Honeywell International Inc.

Joseph Z Lu

Charles Q Zhan

Tariq Samad

Syed M Shahed

John Duane Morningred

Michael L Rhodes

Gregory Edward Stewart

Vladimir Havlena

Christopher J Webb

Jeffrey G Renfro

John A Escarcega

Vladimir Ravlena

Kiran Raviprakash

Samad Tariq

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

Apparatus and method for managing industrial process optimization related to batch operations

Constraints are received on initial components and intermediate components. Information is received on the products to be produced including a quantity of each of the products to be produced and a specification that specifies how the intermediate components are to be combined to form each of the products. An optimization is performed that includes the continuous conversion of initial components into the intermediate components as well as subsequent production of the products, subject to the constraints on each of the initial components, the constraints on each of the intermediate components, and the quantity of each of the products to be produced.

Plant-wide optimization including batch operations

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. At least some of the edges meet at locations away from the proxy limit values.

Apparatus and method for adjustable identification of controller feasibility regions to support cascaded model predictive control (MPC)

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.

Apparatus and method for calculating proxy limits to support cascaded model predictive control (MPC)

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.

Apparatus and method for predicting windup and improving process control in an industrial process control system

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.

Cascaded model predictive control (MPC) approach for plantwide control and optimization

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. During each iteration, the industrial process can be adjusted to move an operating point of the industrial process towards an optimal ending position.

Apparatus and method for real-time sequential quadratic programming in industrial process control systems

A system includes a first controller configured to monitor processes associated with a process facility and to generate control data for optimizing the process facility. The system also includes a second controller configured to use stochastic calculus of variations calculations to modify at least a portion of the control data. The second controller is configured to provide discrete supply chain control and optimization for the process facility and to track at least one of demand requirements and shipping requirements for product manufactured by a discrete supply chain manufacturing process. The system further includes third controllers configured to operate in accordance with the control data to control the processes and to optimize the process facility.

System and method for discrete supply chain control and optimization using model predictive control

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. Controlling the nonlinear process could include performing simultaneous control and optimization, where adjustments to the one or more manipulated variables are chosen to meet the control objectives and possibly to optimize and minimize the adjustments.

Apparatus and method for model predictive control (MPC) of a nonlinear process

A system includes a first controller configured to monitor processes associated with a process facility and to generate control data for optimizing the process facility. The system also includes a second controller configured to use stochastic calculus of variations calculations to modify at least a portion of the control data and provide one of (i) continuous supply chain control and optimization for the process facility and (ii) discrete supply chain control and optimization for the process facility. The system further includes third controllers configured to operate in accordance with the control data to control the processes and to optimize the process facility.

System and method for continuous supply chain control and optimization using stochastic calculus of variations approach

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.

Exhaust catalyst system

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.

Apparatus and method for modeling relationships between signals

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.

Distributed control architecture for powertrains

A method includes identifying a signal and a disturbance associated with a model. The signal and disturbance are identified using historical data associated with one or more process variables. The method also includes decomposing the signal and the disturbance at a plurality of resolution levels. The method further includes extracting a plurality of data segments from the signal using the decomposed signal and the decomposed disturbance. In addition, the method includes determining a quality of the model using the extracted data segments and at least a portion of the historical data.

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Glendale, AZ, United States of America

Joseph Z Lu