Method and system for training machine learning (ml) model for peak detection in flaring

A system comprising:

• a memory;
• and
• at least one processor communicatively coupled to the memory, wherein the at least one processor is configured to:
• receive historical flare data associated with one or more flare stacks over a predefined time period, wherein the historical flare data comprises at least one of mass or volume of gas flared within each of the one or more flare stacks, temperature of the gas flared, and pressure at which the gas is flared;
• train an artificial intelligence (AI)/machine learning (ML) model, based at least on the historical data, predefined definitions of flaring, and labeled flare data, wherein the labeled flare data correspond to tagging of peaks in flaring using a predefined value associated with the flaring;
• determine one or more peaks in the flaring using the trained AI/ML model, wherein the one or more peaks correspond to a maximum value of a peak exceeding a predefined threshold value of the peak for the predefined time period;
• identify one or more parameters associated with each of the one or more peaks;
• determine whether the one or more parameters associated with each of the one or more peaks satisfy predefined parameters;
• and
• deploy the trained AI/ML model for managing the flaring upon determining the one or more parameters associated with each of the one or more peaks satisfy the predefined parameters.

A non-transitory machine-readable information storage medium comprising one or more instructions which when executed by at least one processor cause the at least one processor to:

• receive historical flare data associated with one or more flare stacks over a predefined time period, wherein the historical flare data comprises at least one of mass or volume of gas flared within each of the one or more flare stacks, temperature of the gas flared, and pressure at which the gas is flared;
• train an artificial intelligence (AI)/machine learning (ML) model, based at least on the historical data, predefined definitions of flaring, and labeled flare data, wherein the labeled flare data correspond to tagging of peaks in flaring using a predefined value associated with the flaring;
• determine one or more peaks in the flaring using the trained AI/ML model, wherein the one or more peaks correspond to a maximum value of a peak exceeding a predefined threshold value of the peak for the predefined time period;
• identify one or more parameters associated with each of the one or more peaks;
• determine whether the one or more parameters associated with each of the one or more peaks satisfy predefined parameters;
• and
• deploy the trained AI/ML model for managing the flaring upon determining the one or more parameters associated with each of the one or more peaks satisfy the predefined parameters.