Abstract: A method of tinting liquids in an industrial mixing system. A color measurement unit receives a liquid batch sample and measures the sample to generate a color measurement value. A color digitization processor receives the color measurement value and executes an artificial intelligence (AI) tinting prediction engine. The AI tinting prediction engine stores the color measurement value in a database, which contains a plurality of historical batch sample color information, a plurality of target standard sample color measurement values, and a plurality of colorant information. The AI tinting prediction engine then defines delta errors between the color measurement value and a target standard sample measurement value. The AI tinting prediction engine then generates predicted colorants and colorant masses based on the delta errors and information stored within the database. The predicted colorants are then transmitted to the mixing system and mixed into the liquid batch.

Abstract: Systems and methods for detecting and predicting faults in an industrial process automation system use trend data to forecast alerts and allow action to be taken before a problem occurs. The systems and methods provide fault/failure predictions that improve over time as more empirical data is collected for a related set of system components. The systems and methods may identify relationships among the components of a process automation system; identify and collect changes to system configuration; identify and collect data to inform reliability and predictive models; develop a domain-specific predictive model for one or more components that allows for component-based failure or degradation prediction; develop a system-predictive model that leverages reliability and criticality relationships, component-based predictions and operating parameters to predict the health of a part of or the entire process automation system; deliver a prioritized alert system; and identify root-cause failures of a component.

Abstract: Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

Abstract: A virtual plant operator system for use in an industrial plant. A data aggregator monitors operating data within the industrial plant and sends the operating data as a current state of the industrial plant to an operator assistant. The operator assistant includes a digital twin of the industrial plant and an artificial intelligence engine. The digital twin of the industrial plant receives the current state and simulates plant operations based on the current state. The artificial intelligence engine has at least one machine-learned model. The machine-learned model processes the simulated plant operations based on the current state to determine a recommendation output. The recommendation output includes stabilizing actions to the plant operations in the industrial plant and/or a predicted degree of shutdown responsive to each of the stabilizing actions.

Abstract: A machine-learned method and system for use in operating an industrial plant. A digital twin of the industrial plant is configured to simulate plant operations based on operating variables from a data store. A machine-learned model comprises a stabilizing agent and a disrupting agent. The stabilizing agent modifies the operating variables within the digital twin to perform a stabilizing action for limiting a degree of shutdown and the disrupting agent modifies the operating variables within the digital twin to perform a disruptive action for increasing the degree of shutdown. A composite action reward is configured to reward the machine-learned model for reducing the degree of shutdown from an initial state of the digital twin to a post-action-state of the digital twin.

Abstract: A smart controller continuously monitors data for determining in real time if there is a likelihood that an asset will be shut down and, if so, recommends a new setpoint at which the asset will continue to operate but will not lead to tripping of the system. The smart controller executes a simulation engine to test the new setpoint before implementation to optimize performance while avoiding a shutdown. In this manner, the smart controller provides early warning of possible shutdowns and ensures that key assets are less likely to be shut down.

Abstract: In a method of automating engineering design a knowledge base (KB) is queried for a template to map with new control loop (CL) data of a new CL that was identified in new digitized design data for a new engineering project, the query including the new CL data. The KB is trained to map past CL data of past CLs identified in past digitized design data from past engineering projects to respective templates based on past instantiation of the respective templates with the past CLs by the past engineering projects. The method further includes receiving a selected template in response to the query, wherein the selected template is selected based on its mapping with past CL data that matches the new CL data, and providing configuration data, including an instantiation of the selected template with the new CL data, for implementation of the new CL in an engineering system.

Abstract: A method of automating engineering design is provided. The method includes receiving a training set including pairings of control loop data for respective control loops identified in digitized design data and templates that were instantiated using the control loop data of the respective control loops and training, using machine learning, a knowledge base, based on the training set. The knowledge base, once trained, is configured to be queried with digitized new control loop data, predict a template to pair with the digitized new control loop data, and the predicted template, and the predicted template is configured to be instantiated with the new control loop data for implementation of a control loop in an engineering system.

Abstract: Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.

Abstract: Systems and methods for detecting and predicting faults in an industrial process automation system use trend data to forecast alerts and allow action to be taken before a problem occurs. The systems and methods provide fault/failure predictions that improve over time as more empirical data is collected for a related set of system components. The systems and methods may identify relationships among the components of a process automation system; identify and collect changes to system configuration; identify and collect data to inform reliability and predictive models; develop a domain-specific predictive model for one or more components that allows for component-based failure or degradation prediction; develop a system-predictive model that leverages reliability and criticality relationships, component-based predictions and operating parameters to predict the health of a part of or the entire process automation system; deliver a prioritized alert system; and identify root-cause failures of a component.

Abstract: Systems and methods for controlling industrial an industrial plant comprise: inputting an engineering diagram for a unit of the industrial plant, the engineering diagram including symbols representing assets of the industrial plant; extracting one or more assets from the engineering diagram using machine learning to recognize the one or more assets, the one or more assets including equipment, instruments, connectors, and lines, the lines relating the equipment, instruments, and connectors to one another; determining one or more relationships between the equipment, instruments, connectors, and lines to one another using machine learning to recognize the one or more relationships; and creating a flow graph from the equipment, instruments, connectors, and lines and the relationships between the equipment, instruments, connectors, and lines.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.

Abstract: A method of automatic manufacturability evaluation of plastic models comprises generation of a likely pulling direction, recognition of common features on plastic parts, and then applying manufacturability rules The manufacturability rules can be specified and customized through user specified rule parameters and depend upon the geometric parameters of the recognized features. A system comprises a user interface for selection and customization of DFX (Design for ‘X’) rules for evaluation of a design. The system includes a user interface integrated with a CAD system for receiving the CAD data and displaying the results to the user. Geometry analysis engines are integrated into the system, for extracting the various features and corresponding parameters required as input to the manufacturability rules. The system further involves extensible interfaces for rules and analysis engines which allows users to write their own customized rules and engines and integrate these into the CAD based DFX evaluation system.

Abstract: A system and method for authenticating a client having a privilege server, a head end server, and a web adapter performs the steps of negotiating an authentication scheme between the server proxy and the privilege server. User information is presented to the web adapter. The user information is provided to the head end server and in turn presents the information to the web adapter. The user is validated in accordance with the authentication scheme. When the user is validated a ticket is generated for the user. The ticket is presented to the client privilege server proxy that decrypts the ticket. A token is formed from the ticket and the client user identification. The token from the client is provided to the privilege server. A packet is formed having a sequence number and session key encrypted with the ticket. The packet is provided to the head end server which in turn authenticates the user.

Abstract: A system and method for authenticating a client having a privilege server, a head end server, and a web adapter performs the steps of negotiating an authentication scheme between the server proxy and the privilege server. User information is presented to the web adapter. The user information is provided to the head end server and in turn presents the information to the web adapter. The user is validated in accordance with the authentication scheme. When the user is validated a ticket is generated for the user. The ticket is presented to the client privilege server proxy that decrypts the ticket. A token is formed from the ticket and the client user identification. The token from the client is provided to the privilege server. A packet is formed having a sequence number and session key encrypted with the ticket. The packet is provided to the head end server which in turn authenticates the user.

Abstract: A method for obtaining parallel instruction execution (PIE) for frequently used programming operations, such as database record compression or expansion, cryptographic encoding/decoding, page moving, etc., for which a hardware-assist may be provided. These functions can be performed in parallel with CPU processing by a PIE processing facility (PIE-PF). The method is hardware/microcode based and uses software control in supervisory mode. The preferred embodiment is controlled by privileged subsystem software under an operating system, and does not use I/O channel oriented off-load processing. When the CPU is interrupted during an incomplete parallel operation by the PIE-PF, it is checkpointed in main storage in a manner accessible to the subsystem. The subsystem (after completing a current CPU operation, such as a database record predicate evaluation, can check for the completion of the PIE-PF operation by examining an indicator in a control block in shared storage.