Abu Dhabi National Oil Company (ADNOC) has deployed Taurob’s heavy-duty inspection robot at its Taweelah Gas Compression Plant, where it will conduct routine inspections in hazardous environments without putting people at risk, according to the company’s May 21 release.
The Taurob heavy-duty robot is now conducting on-site autonomous inspections as the first set of eyes on the ground, enabling engineers to identify potential gas leaks, unusual hotspots, and other hazards to enhance safety, said ADNOC.
Built for extreme industrial environments, the robot is fitted with a wide range of advanced sensors, including 3D LiDAR (Light Detection and Ranging) technology and thermal cameras with 360-degree visibility.
“Artificial and Physical Intelligence are core to ADNOC’s long-term energy strategy, transforming how we operate across the value chain, said Dena Almansoori, Chief Technology and Innovation Officer, ADNOC.
“At Taweelah, autonomous advanced robots are already deployed in live operations as we continue to develop the next generation of industrial robotics. This is innovation with purpose, enhancing safety, reducing emissions, improving performance, and supporting the UAE’s AI Strategy 2031 and Robotics & Automation agenda,” Almansoori added.
ADNOC is also co-developing the industry’s first heavy-duty operator robot that lifts and grips industrial equipment, operational by the end of 2026. In addition, the robot can operate remotely and perform tasks autonomously in an environment with temperatures ranging from –20°C to 60°C. It would be precise enough to turn valves and operate gauges, tasks that would normally require people to enter high-risk areas.
This is not the first technology to be deployed at the Taweelah plant, inaugurated in 2018 with 450 million cubic feet per day (mmcf/d) of gas sales. In 2024, ADNOC deployed Neuron 5, an artificial intelligence (AI)-enabled process optimization technology, at the plant and at the Northeast Bab (NEB) offshore field. The technology uses advanced AI models and deep learning algorithms to predict maintenance needs and monitor equipment performance by interpreting data, such as pressure, temperature, and vibration, received from sensors on critical equipment.
