Abstract: A control system for automatic operation of a coker includes a drum feeder operable to modulate a feed of oil into a coke drum of the coker and a controller. The controller is configured to obtain an objective function that defines a control objective as a function of one or more controlled variables affected by modulating the feed of oil into the coke drum and use a predictive model and the objective function to generate a target coker feed rate indicating a target rate at which to feed the oil into the coke drum. The predictive model is configured to predict values of the one or more controlled variables predicted to result from the target coker feed rate. The controller is configured to operate the drum feeder using the target coker feed rate to modulate the feed of oil into the coke drum.

Abstract: A control system for automatic operation of a coker includes a drum feeder operable to modulate a feed of oil into a coke drum of the coker and a controller. The controller is configured to obtain an objective function that defines a control objective as a function of one or more controlled variables affected by modulating the feed of oil into the coke drum and use a predictive model and the objective function to generate a target coker feed rate indicating a target rate at which to feed the oil into the coke drum. The predictive model is configured to predict values of the one or more controlled variables predicted to result from the target coker feed rate. The controller is configured to operate the drum feeder using the target coker feed rate to modulate the feed of oil into the coke drum.

Abstract: A method of generating a controller for a continuous process. The method includes receiving from a storage memory, off-line stored values of one or more controlled variables and one or more manipulated variables of the continuous process over a plurality of time points. The off-line stored values are used to train a first neural network to operate as a predictor of the controlled variables. Then, the method includes training a second neural network to operate as a controller of the continuous process using the first neural network after it was trained to operate as the predictor for the continuous process and employing the second neural network as a controller of the continuous process.

Abstract: A control system for automatic operation of a coker, the control system. The control system includes a drum feeder operable to modulate a feed of oil into a coke drum of the coker. The control system further includes a controller with a processing circuit. The processing circuit obtains a target coke rate indicating a target rate at which to accumulate coke within the coke drum. The processing circuit further uses a neural network model to generate a target coker feed rate predicted to result in the coke accumulating within the coke drum at the target coke rate. The target coker feed rate indicates a target rate at which to feed the oil into the coke drum. The processing circuit further operates the drum feeder using the target coker feed rate to modulate the feed of oil into the coke drum.

Abstract: A predictive control system includes controllable equipment and a controller. The controller is configured to use a neural network model to predict values of controlled variables predicted to result from operating the controllable equipment in accordance with corresponding values of manipulated variables, use the values of the controlled variables predicted by the neural network model to evaluate an objective function that defines a control objective as a function of at least the controlled variables, perform a predictive optimization process to generate optimal values of the manipulated variables for a plurality of time steps in an optimization period using the neural network model and the objective function, and operate the controllable equipment by providing the controllable equipment with control signals based on the optimal values of the manipulated variables generated by performing the predictive optimization process.

Abstract: A method of generating a controller for a continuous process. The method includes receiving from a storage memory, off-line stored values of one or more controlled variables and one or more manipulated variables of the continuous process over a plurality of time points. The off-line stored values are used to train a first neural network to operate as a predictor of the controlled variables. Then, the method includes training a second neural network to operate as a controller of the continuous process using the first neural network after it was trained to operate as the predictor for the continuous process and employing the second neural network as a controller of the continuous process.

Abstract: A control system for automatic operation of a hydrocracking unit is shown. The control system includes a control device operable to affect at least one of a yield or a product quality of one or more output oil products provided as outputs of the hydrocracking unit. The control system further includes a controller configured to obtain an objective function that quantifies a value of operating the hydrocracking unit as a function of at least one of the yield or the product quality of the one or more output oil products, use a neural network model to generate a target control device setpoint predicted to optimize the objective function when the hydrocracking unit operates at the target control device setpoint, and operate the control device using the target control device setpoint to modulate at least one of the yield or the product quality of the one or more output oil products.

Abstract: A control system for automatic operation of a fluid catalytic cracking unit is shown. The control system includes a reactor severity control device operable to modulate a temperature affecting volume gain within the fluid catalytic cracking unit and a controller. The controller includes a processing circuit configured to calculate the volume gain within the fluid catalytic cracking unit by comparing a volume based on one or more input oil feeds to the fluid catalytic cracking unit to a volume of one or more output oil products of the fluid catalytic cracking unit. The processing circuit is further configured to use a neural network model to generate a target severity predicted to optimize the volume gain within the fluid catalytic cracking unit. The processing circuit is further configured to operate the reactor severity control device using the target severity to modulate the temperature affecting the volume gain within the fluid catalytic cracking unit.

Abstract: A controller for a plant that exhibits nonlinear dynamics includes one or more processors and memory storing instructions that cause the one or more processors to perform operations. The operations include training a neural network model during an offline learning period using historical plant data representing a plurality of different historical states of the plant and using the neural network model during online operation of the plant to generate a linear predictor as a function of a current state of the plant, the linear predictor defining a linearization of the nonlinear dynamics localized at the current state of the plant. The controller controls equipment that operate to affect the current state of the plant by performing a predictive control process that uses the linear predictor to generate values of one or more manipulated variables provided as inputs to the equipment.

Abstract: A control system for automatic operation of a coker, the control system. The control system includes a drum feeder operable to modulate a feed of oil into a coke drum of the coker. The control system further includes a controller with a processing circuit. The processing circuit obtains a target coke rate indicating a target rate at which to accumulate coke within the coke drum. The processing circuit further uses a neural network model to generate a target coker feed rate predicted to result in the coke accumulating within the coke drum at the target coke rate. The target coker feed rate indicates a target rate at which to feed the oil into the coke drum. The processing circuit further operates the drum feeder using the target coker feed rate to modulate the feed of oil into the coke drum.