Honeywell International Inc.

Michael Niemiec

Christopher J Webb

Sanjay Kantilal Dave

Andrew John Trenchard

Sriram Hallihole

Mandar Subhash Vartak

Matthew G Grizzle

Greg Stewart

Viraj Srivastava

Lucy Ning Liu

Chris Webb

Matt Grizzle

Andrew John Trenchart

Mandar Vartak

Sanjay K Dave

Andrew Trenchard

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 method includes obtaining measurements associated with a plurality of controlled variables (CVs) and a plurality of manipulated variables (MVs). The method also includes detecting an irregular operating condition of a CV or an MV. The method also includes analyzing case specific operating conditions based on the irregular operating condition. The method further includes removing the irregular operating condition based on analyzing the case specific operating conditions.

Method and apparatus for real time model predictive control operator support in industrial process control and automation systems

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 of generating a hybrid model predictive control (MPC) simulation model for a plant configured to run a process that processes at least one raw material to generate at least one tangible product. A predictive dynamic MPC sub-model is provided for each of plurality of process units in the plant, the plant including at least one process controller coupled to field devices coupled to the plurality of process units, where the process units comprise equipment for converting the raw material or an intermediate material formed from the raw material into to another material. A piping network diagram is obtained that provides a representation of a piping network for routing of the raw material and the intermediate material during the process. The dynamic MPC sub-models are coupled together using the piping network to generate the hybrid MPC simulation model which models the plant as a whole.

Hybrid plant MPC model including dynamic MPC sub-models

A method of alarm notification includes providing data for an industrial process including stored alarm event data and stored Key Performance Indicator (KPI) data, and an alarm event-KPI correlation and operator notification system. Patterns of relationships are discovered between the stored alarm event and KPI data to provide a reference pattern database that identifies KPI violation events in the KPI data as a function of the alarm event data or alarm events in the alarm event data as a function of the KPI data. Pattern matching uses a real-time alarm event and real-time KPI data to determine a pattern similarity by comparing a current sequence spanning a predetermined time of the real-time alarm event and KPI data to the patterns. When the pattern matching identifies a current alarm event or KPI violation event, an operator is notified and a predicted KPI value or feature or a predicted alarm event.

Process performance issues and alarm notification using data analytics

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 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

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Dexter, MI, United States of America

Michael Niemiec