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.

An information processing apparatus includes a first emulator that estimates a behavior of a device for driving a first control target that moves on a first target trajectory and a second emulator that estimates a behavior of a device for driving a second control target that moves on a second target trajectory. A visualization module generates drawing data for visualizing and drawing movement of the first control target and movement of the second control target in a three-dimensional virtual space by using a first command value and a second command value. The first and second emulators calculate the first command value and the second command value that control first and second driving devices in each control cycle according to a calculation command respectively. The calculation command instructs to calculate the command value for setting a movement amount in each control cycle variable.

To improve a working efficiency in a dynamic simulation of a plant, an apparatus, a method, a program and a simulation system are provided, the apparatus including a result acquiring unit configured to acquire a simulation result of a static model indicating a steady state of a plant, and an initial state generating unit configured to generate an initial value of a state parameter of a dynamic model that is a model calculating a dynamic state of the plant based on the simulation result. The initial state generating unit may generate an initial value of a state parameter of at least one device in the dynamic model.

An industrial automation system may include an automation device and a control system communicatively coupled to the automation device. The control system may include a first module of a number of modules, such that the first module may receive an indication of a target variable associated with the industrial automation device. The first module may then receive parameters associated with the target variable, identify a portion of data points associated with controlling the target variable with respect to the parameters, generate a model of each data point of the portion over time with respect to the parameters based on the data points, determine functions associated with the model. The functions represent one or more relationships between the each data point of the portion with respect to controlling the target variable. The first module may then adjust one or more operations of the automation device based on the functions.

Embodiments presented herein provide techniques for planning and scheduling in a factory. The technique begins by generating a bottleneck loading plan from a plurality of inputs. A simulation is run using the bottleneck loading plan. The factory is simulated using decisions made based on the bottleneck loading plan and a lot-to-machine schedule is generated with the simulation bottleneck loading plan.

Aspects of the technology described herein comprise a surrogate model for a chemical production process. A surrogate model is a machine learned model that uses a collection of inputs and outputs from a simulation of the chemical production process and/or actual production data as training data. Once trained, the surrogate model can estimate an output of a chemical production process given an input to the process. Surrogate models are not directly constrained by physical conditions in a plant. This can cause them to suggest optimized outputs that the not possible to produce in the real world. It is a significant challenge to train a surrogate model to only produce outputs that are possible. The technology described herein improves upon previous surrogate models by constraining the output of the surrogate model to outputs that are possible in the real world.

An industrial automation system may include an automation device and a control system communicatively coupled to the automation device. The control system may include a first module of a number of modules, such that the first module may receive an indication of a target variable associated with the industrial automation device. The first module may then receive parameters associated with the target variable, identify a portion of data points associated with controlling the target variable with respect to the parameters, generate a model of each data point of the portion over time with respect to the parameters based on the data points, determine functions associated with the model. The functions represent one or more relationships between the each data point of the portion with respect to controlling the target variable. The first module may then adjust one or more operations of the automation device based on the functions.

An electrocardiograph (ECG) simulator is provided to generate sequences of wave shapes having specified average rates for a sequence of varying rates.

A system is provided for combining frames in a plurality of ways to provide as output a new frame comprising a function of the combined frames. A system is also provided for combining a plurality of frames to provide as output a new frame that generates a specified average period as a function of the periods of the combined frames.