Emission monitoring and control of flare systems
In an embodiment, a method of controlling flaring of a combustion gas including a flare gas, a supplemental fuel gas, and an assist gas is provided. Models estimating, based on flow rates and in-situ speed of sound measurements in the gases, net heating value of the combustion gas within a flare combustion zone, combustion efficiency of the combustion gas, and smoke yield of the combustion gas are maintained. The method also includes receiving measurements of the gas flow rates and determining set points for flow rates of the fuel gas and/or the assist gas based upon the models that achieve a target combustion efficiency. When a difference between a determined set point and its corresponding flow rate for the fuel gas and/or the assist gas is greater than a corresponding predetermined tolerance amount, that flow rate can be adjusted to reduce the determined difference below the predetermined tolerance amount.
1. A method of controlling a flare, comprising:
maintaining, by a memory:
a first model configured to estimate a net heating value of a combustion gas within a combustion zone of a flare based on one or more operating parameters comprising measured volumetric flow rates of a flare gas, a supplemental fuel gas, and an assist gas;
a second model configured to estimate a combustion efficiency of the combustion gas based on the one or more operating parameters; and
a third model configured to estimate a smoke yield resulting from combustion of the combustion gas based on the one or more operating parameters; and;
performing, by at least one processor in communication with the memory, a plurality of operations including:
receiving the one or more operating parameters;
receiving, from the memory, the first, second, and third models;
determining a set point for respective volumetric flow rates of at least one of the supplemental fuel gas or the assist gas, based upon the first model, the second model, and the third model, that achieves a target combustion efficiency; and
determining a difference between each determined set point and its corresponding measured volumetric flow rate for the supplemental fuel gas and the assist gas,
wherein, responsive to the difference being greater than a predetermined tolerance amount, the at least one processor is further configured to output at least one command signal operative to command a flow regulator to adjust the volumetric flow rate of at least one of the supplemental fuel gas, or the assist gas to reduce the difference below the predetermined tolerance amount.
2. The method of claim 1 , wherein the assist gas is steam or air.
3. The method of claim 1 , wherein the target combustion efficiency at the determined set points is a maximum combustion efficiency.
4. The method of claim 3 , wherein at least one of the estimated smoke yield and the estimated net heating value at the determined set points satisfies a predetermined constraint.
5. The method of claim 3 , wherein at least one of the volumetric flow rate of the supplemental fuel gas or the volumetric flow rate of the assist gas is constrained within a predetermined range.
6. The method of claim 1 , wherein the one or more operating parameters further comprise a speed of sound in the flare gas, a pressure of the flare gas, and a temperature of the flare gas.
7. The method of claim 1 , wherein the second and third models are further configured to use the estimated net heating value estimated by the first model.
8. The method of claim 1 , wherein the third model is selected, by the at least one processor, from an empirical model or a non-parametric machine learning model.
9. A system for controlling a flare, comprising:
a memory configured to maintain:
a first model configured to estimate a net heating value of a combustion gas within a combustion zone of a flare based on one or more operating parameters comprising measured volumetric flow rates of a flare gas, a supplemental fuel gas, and an assist gas;
a second model configured to estimate a combustion efficiency of the combustion gas based on the one or more operating parameters; and
a third model configured to estimate a smoke yield resulting from combustion of the combustion gas based on the one or more operating parameters; and
at least one processor in communication with the memory and configured to perform a plurality of operations including:
receiving the one or more operating parameters;
receiving the first, second, and third models from the memory;
determining a set point for respective volumetric flow rates of at least one of the supplemental fuel gas or the assist gas, based upon the first model, the second model, and the third model, that achieves a target combustion efficiency; and
determining a difference between each determined set point and its corresponding measured volumetric flow rate for the supplemental fuel gas and the assist gas,
wherein, responsive to the difference being greater than a predetermined tolerance amount, the at least one processor is further configured to output at least one command signal operative to command a flow regulator to adjust the volumetric flow rate of at least one of the supplemental fuel gas or the assist gas to reduce the difference below the predetermined tolerance amount.
10. The system of claim 9 , wherein the assist gas is steam or air.
11. The system of claim 9 , wherein the target combustion efficiency at the determined set points a maximum combustion efficiency.
12. The system of claim 11 , wherein at least one of the estimated smoke yield and the estimated net heating value at the determined set points satisfies a predetermined constraint.
13. The system of claim 11 , wherein at least one of the volumetric flow rate of the supplemental fuel gas or the volumetric flow rate of the assist gas is constrained within a predetermined range.
14. The system of claim 9 , wherein the one or more operating parameters further comprise a speed of sound in the flare gas, a pressure of the flare gas, and a temperature of the flare gas.
15. The system of claim 9 , wherein the second and third models are further configured to use the estimated net heating value estimated by the first model.
16. The system of claim 9 , wherein the third model is selected, by the at least one processor, from an empirical model or a non-parametric machine learning model.