Abstract: Embodiments of the present disclosure provide for generation and use of multiple representations of a processing plant, for example a refinery plant. In some embodiments a first representation is utilized to visualize the component(s) and/or connection(s) of the processing plant, with a second representation utilized to process one or more aspect(s) of the processing plant. The first representation in some embodiments represents the physical components and/or connections between such components to enable intuitive visualization and monitoring of the components thereof. The second representation in some embodiments represents a virtualized representation of the physical components and/or connections between such components in a manner that is better processable via one or more computing process(es). Such processing is performable to generate data for controlling configuration and/or operation of component(s) in the processing plant.

Abstract: Embodiments of the present disclosure provide for generating at least one optimized operational parameter associated with a processing plant, such as an oil refinery. Embodiments of the disclosure utilize a multi-horizon optimization process, for example that generates optimized operational parameter(s) based on at least a long-horizon optimized plan and a short-horizon optimized plan. Some embodiments utilize the multiple horizons to generate the optimized operational parameter(s) via different process(es) based on configuration(s) and/or characteristic(s) of a processing plant. The resulting optimized operational parameter(s) enable operation of the processing plant during the immediate term in a manner that is optimized specifically for the processing plant in its particular configuration.

Abstract: A method includes receiving, at a master MPC controller from a slave MPC controller, proxy limit values indicating to what extent the slave controller is able to change multiple manipulated variables in multiple directions within a variable space without violating process variable constraints of the slave controller. The variable space includes a first feasibility region defined by the process variable constraints. The method also includes estimating a second feasibility region associated with the slave controller. At least part of the second feasibility region resides within the first feasibility region. The method further includes performing plantwide optimization at the master controller. A solution generated during the plantwide optimization includes a combination of manipulated variable values within the second feasibility region. Estimating the second feasibility region includes identifying edges of the second feasibility region based on the proxy limit values.

Abstract: A method includes receiving, at a master model predictive control (MPC) controller from a slave MPC controller, information indicating to what extent the slave MPC controller is able to change multiple manipulated variables in each of multiple directions within a variable space without violating process variable constraints of the slave MPC controller. The method also includes estimating a feasibility region associated with the slave MPC controller using the information, where the feasibility region identifies a portion of the variable space in which combinations of manipulated variable values satisfy the process variable constraints. In addition, the method includes performing plantwide optimization at the master MPC controller using the feasibility region, where a solution generated during the plantwide optimization includes one of the combinations of manipulated variable values within the feasibility region.

Abstract: Authentication based on a target uniform resource identifier (URI) via security proxies. A framework for creating, updating and deleting authentication groups according to a destination URI may be provided. Each of the authentication groups may have a corresponding adaptable authentication scheme. An access from a client to a server may be classified into an authentication group. An authentication request from the client to the server may be intercepted by an authentication scheme based on the authentication group. A session based cookie may be utilized for supporting access between the client and the server.

Abstract: A method includes identifying one of multiple regions in a range where an output (OP) value used to implement a manipulated variable is located. The manipulated variable is associated with an industrial process, and the OP value represents an output of a downstream controller. The method also includes calculating an achievable manipulated variable (MV) limit for the manipulated variable based on the region in which the OP value is located. For example, when the OP value is located in one region, the achievable MV limit could match a user-specified limit or be based on a gain between the OP value and a value of a process variable. When the OP value is located in another region, the achievable MV limit could track the value of the process variable with a gap.

Abstract: A method includes receiving, at a master MPC controller from a slave MPC controller, proxy limit values indicating to what extent the slave controller is able to change multiple manipulated variables in multiple directions within a variable space without violating process variable constraints of the slave controller. The variable space includes a first feasibility region defined by the process variable constraints. The method also includes estimating a second feasibility region associated with the slave controller. At least part of the second feasibility region resides within the first feasibility region. The method further includes performing plantwide optimization at the master controller. A solution generated during the plantwide optimization includes a combination of manipulated variable values within the second feasibility region. Estimating the second feasibility region includes identifying edges of the second feasibility region based on the proxy limit values.

Abstract: A method includes obtaining a planning model for an industrial facility at a master MPC controller and sending at least one optimization call from the master MPC controller to one or more slave MPC controllers. The method also includes receiving at least one proxy limit value from the slave MPC controller(s) in response to the at least one optimization call. The at least one proxy limit value identifies to what extent one or more process variables controlled by the slave MPC controller(s) are adjustable without violating any process variable constraints. In addition, the method includes performing plantwide optimization at the master MPC controller using the planning model and the at least one proxy limit value. The at least one proxy limit value allows the master MPC controller to honor the process variable constraints of the slave MPC controller(s) during the plantwide optimization.

Abstract: Authentication based on a target uniform resource identifier (URI) via security proxies. A framework for creating, updating and deleting authentication groups according to a destination URI may be provided. Each of the authentication groups may have a corresponding adaptable authentication scheme. An access from a client to a server may be classified into an authentication group. An authentication request from the client to the server may be intercepted by an authentication scheme based on the authentication group. A session based cookie may be utilized for supporting access between the client and the server.

Abstract: A method includes obtaining a planning model for an industrial facility at a master MPC controller and sending at least one optimization call from the master MPC controller to one or more slave MPC controllers. The method also includes receiving at least one proxy limit value from the slave MPC controller(s) in response to the at least one optimization call. The at least one proxy limit value identifies to what extent one or more process variables controlled by the slave MPC controller(s) are adjustable without violating any process variable constraints. In addition, the method includes performing plantwide optimization at the master MPC controller using the planning model and the at least one proxy limit value. The at least one proxy limit value allows the master MPC controller to honor the process variable constraints of the slave MPC controller(s) during the plantwide optimization.

Abstract: A method includes receiving, at a master model predictive control (MPC) controller from a slave MPC controller, information indicating to what extent the slave MPC controller is able to change multiple manipulated variables in each of multiple directions within a variable space without violating process variable constraints of the slave MPC controller. The method also includes estimating a feasibility region associated with the slave MPC controller using the information, where the feasibility region identifies a portion of the variable space in which combinations of manipulated variable values satisfy the process variable constraints. In addition, the method includes performing plantwide optimization at the master MPC controller using the feasibility region, where a solution generated during the plantwide optimization includes one of the combinations of manipulated variable values within the feasibility region.

Abstract: A method includes identifying a nonlinear model used by an optimizer to perform optimization operations associated with an industrial process to be controlled. The method also includes generating a Hessian matrix associated with the nonlinear model. The method further includes providing the Hessian matrix to an advanced process controller that uses the Hessian matrix to perform both process control and optimization operations. The Hessian matrix can approximate a nonlinear objective function. The method can also include providing one or more approximated nonlinear constraints, a solution of a quadratic sub-problem that approximates the nonlinear model, or a combination of multiple solutions of the quadratic sub-problem to the advanced process controller. The Hessian matrix can be updated and provided to the advanced process controller during each of multiple iterations.

Abstract: A method includes identifying a nonlinear model used by an optimizer to perform optimization operations associated with an industrial process to be controlled. The method also includes generating a Hessian matrix associated with the nonlinear model. The method further includes providing the Hessian matrix to an advanced process controller that uses the Hessian matrix to perform both process control and optimization operations. The Hessian matrix can approximate a nonlinear objective function. The method can also include providing one or more approximated nonlinear constraints, a solution of a quadratic sub-problem that approximates the nonlinear model, or a combination of multiple solutions of the quadratic sub-problem to the advanced process controller. The Hessian matrix can be updated and provided to the advanced process controller during each of multiple iterations.

Abstract: A method includes identifying one of multiple regions in a range where an output (OP) value used to implement a manipulated variable is located. The manipulated variable is associated with an industrial process, and the OP value represents an output of a downstream controller. The method also includes calculating an achievable manipulated variable (MV) limit for the manipulated variable based on the region in which the OP value is located. For example, when the OP value is located in one region, the achievable MV limit could match a user-specified limit or be based on a gain between the OP value and a value of a process variable. When the OP value is located in another region, the achievable MV limit could track the value of the process variable with a gap.

Abstract: A method includes obtaining a nonlinear process model modeling a nonlinear process to be controlled. The method also includes obtaining an objective function defining how the process is controlled. The method further includes obtaining a control model defining a dynamic feasible region associated with a controlled variable, where the controlled variable is associated with the process. In addition, the method includes controlling the nonlinear process by solving a control problem that includes the process model, control model, and objective function. The dynamic feasible region defined by the control model could represent a funnel region. The objective function could include terms for minimizing and optimizing adjustments to one or more manipulated variables associated with the process.

Abstract: A method includes receiving first input data from one or more first process control system components. The method also includes receiving second input data from one or more second process control system components. In addition, the method includes performing an iterative process that includes identifying one or more adjustments to at least one target quantity using the first input data, identifying one or more contribution values using the one or more adjustments, and identifying one or more estimated product yields using the one or more contribution values and the second input data. Each target quantity is associated with at least one intermediate or final product to be produced. Each contribution value is based on an intermediate product's contribution to each of multiple final products. Each estimated product yield is associated with an expected quantity of one of the intermediate and final products to be produced.

Abstract: A catalyst system that may regenerate while removing pollutants from an exhaust gas of an engine. The system may have a converter with multiple segments of chambers. At least one of the chambers may be regenerated while the remaining chambers are removing pollutants from the exhaust. The chambers may be rotated in turn for one-at-a-time regeneration. More than one chamber may be regenerated at a time to remove collected pollutants. The system may have plumbing and valves, and possibly mechanical movement of the chambers, within the system to effect the changing of a chamber for regeneration. The chambers connected to the exhaust may be in series or parallel. A particulate matter filter may be connected to the system, and it also may be regenerated to remove collected matter.

Abstract: A method includes obtaining a nonlinear process model modeling a nonlinear process to be controlled. The method also includes obtaining an objective function defining how the process is controlled. The method further includes obtaining a control model defining a dynamic feasible region associated with a controlled variable, where the controlled variable is associated with the process. In addition, the method includes controlling the nonlinear process by solving a control problem that includes the process model, control model, and objective function. The dynamic feasible region defined by the control model could represent a funnel region. The objective function could include terms for minimizing and optimizing adjustments to one or more manipulated variables associated with the process.

Abstract: A projection is associated with a first signal and a second signal. The second signal includes a first portion associated with the first signal and a second portion not associated with the first signal. The projection at least substantially separates the first portion of the second signal from the second portion of the second signal. One or more parameters of a model are identified using at least a portion of the projection. The model associates the first signal and the first portion of the second signal.

Abstract: Systems and methods for controlling automotive powertrains using a distributed control architecture are disclosed. A distributed control system may include a supervisory control unit for controlling one or more powertrain subsystems, and one or more subsystem control units in communication with the supervisory control unit. The supervisory control unit can be configured to execute a central optimization algorithm that computes variables from across multiple powertrain subsystems, and then outputs a number of globally approximated command values to each associated subsystem control unit. In some embodiments, the central optimization algorithm can be configured to solve a global cost function or optimization routine. One or more of the subsystem control units can be configured to execute a lower-level algorithm or routine, which can comprise a higher-fidelity model than that used by the central optimization algorithm.