Methods, systems, and computer-readable media are described herein. One method embodiment includes determining an unscaled efficiency signal of combustion equipment using data measured from the combustion equipment, determining a theoretical efficiency signal of the combustion equipment using a theoretical efficiency surface of the combustion equipment and a subset of the measured data, and normalizing the unscaled efficiency signal using values from a correlated portion of the theoretical efficiency signal to monitor efficiency of the combustion equipment. Other embodiments can include providing a performance indicator of the combustion equipment in response to an operational mode change.

Real-time dynamic optimization of a process model in an online model-based process control computing environment. A mixed integer nonlinear programming (MINLP) solver utilizes grouping of first-principle model units to implement constraints of the underlying process. A group identifier parameter and a group complement parameter enable the grouping behavior through association with the first-principles model units.

A heat controlled oven system includes a plurality of oven levels, including an oven belt and gas burners; a gas flow network, including a gas supply line, a variable flow control valve, and on/off flow control valves; and a heat control unit, including a processor, a non-transitory memory, and input/output component, a heat modeler, a heat manager, a feedback controller, and a valve controller, such that the heat control unit is configured to calculate an estimated heat demand to adjust to a temperature set point, based on a heat model of the at least one oven level, and further calculates an optimized heat demand using a control loop feedback algorithm. Also disclosed is a method of heat calculation for an oven, including defining a heat model, calculating and optimizing the estimated heat demand, calculating and setting a variable valve position for the gas burners.

A method of reducing plant emissions includes providing a MPC model for a flaring process including one-to-one models between controlled variables (CVs) including a smoke count and/or a flare count (CV1) and a noise level (CV2), and flow of assist gas as a manipulated variable (MV) and another process gas flow as a disturbance variable (DV). The MPC model receives sensed flare-related parameters during the flaring process including a measure of CV1 (CV1*) and CV2 (CV2*). Provided CV1* is above a minimum setpoint for CV1 (CV1 setpoint) and CV2* is above a setpoint for CV2 (CV2 setpoint), the flaring process is automatically controlled using the MPC model which determines an updated flow setpoint for MV from CV1* and CV2*, the CV1 and CV2 error, and the identified one-to-one models.

A method of improving an endothermic process in a furnace utilizing steps a) calibrating the simplified physical model of step c3) by measuring one or more tube temperature for at least a tube impacted by the throttling of a burner in standard and in throttled state, b) acquiring information on a tube temperature for the tubes present in the furnace with all the burners present in the furnace under standard non-throttled conditions, c) getting a map of burners to throttle including c1) choosing at least one parameter representative of the performances of the furnace with a target of improvement, c2) choosing at least one or more power ratio for the burner throttling, c3) utilizing the information of step b) and a simplified physical model of the impact of throttling a burner on the tube temperature, c4) getting a map of burners to throttle, step d) throttling the burners.

A method includes obtaining gas management data from a dynamic sensor array disposed within a gas processing plant, the gas processing plant including a gas flaring system. The method further includes obtaining a set of gas management parameters, and determining, with a machine learning model, a predicted emission of the gas flaring system based on the gas management data. The method further includes determining, based on the predicted emission, an emission reduction strategy and adjusting, with a gas processing controller and a gas flaring controller, the set of gas management parameters to execute the emission reduction strategy. Executing the emission reduction strategy includes directing a portion of feed gas from the gas processing plant to the gas flaring system according to the adjusted set of gas management parameters and flaring, using the gas flaring system, the portion of the feed gas according to the adjusted set of gas management parameters.