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Presentation Author

Kian Seng Lee
Kian Seng Lee

Product Manager


AI-Driven Live Advisory for LNG Plant Start-Up

LNG plant start-up is a challenging process that requires simultaneous control and monitoring of different units. Any wrong decision or miscommunication could cause delays to rectify issues, costly maintenance and loss of production opportunity. Typically, different panel operators have different approaches to start-up the plant based on their individual experience and tacit knowledge, yielding varied results. To capture the valuable tacit knowledge from experienced operators and leverage on data insights from over two decades of operating the facilities, an AI-driven live advisory was developed to provide panel operators with real-time parameters control advisory in response to actual unit conditions, enabling consistent and optimised plant start-up. Combination of machine learning algorithms was used to learn from 3.4 billion data points from over 1800 sensors readings for the past 20 years. The model built predicts the main cryogenic heat exchanger (MCHE) temperature profile with respect to inputs and controllable parameters. The predictive model is then augmented with optimisation algorithms to generate real-time advisory of the parameters control that would result in optimum cooling down of MCHE, given the actual state of the plant. Inputs from experienced panel operators and engineers were also codified in the algorithms to ensure the advisory generated is feasible considering process and equipment limitations. The live advisory has been employed in ten start-ups of LNG trains in PETRONAS LNG Complex for pre-cooldown process. It successfully achieved consistent and optimised cooling rate of MCHE, resulting in reduction of overall duration by 44% and carbon emission by 17%, as compared to the past average. On top of that, six of the start-ups achieved the top pre-cooldown execution in the last 20 years when benchmarked against all the historical occurrences of the respective plants using established criteria like overall duration, temperature rate of change, temperature compliance and gas usage. The innovation has also received recognition by professional and reputable institutions at international level such as IChemE Global Award (Process Automation and Digitalisation), Gastech Engineering Partnership of the Year and Malaysia Technology Excellence Awards (AI - Oil & Gas)