| Sumario: | Introduction: The accelerating pace of climate change, driven primarily by rising greenhouse gas emissions, presents a critical challenge to sustainable natural resource management. Carbon Capture and Storage (CCS) is increasingly recognized as a viable mitigation strategy to reduce atmospheric CO2 levels and support global carbon neutrality goals. Methods: This study explores the integration of Machine Learning (ML)-- specifically Graph Convolutional Networks (GCNs)--and High-Performance Computing (HPC) to optimize CCS processes. GCNs are employed to analyze and model complex datasets for CO2 transport, identifying optimal routes based on criteria such as distance, cost, and efficiency. Simultaneously, HPC infrastructure, including GPU acceleration, is leveraged to enhance computational speed and processing capabilities. Results: The combined implementation of GCNs and HPC significantly reduces computational time--by approximately 65% compared to GCNs without HPC support. This acceleration enables real-time route optimization and model recalibration based on dynamic environmental and logistical inputs, improving the operational efficiency of CO2 capture, transport, and storage. Conclusions: The integration of high-performance computing with advanced ML techniques offers a transformative approach to improving CCS systems. By enabling rapid, data-driven decision-making, this strategy not only enhances the precision and efficiency of CCS but also strengthens broader efforts toward climate change mitigation and sustainable environmental management.
|
|---|