How IMI TWTG NEON Sensors transformed cooling efficiency at an LNG facility in the United States

For LNG operators, maintaining optimal cooling performance is essential to maximising plant efficiency and avoiding costly disruptions. Yet hidden mechanical issues and cooling inefficiencies can be difficult to identify before they have a real impact on operations. Across large facilities with dozens of fin fans and extensive heat exchanger networks, traditional inspection methods often fall short.

This case study explores how IMI TWTG NEON sensors helped improve fault detection, optimise cooling efficiency and enable predictive maintenance through a scalable, cost-effective solution at a major LNG manufacturing facility in the United States.

Like all LNG plants, the US facility relied on air-cooled heat exchangers (ACHEs) to efficiently remove large volumes of heat generated during processing. This cooling is typically achieved using fin fans.

Fin fans blow ambient air onto the heat exchanger pipes. If the air temperature drops, the fin fan speed can be reduced, resulting in significant energy savings, as an LNG train has 90 fin fan motors. However, they are prone to mechanical degradation over time, including belt slippage, which can significantly reduce cooling efficiency and affect the liquefaction process.

With a typical LNG train operating around 90 fin fans simultaneously, detecting and diagnosing small faults across the system is both complex and time-consuming. The result is that significant maintenance effort is required to maintain optimal performance.

An additional hazard arises from hot air recirculation (HAR), a phenomenon that can significantly impact the performance and efficiency of ACHEs, particularly in LNG and petrochemical plants. As ACHEs expel massive volumes of hot air, some may be recirculated back into the intake inlets. This reduces cooling capacity and creates operational inefficiencies, ultimately impacting production across the entire plant.

A 5°C increase in temperature on the air intake of the compensator can reduce plant efficiency by 15%

This creates two issues: one, earlier detection of mechanical issues; and two, the identification of HAR occurrences, both of which are resolved with sensors. There is, however, a third consideration: the installation and ongoing maintenance of existing plants spread over a large geographical area with limited connectivity.

To address early fault detection and heat exchanger performance challenges, our engineering experts worked closely with the LNG facility’s engineers to deploy a wireless, sensor-based monitoring solution across critical cooling assets.

We designed our range of IMI TWTG NEON industrial IoT sensors to provide the plant with proactive monitoring. Providing up to 10 years of battery life, wireless installation, and communication via LoRaWAN, the NEON sensors offer low installation costs, making them very cost-effective to scale up across an existing plant.

But it was the detailed, accurate data they provided that delivered proven results and led to a far safer, more productive operation. By combining two of the four NEON sensor types, operatives at the LING facility received comprehensive, real-time data to enhance performance.

The IMI TWTG NEON Vibration Sensor, attached to fin fans across the plant, detected belt slippage soon after installation, while it was week 30 before it became a noted problem on the site itself. The detailed daily data could point directly to the belt that needed maintenance.

Inlet and outlet temperature monitoring on heat exchangers at the LNG site provided a clear measure of cooling performance. The NEON Temperature Transmitter, sited at both the inlet and outlet of the heat exchanger and feeding data into the analytics platform, detected degradation in efficiency before it became a concern.

The operational benefits of IMI’s NEON sensors proved to be considerable:

The sensors proved easy to maintain, with transmitter located up to three meters away from the sensor, allowing access without scaffolding or a complete operational shutdown. The low cost of the sensors made scaling up easier than with other options. Increased sensors pinpointed faults, requiring engineers at the site to focus only on sensors that raised real-time concerns.

With a full-spectrum fast-fourier transform (FTT), engineers had all the required data at hand without delay, removing the need for manual rounds. Data could be continuously analysed from a central smart room. So full-time monitoring anticipated failures, reduced downtime and ultimately increased the LNG plant’s overall operational efficiency.

Following the successful installation of the sensors, the data they generated provided a clear, uninterrupted performance, significantly improving understanding of machine behaviour. Engineers and specialists gained insights from data that enabled better decision-making.

Both the IMI TWTG NEON sensors communicate via LoRaWAN, offering flexibility and low network costs. This provides several advantages.

There are no cabling requirements and installation be undertaken rapidly and at low cost, without disrupting operations. NEON technology is scalable so that a single site can support over 1000 sensors from a small number of gateways. Advanced noise filters and sub-GHz frequencies enable long-range, uninterrupted communication. There is no problem with accessibility, given that measurements can be taken from the whole site, which, under previous maintenance procedures was impossible. And the fact that data is generated and housed on a private network means that it remains secure and within the operator’s control.

IMI TWTG NEON sensors transformed the way in which this US-based LNG plant monitored and maintained critical cooling infrastructure. By combining wireless deployment, long-life sensors, and real-time analytics, operators could shift their attention comprehensively from costly reactive maintenance to predictive decision-making. The result improved cooling efficiency, reduced downtime, provided the operators with greater confidence in their plant’s performance, all delivered through scalable, cost-effective technology.

Three-axis vibration sensor

On-device automatic spectral analysis detects faults such as bearing faults, misalignment, vibrations, cavitation and bearing wear and lubrication issues

Full spectrum FFT – 8192 lines

Results are transmitted wirelessly via LoRaWAN, removing the need for additional cabling

Extended frequency range: 0.5Hz – 6.66kHz

Fault indicators and alarms are displayed in the SolidRed software platform