificial intelligence-based optimal air damper control system and method for increasing energy efficiency of industrial boilers
There is provided an AI-based air damper control system and method for industrial boilers. An AI-based optimal air damper control method according to an embodiment calculates energy efficiency under a given control condition and an environment by extracting energy efficiency-related data from industrial boiler operational data and analyzing a correlation between corresponding data, trains an AI-based optimal air volume-for-load prediction model by using the extracted data and the calculated energy efficiency as training data, and derives 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 controls the air damper according to the corresponding air volume condition.
1 . An AI-based optimal air damper control method comprising:
collecting, by a system, industrial boiler operational data;
calculating, by the system, 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;
training, by the system, 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
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.
2 . The AI-based optimal air damper control method of claim 1 , wherein the collected industrial boiler operational data includes a quantity of feed water, a temperature of feed water, a quantity of fuel used, a boiler pressure, an exhaust gas NOx, O 2 , an exhaust gas temperature, an air damper input value, and a fuel damper input value.
3 . The AI-based optimal air damper control method of claim 1 , wherein the calculating the energy efficiency comprises calculating the energy efficiency by referring to Equation 2 presented below:
Boiler
Efficiency
=
Heat
Output
Heat
Input
×
100
=
Quantity
of
Feed
Water
×
(
Enthalpy
of
Steam
-
Temperature
of
Feed
Water
)
Calorific
Value
of
Fuel
×
Quantity
of
Fuel
used
×
100
Equation
2
4 . The AI-based optimal air damper control method of claim 3 , wherein the calculating the energy efficiency comprises, when energy efficiency is calculated by referring to Equation 2 above, using a heat transfer value for a boiler pressure in a saturated steam table as the enthalpy of steam, and using a higher heating value of fuel used by the boiler as the calorific value of fuel.
5 . The AI-based optimal air damper control method of claim 3 , wherein the training comprises, when training the optimal air volume-for-load prediction model, using, as training data, energy efficiency-related data including a boiler pressure, a quantity of fuel used (load), a temperature of feed water, an air damper input value, and the calculated energy efficiency (boiler efficiency).
6 . The AI-based optimal air damper control method of claim 5 , wherein the training comprises, when performing pre-processing on the training data, determining, as an abnormal data value, an efficiency value that is calculated when a boiler is turned off after water is drained off from a boiler water tank and steam is generated, and water supply is late, and excluding the abnormal efficiency value from the training data.
7 . The AI-based optimal air damper control method of claim 5 , wherein the optimal air volume-for-load prediction model is configured to construct an artificial neural network that is comprised of three hidden layers and four neurons per hidden layer, and to use an exponential linear unit (ELU) as an activation function.
8 . The AI-based optimal air damper control method of claim 1 , wherein the system does not perform the collecting, the calculating, and the training on a one-time basis, and, when new industrial boiler operational data is collected, periodically refines the optimal air volume-for-load prediction model by adding the new industrial boiler operational data to the training data.
9 . 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.
10 . The AI-based optimal air damper control system of claim 9 , wherein the collected industrial boiler operational data includes a quantity of feed water, a temperature of feed water, a quantity of fuel used, a boiler pressure, an exhaust gas NOx, O2, an exhaust gas temperature, an air damper input value, and a fuel damper input value.
11 . The AI-based optimal air damper control system of claim 9 , wherein, for the calculating the energy efficiency, the one or more processors are configured to calculate the energy efficiency by referring to Equation 2 presented below:
Boiler
Efficiency
=
Heat
Output
Heat
Input
×
100
=
Quantity
of
Feed
Water
×
(
Enthalpy
of
Steam
-
Temperature
of
Feed
Water
)
Calorific
Value
of
Fuel
×
Quantity
of
Fuel
used
×
100
Equation
2
12 . The AI-based optimal air damper control system of claim 11 , wherein, for the calculating the energy efficiency, the one or more processors are configured to, when energy efficiency is calculated by referring to Equation 2 above, use a heat transfer value for a boiler pressure in a saturated steam table as the enthalpy of steam, and use a higher heating value of fuel used by the boiler as the calorific value of fuel.
13 . The AI-based optimal air damper control system of claim 11 , wherein, for the training, the one or more processors are configured to, when training the optimal air volume-for-load prediction model, use, as training data, energy efficiency-related data including a boiler pressure, a quantity of fuel used (load), a temperature of feed water, an air damper input value, and the calculated energy efficiency (boiler efficiency).
14 . The AI-based optimal air damper control system of claim 13 , wherein, for the training, the one or more processors are configured to, when performing pre-processing on the training data, determine, as an abnormal data value, an efficiency value that is calculated when a boiler is turned off after water is drained off from a boiler water tank and steam is generated, and water supply is late, and exclude the abnormal efficiency value from the training data.
15 . The AI-based optimal air damper control system of claim 13 , wherein the optimal air volume-for-load prediction model is configured to construct an artificial neural network that is comprised of three hidden layers and four neurons per hidden layer, and to use an exponential linear unit (ELU) as an activation function.
16 . 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.
17 . The AI-based optimal air damper control system of claim 9 , wherein the system does not perform the collecting, the calculating, and the training on a one-time basis, and, when new industrial boiler operational data is collected, periodically refines the optimal air volume-for-load prediction model by adding the new industrial boiler operational data to the training data.