A system and method for the automated checking of I/O loops of a process automation system is disclosed that includes a dongle configured to be installed on a terminal block and make an electrical connection to at least one I/O loop. Operating software communicates with the dongle and to a database of I/O loop data. The operating software constructs an I/O loop check file using the database of I/O loop data and downloads the I/O loop check file to the dongle, where the dongle uses the I/O loop check file to test the I/O loop.

There is provided a method for optimizing a manufacturing process of a new part. The method includes executing, by a system configured to drive the manufacturing process, a set of manufacturing functions. Executing these functions include receiving data associated with one or more field parts similar to the new part, and generating, based on the data, a forecast representative of a longevity of the one or more parts. The method further includes generating a digital thread forming a surrogate model for the new part, based on the forecast. Further, the method includes creating the set of manufacturing functions based on the surrogate model and manufacturing the new part according to the set of manufacturing functions.

There are provided methods and systems for optimizing a manufacturing process. For example, there is provided a method for generating a model for driving a decision of a manufacturing process. The method includes simultaneously receiving data from a plurality of sources and executing a machine learning-based procedure on the data. The method further includes updating a physics-based model corresponding to the model in real time based on a result of the machine learning-based procedure.

A communication system and method that provides predictive and prescriptive analytics for a system running at an edge. In one embodiment, the communication system includes an architect subsystem configured to build, test and deploy a model based on sensor characteristics of the system. The sensor characteristics are from at least one of an operator input, a historical input, a specification input, and a subject-matter expert input. The communication system also includes an edge subsystem configured to receive said model and perform predictive and prescriptive analytics on sensor data from said system running on said model deployed at said edge.

Systems and methods for implementing an economic strategy such as a model predictive control (EMPC) strategy. An EMPC tool is configured to present to receive sinks and connections between central plant equipment. The EMPC tool also includes a data model extender configured to extend a data model to define new entities and/or relationships. The EMPC tool also includes a high level EMPC algorithm configured to generate an optimization problem and an asset allocator configured to solve the resource optimization problem in order to determine optimal control decisions used to operate the central plant.

Controlling circumferential variability in a blast furnace may include generating a predictive model that sets up a relationship between a standard deviation of a selected state variable, state variables and one or more control variables in blast furnace operation for predicting the standard deviation. A number of circumferential sections of the blast furnace is defined, and the predictive model associated with the selected state variable for each of the circumferential sections is trained based on process data of the blast furnace. A plurality trained predictive models is generated associated with different circumferential sections and different selected state variables. One or more future control variable set points that minimize a sum of the plurality of predictive models, is determined. One or more future control variable set points is transmitted to a control system to control the blast furnace operation.

Controlling circumferential variability in a blast furnace may include generating a predictive model that sets up a relationship between a standard deviation of a selected state variable, state variables and one or more control variables in blast furnace operation for predicting the standard deviation. A number of circumferential sections of the blast furnace is defined, and the predictive model associated with the selected state variable for each of the circumferential sections is trained based on process data of the blast furnace. A plurality trained predictive models is generated associated with different circumferential sections and different selected state variables. One or more future control variable set points that minimize a sum of the plurality of predictive models, is determined. One or more future control variable set points is transmitted to a control system to control the blast furnace operation.

The present invention is a control device which includes a filter unit for performing an attenuation process at a predetermined frequency on a control input based on a predetermined target command, generates the control input through model predictive control executed by a model predictive control unit and causes an output of a predetermined control object to follow the predetermined target command. A prediction model defines a correlation between the control input and predetermined extended state variables including a state variable related to a predetermined control object and a predetermined filter state variable related to the filter unit, and a predetermined evaluation function for model predictive controls configured to calculate a state quantity cost that is a stage cost with respect to state variables except the predetermined filter state variable among the predetermined extended state variables, and a control input cost that is a stage cost related to the control input.

Systems and methods for implementing an economic model predictive control (EMPC) strategy in any resource-based system include an EMPC tool. The EMPC tool is configured to present user interfaces to a client device. The EMPC tool is further configured to receive first user input including resources and subplants associated with a central plant. The EMPC tool is also configured to receive second user input including sinks and connections between central plant equipment. The EMPC tool also includes a data model extender configured to extend a data model to define new entities and/or relationships specified by user input. The EMPC tool also includes a high level EMPC algorithm configured to generate an optimization problem and an asset allocator configured to solve the resource optimization problem in order to determine optimal control decisions used to operate the central plant.

There are provided methods and systems for optimizing a manufacturing process. For example, there is provided a method for generating a model for driving a decision of a manufacturing process. The method includes simultaneously receiving data from a plurality of sources and executing a machine learning-based procedure on the data. The method further includes updating a physics-based model corresponding to the model in real time based on a result of the machine learning-based procedure.