Ventilation system with automatic flow balancing derived from a neural network and methods of use

Abstract

A ventilation system with automatic flow balancing derived from a neural network to consistently achieve a desired flow rate for inlet flow and/or outlet flow in various operating environments to optimize system performance. The system includes a ventilation device that includes an exhaust blower assembly with a blower motor and a control circuit having a mathematical equation derived from the use of the neural network. When the estimated exhaust blower flow is different than an exhaust flow set point, the exhaust control circuit selectively alters power supplied to the exhaust motor.

1 . A ventilation system with air flow modification system derived from a neural network, the ventilation system comprising:
 a ventilation device including:
 a first blower assembly including a blower motor, a first mathematical equation, and a pre-determined current limit for the blower motor; 
 wherein the first mathematical equation: (i) was created using a neural network to, and (ii) determines an estimated blower air flow for the first blower assembly; and 
 wherein a warning is provided to the user, when an air flow set point is set to a value that requires current supplied to the blower motor is greater than the pre-determined current limit for the blower motor. 
   
     
     
         2 . The ventilation system of  claim 1 , wherein the estimated blower air flow is within 5% of an air flow generated by the blower motor. 
     
     
         3 . The ventilation system of  claim 1 , further comprising a first damper operably associated with the first blower assembly and having a plurality of positional settings; and
 wherein the first mathematical equation is further configured to utilize the positional setting of the first damper in determining the estimated blower air flow for the first blower assembly.   
     
     
         4 . The ventilation system of  claim 3 , wherein the ventilation system is configured to control the positional setting of the first damper based on the air flow set point. 
     
     
         5 . The ventilation system of  claim 1 , wherein the ventilation device includes a plurality of mathematical equations configured to determine the estimated blower air flow for the first blower assembly; and
 wherein one mathematical equation of the plurality of mathematical equations is selected to be used to control the blower motor based upon a set of operating parameters.   
     
     
         6 . The ventilation system of  claim 5 , wherein the set of operating parameters includes density of the air external to the ventilation system. 
     
     
         7 . The ventilation system of  claim 5 , wherein the set of operating parameters includes a temperature of air external to the ventilation system. 
     
     
         8 . The ventilation system of  claim 5 , wherein the set of operating parameters includes a humidity of air external to the ventilation system. 
     
     
         9 . The ventilation system of  claim 5 , wherein the set of operating parameters include: (i) an identification of the type of an air filter installed in the ventilation system, (ii) inclusion of a heat recovery core within the ventilation system, (iv) inclusion of an air handler, or (v) inclusion of an HVAC. 
     
     
         10 . The ventilation system of  claim 1 , further includes a communication module that is capable of receiving an updated mathematical equation from a remote location; and
 wherein the ventilation device is capable of replacing the first mathematical equation with the updated mathematical equation   
     
     
         11 . A ventilation system with air flow modification system derived from a neural network, the ventilation system comprising:
 a first blower assembly including a blower motor and a control circuit, said control circuit having a plurality of mathematical equations configured to determine an estimated blower air flow for the first blower assembly;   wherein each of the plurality of mathematical equations include a set of air path parameters of the blower motor that are derived from the use of a neural network; and   wherein one mathematical equation of the plurality of mathematical equations is selected to be used to control the blower motor based upon a set of installation parameters.   
     
     
         12 . The ventilation system of  claim 11 , wherein the estimated blower air flow is within 5% of an air flow generated by the blower motor. 
     
     
         13 . The ventilation system of  claim 11 , further comprising a first damper operably associated with the first blower assembly and having a plurality of positional settings; and
 wherein the first mathematical equation is further configured to utilize the positional setting of the first damper in determining the estimated blower air flow for the first blower assembly.   
     
     
         14 . The ventilation system of  claim 13 , wherein the ventilation system is configured to control the positional setting of the first damper based on an air flow set point. 
     
     
         15 . The ventilation system of  claim 11 , wherein the control circuit further includes a current limit for the blower motor; and
 wherein a warning is provided to the user when an air flow set point is set to a value that requires the current supplied to the blower motor to be greater than the current limit.   
     
     
         16 . The ventilation system of  claim 11 , further includes a communication module that is capable of receiving an updated mathematical equation from a remote location;
 wherein the control circuit is capable of replacing the first mathematical equation with the updated mathematical equation; and   wherein the updated mathematical equation determines the estimated blower air flow for the first blower assembly based upon the following inputs: (i) updated air path parameters of the blower motor that are derived from the use of a neural network and (ii) blower motor current.   
     
     
         17 . A ventilation system with air flow modification system derived from a neural network, the ventilation system comprising:
 a first blower assembly including: (i) a blower motor, (ii) a control circuit, and (iii) communication module, said control circuit having a first mathematical equation derived from the use of a neural network;   wherein the communication module is capable of receiving an updated mathematical equation derived from the use of the neural network from a remote location;   wherein the control circuit is capable of replacing the first mathematical equation with the updated mathematical equation.   
     
     
         18 . The ventilation system of  17 , further comprising a first damper operably associated with the first blower assembly and having a plurality of positional settings; and
 wherein the first mathematical equation is further configured to utilize the positional setting of the first damper in determining the estimated blower air flow for the first blower assembly.   
     
     
         19 . The ventilation system of  claim 17 , wherein the control circuit is configured to calculate the energy utilized by the ventilation system over pre-determined amount of time. 
     
     
         20 . The ventilation system of  claim 17 , wherein the control circuit is configured to calculate a projection for the amount of energy over a pre-determined amount of time.