A control parameter optimization device is provided with: a plant model configured to calculate a control command value by a control device and a process quantity of a plant; a control parameter updating unit configured to update a control parameter used for calculating the control command value in the plant model, on the basis of an objective function calculated based on a calculation result of the process quantity in the plant model; and a structural model configured to calculate a clearance between a stationary member and a rotating member in the rotating machine, on the basis of the process quantity from the plant model. The control parameter updating unit is configured to search for an optimal control parameter within a range where the clearance calculated by the structural model satisfies a constraint condition.

An apparatus is provided. The apparatus including a plurality of network interfaces, including a first network interface and a second network interface. The apparatus also includes a processor with two or more independent processing units, including a first independent processing unit and a second independent processing unit. The apparatus further includes a memory having first instructions and second instructions stored thereon. Execution of the first instructions, cause the first independent processing unit to execute operations associated with a first operating system and communicate, via the first network interface, over a bi-direction communication, with one or more platform computing devices. Execution of the second instructions, cause the second independent processing unit to execute real-time operations associated with a second operating system and communicate, via the second network interface, with one or more computing devices each having one or more sensors thereon.

Various systems and methods are provided for implementing a software defined industrial system. In an example, self-descriptive control applications and software modules are provided in the context of orchestratable distributed systems. The self-descriptive control applications may be executed by an orchestrator or like control device, configured to: identify available software modules adapted to perform functional operations in a control system environment; identify operational characteristics that identify characteristics of execution of the available software modules that are available to implement a control system application; select a software module for execution based on the operational configuration and the operational characteristics identified in the manifest; and cause the execution of the selected software module in the control system environment based on an application specification for the control system application.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

A method for verifying process orchestration logic for a modular plant includes generating a plant execution model representing the process orchestration logic; analyzing the plant execution model to identify one or more potential failure scenarios; and generating one or more test cases based on the one or more identified failure scenarios.

A method for enabling automation templates as a service for data processing includes: receiving a selection of an automation template among automation templates available for performing an automation request; retrieving, from a cloud system, an inputs form template corresponding to the selected automation template; receiving inputs to be inputted to the inputs form template; submitting, to the cloud system, the inputs form to trigger an automation execution based on the selected automation template and the inputs form; performing data ingestion based on input data sources specified in the inputs form and pre-defined set of rules specified in the selected automation template; executing an automation process based on a pre-defined set of calculations, transformations, and/or arrangements specified in the automation template; and pushing results of the executing based on destination information specified in the inputs form and the pre-defined set of rules specified in the automation template.

A method of optimizing synthetic conditions includes receiving a graph-type descriptor comprising at least one of structural information of at least one reactant and structural information of a target product to be synthesized by the reactant; determining combinations of synthetic conditions for generating the target product by applying the graph-type descriptor to a prediction neural network model; selecting at least one initial condition combination from among the combinations based on a first confidence corresponding to a yield of the combinations; updating the prediction neural network model based on a ground-truth yield obtained from a result of an experiment with the initial condition combination; determining a priority of the combinations based on the updated prediction neural network model; and determining subsequent combinations of synthetic conditions based on the determined priority.

One or more systems, computer-implemented methods and/or computer program products to facilitate a process to monitor and/or facilitate a modification to a manufacturing process. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that identifies values of inflow data of one or more inflows of a set of inflows to a manufacturing process as control variables, and a computation optimization component that optimizes one or more intermediate flows, outflows or flow qualities of the manufacturing process using, for mode-specific regression models, decision variables that are based on a set of joint-levels of the control variables. An operation mode determination component can determine operation modes of the manufacturing process that are together defined by a set of joint-levels of the control variables.

The purpose of the present invention is to provide a plant operation optimization assistance device, a plant operation optimization control device, and a method, which enable a reduction in computational load. The plant operation optimization assistance device is characterized by comprising: an input unit for inputting, as an input signal, an operation amount signal assigned to a plant and a process signal detected at the plant; a sensitivity estimation unit for taking, as a sensitivity signal, a time change amount of the process signal with respect to the operation amount signal; and a signal classification unit for sorting operational states of the plant from the input signal and giving an operational state signal, and also extracting, as a state-specific high-sensitivity signal, a sensitivity signal indicating high-sensitivity, from among sensitivity signals, for each of the sorted operational states.