A system and method for remote monitoring and control of landfill and recycling operations provide an intelligent centralized platform for remote, real-time industrial data gathering and process control for management of landfill and recycling operations such as leachate, gas, water and other liquids. The system and method can directly upload machine data (such as liquid volumes, flows, level, equipment runtime, sorter data, status, etc) into a centralized platform. This data can be used for analytics and automation of processes and equipment control.

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.

Various embodiments of the teachings herein include a method for optimizing a gasifier model. Some examples include: virtually partitioning the gasifier by temperature; enabling the gasifier model to input feed split from total feed based on a respective proportion coefficient in each virtual partition to perform gasification reactions independently by each virtual partition; enabling the gasifier model to determine a virtual value of a gasifier parameter after mixing the virtual partitions, quenching and shifting when gasification reactions reach an equilibrium in each virtual partition; determining a measured value of the gasifier parameter when the total feed is input; and adjusting the proportion coefficients based on a comparison result between the measured value and the virtual value of the gasifier parameter.

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.

This present disclosure provides a method for regulating a rated outlet pressure of a gate station compressor for smart gas, which is implemented based on an Internet of Things system for regulating a rated outlet pressure of a gate station compressor for smart gas. The method includes: obtaining user features of a downstream gas usage based on the smart gas object platform, the user features including at least a user type and at least one of downstream flow prediction values of a plurality of future moments, wherein the downstream flow prediction values are obtained by a downstream flow prediction model based on a historical downstream flow sequence; obtaining operation parameters of a compressor, 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.

The present disclosure provides a method for determining a gas pipeline network opening scheme based on 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 the gas pipeline network opening scheme for the region.

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.

Industrial machines positioned along a gas transmission pipeline are controlled using setpoints. Operators of such pipelines would benefit from real-time data and recommendations to guide them in optimizing performance of the pipelines. Accordingly, a compression optimization system is disclosed that monitors and provides real-time data and notifications, recommends optimal control setpoints using a machine-learning or other artificial-intelligence model, which may self-learn to realistically represent the pipeline, and executes simulations for hypothetical scenarios. The compression optimization system enables efficient management of the gas transmission pipeline.

Embodiments of the present disclosure provide a method, an Internet of Things system, and a storage medium for smart gas data management. The method includes: obtaining at least one type of raw gas data uploaded by at least one platform in the IoT system, wherein the raw gas data includes at least one of gas transportation data, gas pipeline data, and gas equipment data; evaluating a time reliability and a data reliability of the at least one type of the raw gas data; and generating at least one storage instruction based on the time reliability and the data reliability of the at least one type of the raw gas data to store the raw gas data in a corresponding data storage area.

A system for flare combustion control includes a sound speed measurement device for measuring sound speed in a flare vent gas, and a flare combustion controller including a memory and a processor. The processor is configured to receive the measured sound speed and determine, based on the measured sound speed, a molecular weight of the flare vent gas. The processor is further configured to determine, based on the determined molecular weight, a net heating value of the flare vent gas, and adjust the net heating value of the flare vent gas by regulating an amount of a supplemental fuel gas in the flare vent gas.