Honeywell International Inc.

Sriram Hallihole

Sanjay Kantilal Dave

Mandar Subhash Vartak

Andrew John Trenchard

Christopher J Webb

Michael Niemiec

Matthew G Grizzle

Ranganathan Srinivasan

Madhukar Madhavamurthy Gundappa

J Ward MacArthur

Gobinath Pandurangan

Lucy Ning Liu

Ward MacArthur

Sujit Gaikwad

Sachi Dash

Rajni Jain

Venkata Krishna Prasad Reddy Vurubindi

S Chandrakanth Vittal

Chris Webb

Avijit Das

Sreesathya Sathyabhama Sreekantan

Roshan Yohannan

Sanjay Venugopal

Matt Grizzle

Venkata K P Reddy Vurubindi

Murugesh Palanisamy

Michael Niemeic

Andrew John Trenchart

Mandar Vartak

A method includes obtaining data identifying values of one or more process variables associated with an industrial process controller and identifying one or more constraint violations using the data. The method also includes, for each identified constraint violation, analyzing a behavior of the controller, a behavior of an industrial process being controlled, and how the controller was being used by at least one operator for a period of time. At least part of the period of time is prior to the identified constraint violation. The method further includes generating a graphical display based on the analysis, where the graphical display identifies one or more probable causes for at least one of the one or more constraint violations.

Apparatus and method for automated identification and diagnosis of constraint violations

A field device, method, and non-transitory computer readable medium provide for cost of lost opportunity to operators in real-time using an advance process control. The field device includes a memory and a processor operably connected to the memory. The processor receives current values and average values for controlled variables and manipulated variables; determines costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and stores the costs of lost opportunity for the field device.

Method and system to provide cost of lost opportunity to operators in real time using advance process control

A method includes obtaining data associated with operation of an industrial process controller and identifying impacts of operational problems of the industrial process controller. The method also includes generating a graphical display for a user, where the graphical display presents one or more recommended actions to reduce or eliminate at least one of the impacts of at least one of the operational problems. The method further includes triggering at least one of the one or more recommended actions based on input from the user. The method could also include executing one or more analytic algorithms to process the obtained data and identify the operational problems of the industrial process controller. Each of the one or more analytic algorithms could be instantiated as a container, and multiple containers could be instantiated and executed as needed. Results of executing the one or more analytic algorithms could be transformed into a standard format.

Apparatus and method for identifying, visualizing, and triggering workflows from auto-suggested actions to reclaim lost benefits of model-based industrial process controllers

A method includes obtaining data associated with operation of a model-based industrial process controller. The method also includes identifying at least one estimated impact of at least one operational problem of the industrial process controller, where each estimated impact is expressed in terms of a lost opportunity associated with operation of the industrial process controller. The method further includes presenting the at least one estimated impact to a user. The at least one estimated impact could include impacts associated with noise or variance in process variables used by the industrial process controller, misconfiguration of an optimizer in the industrial process controller, one or more limits on one or more process variables, a quality of at least one model used by the industrial process controller, a quality of one or more inferred properties used by the industrial process controller, or one or more process variables being dropped from use by the industrial process controller.

Apparatus and method for estimating impacts of operational problems in advanced control operations for industrial control systems

A method includes identifying one or more proxy limit variables to be used to define an operating envelope associated with an industrial process controller. The industrial process controller is associated with a number of process variables including the proxy limit variable(s). The method also includes maximizing and minimizing at least one of the proxy limit variable(s). The method further includes presenting maximum and minimum values of the at least one of the proxy limit variable(s) as the operating envelope in a graphical user interface to at least one user. A single proxy limit variable could be used, and maximum and minimum values of the single proxy limit variable can be presented in a one-dimensional representation of the operating envelope. Multiple proxy limit variables could also be used, and maximum and minimum values of the proxy limit variables can be presented in a multi-dimensional representation of the operating envelope.

Apparatus and method for using advanced process control to define real-time or near real-time operating envelope

A method includes obtaining data identifying values of one or more controlled variables associated with an industrial process controller. The method also includes identifying periods when at least one of the one or more controlled variables has been moved to an associated limit by the controller. The method further includes, for each identified period, (i) identifying a standard deviation of predicted values for the associated controlled variable and (ii) determining a control giveaway value for the associated controlled variable based on the standard deviation. The control giveaway value is associated with an offset between the associated controlled variable's average value and the associated limit. In addition, the method includes identifying variances in the one or more controlled variables using the control giveaway values and generating a graphical display identifying one or more impacts or causes for at least some of the variances.

Apparatus and method for identifying impacts and causes of variability or control giveaway on model-based controller performance

A method includes retrieving process variable values from a multivariable predictive controller. The multivariable predictive controller is executed on an embedded platform and is configured to control an industrial process. The method also includes simulating how the multivariable predictive controller would attempt to control the industrial process based on the process variable values. The method further includes transmitting simulated process variable values to the multivariable predictive controller.

Apparatus and method for performing process simulations for embedded multivariable predictive controllers in industrial process control and automation systems

A method of dynamic model selection for hybrid linear/non-linear process control includes developing a plurality of process models including at least one linear process model and at least one non-linear process model from inputs including dynamic process data from a processing system that runs a physical process. At least two of the plurality of process models are selected based on a performance comparison based on at least one metric, wherein the selected process models number less than a number of the plurality of process models received. A multi-model controller is generated that includes the selected process models. The physical process is simulated using the multi-model controller by applying the selected process models to obtain closed loop performance test data for each of the selected models. The performance test data is compared. A selected process model is then selected.

Dynamic model generation for implementing hybrid linear/non-linear controller

A system includes a process controller and an equation evaluation apparatus. The equation evaluation apparatus includes an equation editor, a model factory, and an equation evaluation engine. The equation editor is adapted to receive equations describing a process to be controlled by the process controller. The equation editor is also adapted to generate model information representing the equations. The model factory is adapted to receive the model information and generate an equation stack representing the equations. The equation evaluation engine is adapted to receive evaluation information from the process controller, evaluate at least one of the equations using the evaluation information and the equation stack, and send a result of the evaluation to the process controller. The model information could include information representing algebraic equations, differential equations, algebraic states, differential states, inputs, parameters, constants, and/or expressions.

System and method for solving chemical engineering equations and model development using equation editor

A model predictive controller (MPC) for controlling physical processes includes a non-linear control section that includes a memory that stores a non-linear (NL) model that is coupled to a linearizer that provides at least one linearized model, and a linear control section that includes a memory that stores a linear model. A controller engine is coupled to receive both the linearized model and linear model. The MPC includes a switch that in one position causes the controller engine to operate in a linear mode utilizing the linear model to implement linear process control and in another position causes the controller engine to operate in a NL mode utilizing the linearized model to implement NL process control. The switch can be an automatic switch configured for automatically switching between linear process control and NL process control.

Integrated linear/non-linear hybrid process controller

A method and system for process control. The control system can be operably coupled to a processing system. The control system can include control devices operably coupled to the processing system; a modeling module to provide a linear model based at least in part on the processing system; a computational module to provide controller algorithms associated with the control devices; a user interface module to present at a user interface controller information based at least in part on the linear model and the controller algorithms; and a separate coordination module for establishing communication between the modeling module, the computational module and the user interface module. One or more control signals can be provided to at least one of the control devices for controlling the processing system. In one embodiment, the modeling module can generate the linear model from a non-linear process.

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Bengaluru, India

Sriram Hallihole