Why Leading Industry Players Focus on Compressed Air Station Energy Efficiency

In recent years, leading companies in China’s aluminium, automotive, electronics, and consumer electronics industries have been focusing on energy efficiency in their compressed air stations.

Many managers still believe that energy savings can be achieved simply by replacing air compressors. However, the common approach among leaders is to install an “AI brain” in the station room, without changing the main equipment or production lines, resulting in a direct reduction in electricity costs of 10%–30%.

“Dual Carbon” Targets Make Energy Efficiency a Hard Metric

Under the hard metric of “Dual Carbon” targets, every unnecessary kilowatt-hour of electricity increases both costs and carbon emissions. Compressed air stations are often overlooked high-energy consumption scenarios, accounting for 10-50% of the total plant energy consumption. Waste is mainly hidden in four key areas:

• Over-pressurization: For every 1 bar increase in exhaust pressure, energy consumption rises by approximately 7%, leading to unnecessary electricity consumption.
• Pipe network leakage: Without systematic leak detection, leakage can account for 20%~30% of the total gas production, resulting in wasted electricity costs.
• Inefficient equipment coordination: Traditional PLC and manual control methods lead to equipment idling and multiple machines competing for operation, reducing load efficiency.
• Reliance on manual inspections: Delayed responses mean that waste has already occurred by the time issues are identified.

AI Intelligent Control for Real Energy Savings and Unmanned Operation

The essence of SIND’s AI intelligent control for compressed air stations is to install a “thinking brain” in the station room, enabling a closed-loop control process that includes “perception → decision → execution” within seconds, ensuring stable pressure, lower energy consumption, and complete automation without human intervention.

Perception Layer: SIND’s AI hardware collects real-time data from the production, transmission, and usage ends (current, flow, IGV opening, pipe pressure dew point, end pressure, and worst-case pressure).

Decision Layer: SIND’s AI model and LingX Agent work together, considering multiple factors such as equipment performance, pipe pressure drop, and multi-end flow prediction. Based on algorithms for load balancing, multi-station collaboration, and safety control during unplanned shutdowns, the system outputs the most energy-efficient and safe instructions for on-site equipment execution within seconds.

Execution Layer: The AI intelligent control system directly issues commands for starting, stopping, loading, IGV/BOV adjustment, VFD adjustment, and valve opening adjustment, achieving energy savings of 10%–30% (up to 35% in some cases) without human intervention.

AI Intelligent Control Achieves Real Savings: 2.8 Million kWh Saved at Aluminium Corporation’s Centrifugal Compressed Air Station

In one of the factories under the Aluminium Corporation, SIND’s AI intelligent control system optimised the operation of seven centrifugal air compressors based on real-time network pressure, flow, and equipment efficiency models. This resulted in more balanced loads, more efficient throttling, and fewer blow-offs for each machine.

Efficiency Improvement: LingX Agent automatically collects data, generates reports, and performs fault analysis, enhancing management efficiency.

Energy Savings: By reducing the main pipe pressure while ensuring safe gas supply, the system reduced the number of operating machines, saving 2.8 million kWh annually (CN¥2,800,000, approximately US$394,366).