A method of controlling an industrial gas production facility comprising: receiving time-dependent power data receiving time-dependent operational characteristic data; defining one or more power constraints for the operational parameters of the power network; defining one or more process constraints for the operational parameters of each industrial gas plant; generating, based on the power data, the operational characteristic data, the one or more power constraints and the one or more process constraints, control set point values for the one or more industrial gas plants to achieve a pre-determined production parameter for the industrial gas production facility; and sending the control set point values to a control system to control the one or more industrial gas plants by adjusting one or more control set points of the industrial gas plants to achieve the pre-determined production parameter for the industrial gas production facility.

An acquisition unit acquires a bundle of detection values for each of a plurality of sensor values pertaining to a plant. A distance calculation unit obtains the Mahalanobis distance of the bundle of detection values acquired by the acquisition unit using, as reference, a unit space constituted by a collection of bundles of detection values for each of the plurality of sensor values. A determining unit determines, based on whether the Mahalanobis distance is at or within a prescribed threshold, whether the operation state of the plant is normal or abnormal. A trend specification unit specifies a trend with regards to at least one sensor value. An abnormality cause estimation unit estimates an abnormality cause based on the trend for the sensor value(s), and a fault site estimation database for holding the relationship between abnormality causes that may occur in the plant and sensor values for each of the trends.

This present disclosure provides a method for gate station compressor operation optimization for smart gas, which is implemented based on an Internet of Things system for gate station compressor operation optimization for smart gas. The Internet of Things system includes a smart gas device management platform, a smart gas sensor network platform, and a smart gas object platform interacting in sequence. The method is executed by the smart gas device management platform, comprising: obtaining user features of a downstream gas usage based on the smart gas object platform, the user features including at least a user type; obtaining operation parameters of a compressor based on the smart gas object platform, the operation parameters including at least a rated outlet pressure set by the compressor; and determining a rated outlet pressure adjustment amount of the compressor based on the user features and the operation parameters.

Methods for monitoring reliability of a smart gas pipeline network are provided. The method may be implemented based on a smart gas safety management platform of an Internet of Things (IoT) system for monitoring reliability of a smart gas pipeline network. The method may include: obtaining a reliability sequence of the gas pipeline network, the reliability sequence including reliability of the gas pipeline network at a plurality of moments; and determining a monitoring plan based on the reliability sequence, wherein determining the reliability of the gas pipeline network at each of the plurality of moments includes: obtaining a gas transportation feature of the gas pipeline network at a target moment; determining, based on the gas transportation feature, a degree of wear of a pipeline wall; and determining, based on the degree of wear of the pipeline wall, the reliability of the gas pipeline network at the target moment.

The present disclosure provides a preset management method of a gas pipeline network for a smart gas, which is performed by a smart gas management platform. The smart gas management platform comprises a smart user service management sub-platform, a smart operation management sub-platform and a smart gas data center. The method comprises: obtaining, by the smart gas data center, a region feature of each region within a target range through a smart gas sensing network platform; determining, by the smart operation management sub-platform, a gas demand degree of the each region based on the region feature of the each region; determining, by the smart operation management sub-platform, a region as a first-class region based on the gas demand degree in the region meeting a preset condition, and determining a gas pipeline network opening scheme for the region.

The embodiments of the present disclosure provide methods for controlling natural gas efficiency enhancement of smart gas, smart gas Internet of Things (IoT) systems and mediums. The method may comprise obtaining at least one user need through a smart gas user platform based on a smart gas service platform, the user need including a gas usage target need; determining at least one optimization objective based on the at least one user need; determining a target proportioning feature of a synergist according to the at least one optimization objective, the target proportioning feature including a proportioning vector and an addition proportion of the synergist; and configuring a target synergist that satisfies the target proportioning feature to enhance efficiency of natural gas by sending the target proportioning feature to a smart gas object platform through a smart gas sensor network platform.

A gas supply amount measurement method is performed in a gas supply system including a vaporization section, a control valve provided downstream of the vaporization section, and a supply pressure sensor for measuring the supply pressure between the vaporization section and the control valve. The method comprises: a step of measuring an initial supply pressure by the supply pressure sensor in a state where the control valve is closed; a step of opening the control valve for only a predetermined time; a step of measuring for a plurality of times of the supply pressure in a period of time between a time at which the pressure starts to fall from the initial supply pressure and a time at which a predetermined time has elapsed when the control valve is open for only a predetermined time, and a step of determining the gas supply amount when the control valve is open for only a predetermined time by calculation based on the measured values of the plurality of supply pressures.

A semiconductor device manufacturing system and a method for manufacturing semiconductor device are provided. The semiconductor device manufacturing system includes a substrate processing device and a processor. The substrate processing device includes a processing chamber, a gas supply module and a gas source. The processor is configured to monitor and control the gas supplied into the substrate processing device.

There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.

A computer-readable medium includes recorded instructions for performing a method of improving evacuation gas flow performance of an additive manufacturing build chamber. Instruction execution by a processor of a host computer device causes the processor to receive an input data set inclusive of flow field data of an inert gas through the build chamber, and an improvement metric operable to characterize flow improvements therein. The processor generates a flow field data set in response to the input data set, and extracts evacuation streamline data from the flow field data set. The streamline data describes expected flow paths of the inert gas through the modified build chamber. Instruction execution also causes the processor to generate an output data set using the streamline data, with the output data set including the flow improvements as characterized by the metric.