A modular automation support system for modular plants comprises an automation engineering support system comprising a feedback processing component configured to modify the operation of one or more other components of the automation engineering support system based on user feedback.

A computer implemented method for generating a problem specific representation of a process network to enable controlling or monitoring a process network with at least two interconnected chemical plants, the method comprising the steps of providing a first digital representation of the process network comprising a digital process representation of each plant, its connections to other plants and sensor elements placed in the process network, generating based on the first digital representation a graph structure including vertices representing unit operations, edges linking unit operations representing at least physico-chemical quantities, wherein the edges include edge meta data representing at least physico-chemical quantities, and a measurable tag, generating based on the graph structure a collapsed graph structure including, vertices representing virtual unit operations, edges linking virtual unit operations representing at least physico-chemical quantities, wherein the edges include edge meta data representing observable physico chemical quantities, and their relation to vertices, deriving a set of balance equations from the collapsed graph structure, providing the set of balance equations, and physico- chemical quantities for monitoring and/or controlling operation of a process network is proposed.

A function to optimize factory management using information on operation ability and capability of an operator and a machine is provided.

Provided is a factory management device which makes a plan for a factory, the device including a storage unit which stores operator measurement information obtained by measuring a movement of an operator and machine measurement information obtained by measuring a predetermined value which indicates an operating state of a machine that is production equipment of the factory, an operator ability prediction unit which predicts changes in operation ability of the operator using the operator measurement information, a machine capability prediction unit which predicts changes in operation capability of the machine using the machine measurement information, a production capacity prediction unit which predicts a production capacity of the factory using prediction of changes in the operation ability and capability of the operator and the machine, and a planning unit which makes a plan for the factory that satisfies a predetermined productivity index using the predicted production capacity of the factory.

Methods, systems, and computer programs are presented for predicting the performance of semiconductor manufacturing equipment operations. One method includes an operation for obtaining machine-learning (ML) models, each model related to predicting a performance metric for an operation of a semiconductor manufacturing tool. Further, each ML model utilizes features defining inputs for the ML model. The method further includes an operation for receiving a process definition for manufacturing a product with the semiconductor manufacturing tool. One or more ML models are utilized to estimate a performance of the process definition used in the semiconductor manufacturing tool. Additionally, the method includes presenting, on a display, results showing the estimate of the performance of the manufacturing of the product. In some aspects, the use of hybrid models improves the predictive accuracy of the system by augmenting the capabilities of data-driven models with the reinforcement provided by the physics-based models.

A visualization device includes a processor for carrying out a first displaying process of displaying, on a display device, a first screen indicating power consumption of a whole of an equipment group in each of periods included in a period group, a second displaying process of displaying, on the display device, a second screen indicating a cycle time of each of pieces of equipment included in the equipment group in a particular period, and a third displaying process of displaying, on the display device, a third screen indicating a cycle time of each of processes carried out with use of a particular piece of equipment in the particular period.

An industrial integrated development environment (IDE) provides a development framework for designing, programming, and configuring multiple aspects of an industrial automation system using a common design environment and data model. Projects creating using embodiments of the IDE system can be built on an object-based model rather than, or in addition to, a tag-based architecture. To this end, the IDE system can support the use of automation objects that serve as building blocks for this object-based development structure. These automation objects represent corresponding physical industrial assets and have associated programmatic attributes relating to those assets, including data logging and device configuration parameters. Functional relationships between automation objects can be defined to yield object hierarchies, and object attributes can be propagated across objects up and down the hierarchy.

A system includes a memory and a processing device, operatively coupled to the memory, to perform operations including receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing. The data associated with the process comprises at least one of: an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings. The operations further include obtaining an output by applying the data associated with the process to the trained machine learning model. The output is representative of the defect impact with respect to the at least one defect type.

An information processing device includes one or more processors. The one or more processors are configured to: perform, by using a physical model for performing a simulation of operations of a plurality of electronic devices, the simulation, and output a plurality of pieces of first data representing outputs by the plurality of electronic devices; and estimate, based on the first data and a plurality of pieces of second data representing outputs obtained by operating the plurality of electronic devices, mapping data representing a correspondence between the plurality of electronic devices that output the first data and the plurality of electronic devices that output the second data.

The plant control method includes the following. Calculating a first reference amount to be supplied for an amount of hydrogen to be supplied to a second production device (40). Making a decision on whether or not the amount of remaining hydrogen in a storage device (20) at the beginning of a subject term falls within a reference range.

Two or more computational services are defined that each represent a respective different manufacturing capability used to partially create a target part model. A common space shared among the computational services is defined to reference the target part model and manufacturing primitives corresponding to each capability. The computational services are queried to construct a logical representation of the planning space based on intersections among the primitives. One or more process plans are formed using the different manufacturing capabilities to manufacture the part.