Pipeline integrity is crucial in the energy sector, as solid plugs from wax deposition pose significant risks. Existing prediction methods are hindered by a limited understanding of molecular mechanisms and interactions with flow assurance chemicals. To address this, we are developing software that combines mesoscale computational modeling and machine learning to simulate wax deposition. This tool offers reliable, customizable predictions for various oils and operational conditions, enhancing pipeline safety and efficiency.
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