Applied Chemical Engineering

A Review of smart AI systems for real-time monitoring and optimization of ocean-based carbon capture, utilization, and storage networks

Dinesh Keloth kaithari

Department of Mechanical and Industrial Engineering, College of Engineering, National University of Science and Technology, Muscat, Oman

Anant Kaulage

Department of Computer Engineering, MIT Art, Design and Technology University,Loni Kalbhor, Pune , 412201, Maharashtra ,India

Ayyappadas MT

Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam – 690525, Kerala, India

Puja Gholap

Department of Computer Engineering, Sharadchandra Pawar College of Engineering, Dumberwadi (Otur), Junnar, Pune - 412409, Maharashtra ,India

Aarti Puri

Department of First Year Engineering (Engineering Chemistry), Dr. D. Y. Patil Institute of Technology, Pimpri – 411018, Pune, Maharashtra, India; School of Technology and Research, Dr. D. Y. Patil Dnyan Prasad University, Pimpri, Pune, 411018, Maharashtra, India

Mahesh Ashok Bhandari

Department of Information Technology, Vishwakarma Institute of Technology (VIT), Bibwewadi, Pune, 411037, Maharashtra, India

Kishor Renukadasrao Pathak

Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra 411048, India

Shital Yashwant Waware

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri , 411018, Pune, Maharashtra ,India; School of Technology and Research, Dr. D. Y. Patil Dnyan Prasad University, Pimpri, Pune, 411018, Maharashtra, India

Anant Sidhappa Kurhade

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri , 411018, Pune, Maharashtra ,India; School of Technology and Research, Dr. D. Y. Patil Dnyan Prasad University, Pimpri, Pune, 411018, Maharashtra, India

DOI: https://doi.org/10.59429/ace.v8i3.5747

Keywords: Marine AI systems; CO₂ sequestration; offshore monitoring; environmental risk prediction; autonomous sensing; digital twin modelling; reinforcement learning; edge computing

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Abstract

Importance – Ocean-based Carbon Capture, Utilization, and Storage (CCUS) systems are increasingly recognized as a vital solution for mitigating climate change due to their vast storage potential. Yet, their deployment faces significant challenges including harsh marine conditions, biofouling, corrosion, and limited real-time monitoring capabilities, which reduce safety and efficiency. Research Gap – Although land-based CCUS has been extensively studied, research on AI-enabled frameworks for offshore CCUS remains limited. Existing work is often confined to simulations or small-scale pilots, with inadequate attention to adaptive fault-tolerant control, multi-metric performance evaluation, and long-term field validation. Objective – This study aims to develop and validate a smart AI-enabled framework for real-time monitoring, predictive control, and optimization of offshore CCUS networks, with a focus on enhancing safety, efficiency, and environmental sustainability. Methodology – The proposed framework integrates IoT-enabled underwater sensors, autonomous vehicles, satellite imaging, and edge computing with advanced AI models including CNNs, LSTMs, GANs, and reinforcement learning. Validation was performed through a simulation-based case study on an offshore saline aquifer using a digital twin and multi-objective genetic algorithm optimization. Key Findings – The system achieved a 28% reduction in leak detection time, a 31% improvement in injection efficiency, and an 18% reduction in ecological risk compared with conventional monitoring approaches. The digital twin predicted plume migration with 95% accuracy, and robustness tests showed less than 5% performance degradation under sensor faults. Implications – These outcomes demonstrate that AI integration can significantly enhance monitoring, predictive decision-making, and compliance in offshore CCUS systems. The findings provide practical guidance for advancing autonomous and sustainable marine carbon storage, though large-scale deployment will require solutions to data scarcity, energy constraints, and regulatory integration.

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