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Carbon Capture, Utilization, and Storage (CCUS): Evaluating Technological Maturity and Global Adoption
Saanvi Burle
School of Computer Science
Dr. Vishwanath Karad MIT World Peace University Pune, India
saanvi.burle@mitwpu.edu.in
Prof. Renuka Mane
Assistant Professor
School of Computer Engineering & Technology
renuka.suryawanshi@mitwpu.edu.in
Abstract
Reducing CO2 emissions from industrial sources and energy generation depends much on Carbon Capture, Utilisation, and Storage (CCUS) technologies. With an emphasis on their Technology Readiness Levels (TRL) to evaluate their maturity and scalability, this paper investigates the present state of CCUS technologies, so highlighting important developments in CO2 capture methods, including direct air capture and solvent-based absorption, together with computational approaches optimising geological storage and use techniques. Although CCUS could enable sectors to reach carbon neutrality by lowering atmospheric CO2 concentrations, problems including high operating costs, scalability problems, and the demand for strong, technologically driven monitoring systems still exist. Based on computational integration, this paper sorts CCUS technologies, assesses artificial intelligence (AI) and machine learning (ML) models for best capture efficiency, and investigates blockchain-enabled systems for open carbon credit verification. It also looks at IoT-based sensor networks for real-time storage integrity monitoring and the part high-performance computers (HPC) play in modelling carbon sequestration dynamics. Though new trends show increasing integration of artificial intelligence-driven predictive maintenance, blockchain-based carbon accounting, and edge computing for distributed monitoring, key findings reveal that despite technological advancements, major deployment remains limited by economic and technical constraints. While outlining future research directions to improve CCUS adoption through computational advancements, so ensuring alignment with global climate targets, the study identifies major knowledge gaps including improved real-time anomaly detection in storage reservoirs, advanced simulation models for CO2 plume behaviour, and cost-effective digital twins for process optimisation.
Index Terms:
Carbon Capture, Utilization, and Storage (CCUS); Technology Readiness Levels (TRL); Direct Air Capture (DAC); Solvent-Based Absorption; Artificial Intelligence (AI); Machine Learning (ML); Blockchain; Internet of Things (IoT); High-Performance Computing (HPC); Carbon Sequestration; Computational Fluid Dynamics (CFD); Predictive Maintenance; Carbon Accounting; Digital Twin; Anomaly Detection; CO₂ Storage Monitoring; Carbon Credit Verification; Decentralized Monitoring; Edge Computing; Climate Change Mitigation.