Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that can optimize a manufacturing plant's operations and/or production based on simulations that utilize information about the current and forthcoming manufacturing conditions to determine the optimal operational characteristics for the different plant devices. Methods may include obtaining, using data from a set of sensors, a set of current operational characteristics for multiple plant devices in a plant. Multiple manufacturing scenarios can be simulated using different sets of operational characteristics for plant devices in which at least one operational characteristic from the set of current operational characteristics is varied. A first manufacturing scenario can be determined that satisfies overall operational parameter(s) for one or more plant devices or for the whole manufacturing plant. Setting(s) of one or more plant devices can be adjusted based on a set of operational characteristics corresponding to the first manufacturing scenario.

An interactive assistance system and method for computer-aided control optimization for a technical system is provided. For example, a gas or wind turbine, in particular for optimizing the action sequence or the control variables of the plant (e.g. gas supply, compression), wherein an input terminal is provided for reading at least one status parameter providing a first system status of the technical system, and at least one setting parameter for adapting a reward function. A simulation module having a pre-trained neuronal network, simulating the plant, serves to simulate an action sequence on the technical system, starting from the first system status and to the prediction of the resulting statuses of the technical system.

A system and method for conveyor configuration and testing. The system is configured to execute the method, which includes: receive input data relating to configuration of a conveyor system; prepare a simulation of the configured conveyor system; operate the simulation of the conveyor system; determine at least one operational parameter related to the conveyor system to be monitored; monitor the at least one operational parameter during operation of the simulation of the conveyor system; determine if the configuration of the conveyor system needs to be adjusted based on the monitored operational parameter; if the configuration needs to be adjusted, automatically make an adjustment and return to operate the simulation of the conveyor system; and continue the simulation until otherwise terminated. In some cases, the monitoring operational parameters uses a machine learning model based on actual data from operating conveyors.

A program optimization system is provided with a CNC simulator configured to sequentially read out a machining program and perform a machining simulation, a machining program storage unit configured to successively transfer the machining program to the CNC simulator, and a transfer speed control unit configured to control a transfer speed to be a predetermined lower limit value. The CNC simulator optimize the machining program when a state of buffering deficiency in which the machining program to be read out is insufficient is detected in a cutting section and create optimized machining program free from a buffering deficiency.

Operation of an industrial plant is simulated based on a range of values of process variable input parameters to produce a range of values of simulated output parameters. An optimum operating region of the plant is defined where the plant operates more economically, such as increasing the yield of a product meeting specification while keeping the operating cost minimum. Identifying the location of the optimum operating region at any moment is of fundamental importance. Finding this region includes collecting process variables such as outputs from a plant historian relevant to the process or asset. A digital twin of the same process is created to analyze the optimal region of assets using simulation software, recommend improvements for the process operation, generate online simulation of nuisance alarm reductions, and provide optimized alarm limits to maintain operation of the industrial asset.

An interactive assistance system and method for computer-aided control optimization for a technical system is provided. For example, a gas or wind turbine, in particular for optimizing the action sequence or the control variables of the plant (e.g. gas supply, compression), wherein an input terminal is provided for reading at least one status parameter providing a first system status of the technical system, and at least one setting parameter for adapting a reward function. A simulation module having a pre-trained neuronal network, simulating the plant, serves to simulate an action sequence on the technical system, starting from the first system status and to the prediction of the resulting statuses of the technical system.

A system and method for conveyor configuration and testing. The system is configured to execute the method, which includes: receive input data relating to configuration of a conveyor system; prepare a simulation of the configured conveyor system; operate the simulation of the conveyor system; determine at least one operational parameter related to the conveyor system to be monitored; monitor the at least one operational parameter during operation of the simulation of the conveyor system; determine if the configuration of the conveyor system needs to be adjusted based on the monitored operational parameter; if the configuration needs to be adjusted, automatically make an adjustment and return to operate the simulation of the conveyor system; and continue the simulation until otherwise terminated. In some cases, the monitoring operational parameters uses a machine learning model based on actual data from operating conveyors.

In example embodiments, a multi-stage PFS diagram generation technique is used to iteratively define the layout of a PFS diagram from a subset of engineering data in a 3D model of an industrial process. The multi-stage PFS diagram generation technique may repeatedly call an automatic layout generator, which each time solves for one unknown quality of the PFS diagram (e.g., relative positions of components in the PFS diagram, positions on components in the PFS diagram, sizes of the components in the PFS diagram). The PFS diagram may be adapted based on user preferences, for example to define the subset of engineering data, or to constrain aspects of its layout. Updated PFS diagrams may be generated by selecting different user preferences.