Gate Station Compressor Pressure Control Using IoT Flow Prediction
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Solution Overview
Problem
The high operational costs of gas gate station compressors, primarily due to gas and electricity consumption, necessitate an efficient method to regulate the rated outlet pressure to optimize energy usage.
Innovation Solution
An Internet of Things (IoT) system that includes a smart gas device management platform, a smart gas sensor network platform, and a smart gas object platform, utilizing machine learning models to predict downstream flow and outlet pressure values, allowing for precise adjustment of the compressor's rated outlet pressure based on user features and operation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the compressor operates at high rated outlet pressure to ensure sufficient gas supply, then gas delivery reliability is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic adjustment of the compressor's rated outlet pressure based on real-time downstream flow predictions and actual gas usage patterns. Instead of maintaining a fixed high pressure, the system continuously adapts the pressure setpoint to match actual demand, ensuring sufficient supply reliability while minimizing energy consumption at any given moment.
Solution Approach 2:
The system changes the operating parameter (rated outlet pressure) of the compressor based on predicted and actual downstream flow conditions. By adjusting the pressure parameter dynamically rather than maintaining a constant high value, the system resolves the contradiction between ensuring reliable gas supply and reducing energy consumption.
2Use of energy by moving object
If the compressor rated outlet pressure is frequently adjusted to match varying downstream demand, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical pressure regulation systems with an intelligent control system that uses machine learning models and IoT technology. The downstream flow prediction model and smart device management platform compute optimal pressure settings algorithmically, substituting sophisticated mechanical adjustment mechanisms with software-based intelligence that achieves energy efficiency without proportionally increasing physical system complexity.
Solution Approach 2:
The system implements self-service through automated prediction and adjustment of compressor pressure settings. The downstream flow prediction model automatically forecasts future flow conditions, and the smart device management platform autonomously determines and applies optimal pressure adjustments without requiring manual intervention or complex operator decision-making processes.
3Loss of energy
If machine learning models are used to predict downstream flow and optimize pressure settings, then operational cost reduction is achieved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by using the downstream flow prediction model to forecast future gas flow conditions before they occur. This advance prediction allows the system to proactively optimize compressor pressure settings in anticipation of demand changes, reducing operational costs through preventive optimization rather than reactive adjustment, while the computational complexity is managed by performing predictions during off-peak computational periods.
Data Source
AI summary
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.