Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 product...

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Bibliographic Details
Published in:The Science of the total environment Vol. 917; p. 170085
Main Authors: Al-Sakkari, Eslam G., Ragab, Ahmed, Dagdougui, Hanane, Boffito, Daria C., Amazouz, Mouloud
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 20.03.2024
Subjects:
artificial intelligence
Artificial intelligence and machine Learning
carbon
Carbon capture, utilization and sequestration
carbon dioxide
climate change
cost effectiveness
energy
environment
infrastructure
Life cycle assessment
Material and process design
process design
Reinforcement Learning
Simulation-based optimization
supply chain
Surrogate costing models
Life cycle assessment
Surrogate costing models
Material and process design
Reinforcement Learning
Carbon capture, utilization and sequestration
Artificial intelligence and machine Learning
Simulation-based optimization
ISSN:0048-9697, 1879-1026, 1879-1026
Online Access:Get full text
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Summary:Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. The developed AI methods can be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain. [Display omitted] •Carbon capture, utilization and sequestration (CCUS) technologies were reviewed.•The application of artificial intelligence (AI), as a data-driven approach, in CCUS was reviewed and evaluated.•Limitations of existing mechanistic and data-driven methods were determined.•New opportunities for applying AI to optimize the performance of CCUS systems were proposed in detail.•Hybrid modeling is identified as a promising approach for accelerated CCUS optimization.
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ISSN:0048-9697
1879-1026
1879-1026
DOI:10.1016/j.scitotenv.2024.170085