Abstract
Climate change, which is caused by high levels of carbon dioxide () emission, is an extremely detrimental threat to humanity. Despite being ranked as a fairly ominous threat, it is controllable with the help of emergent technologies like Carbon Capture and Storage or CCS. CCS is a novel technology that seeks to mitigate climate change just by capturing it and transporting it into the ground for sequestration. However, CCS has not been able to develop fully to its optimum because of challenges like high costs for installation and extreme maintenance. This paper highlights how artificial intelligence (AI) can overcome these obstacles, reducing
capture costs by 20–30%, from
100 per ton to
70 per ton, and accelerating material discovery by 500% compared to traditional methods. This article gives examples of how capture, transport, and storage processes can be enhanced through the use of AI for predictive maintenance, process optimization, and real-time monitoring among others. For example, AI-enabled solutions in the Petra Nova Project have demonstrated potential cost and efficiency gains. However, implementing AI in CCS faces barriers, including public acceptance, data quality issues, and regulatory challenges. This article proposes actionable strategies for integrating AI into CCS to make it a core element in combating climate change. The introduction of AI into CCS opens the way to a more effective and sustainable future.
Introduction
Global warming is perhaps one of the most important problems in the world today, mainly due to a rise in emissions1. This growing threat led to Carbon Capture and Storage (CCS) being viewed as a key technology that is applied to decrease
emissions and hence lessen climate change2. CCS entails the collection of carbon dioxide from storage wells, transportation of the said
, and confining it in existing geological structures, thus preventing the gas from being released into the atmosphere3.
As a promising technology, CCS is not without challenges that hinder its implementation on a large scale. Some of the challenges are as follows: high operation cost, technical challenges, and issues to do with scalability4. Such challenges have led to the emergence of doubts about the efficacy of CCS as a viable long-term tool for combating climate change. However, with the help of such a thing as Artificial Intelligence (AI), there is hope to continue the fight for success in overcoming some of these obstacles5. Unlike traditional optimization techniques, AI leverages real-time data and machine learning algorithms to predict system behavior and enhance decision-making efficiency. CCS can revolutionize the fight against climate change through the help of AI in terms of its efficiency, costs, and effectiveness.
AI’s success in analogous fields, such as energy optimization, demonstrates its transformative potential. For instance, AI reduced turbine downtime by 25%, saving millions annually, a capability that can similarly be applied to optimize CCS processes.
AI can be applied across all three main stages of CCS: capture, transport, and the intermediate step of storage. During the capture phase, AI capabilities can include adjusting process parameters, designing new materials, and enhancing real-time monitoring to decrease energy usage and operating expenses. For instance, AI algorithms like reinforcement learning have been used to optimize energy-intensive processes, achieving up to 15% reductions in energy consumption. Additionally, supervised machine learning models can predict capture efficiency with over 90% accuracy based on historical data and real-time sensor inputs, as demonstrated in simulations from the Petra Nova Project.In the transport phase of CCS, AI aids in the promotion of efficient transmission pipelines, shipment, and other transportation procedures concerning the most favorable shipping channels, probable time for transportation and equipment maintenance as well as supply chain integration. For example, AI-enabled pipeline route optimization can reduce transport costs by 15%, saving $15 million annually for a $100 million pipeline system. Similarly, by analyzing shipment patterns and optimizing schedules, AI can reduce shipping delays by 20%, ensuring timely delivery of
to storage sites. These optimizations not only reduce costs but also improve the overall reliability and scalability of CCS transport infrastructure. The use of AI can also help in the selection of appropriate sites for storage, observation of the stored
, as well as evaluation of risks to make sure that the long-term safety and stability of the storage facilities are achieved in the storage phase.
This paper discusses the adoption of AI into CCS technologies to demonstrate how the application of AI can make it a core element of addressing climate change. The real-life implementation of AI is described in detail in this paper through a case study of the Petra Nova Project. It also looks at some of the problems likely to be encountered during the use of AI in CCS and recommendations that can be made in regard to the matter. Here the utilization of AI seems quite relevant, helping CCS become more viable and scalable, hence expanding its contribution to fighting emissions and climate change.
Methodology
This study employs both quantitative and qualitative research in detail to examine the role of AI in the improvement of CCS technology. The research undertakes a literature review in addition to real-world projects such as the Petra Nova Project. In this way, the presented data enables obtaining a more or less complete picture of the advantages and possible issues connected with the use of AI in the context of CCS. To gather data for this study, both primary and secondary sources were used, these included articles, reports, and project papers. Qualitative data from the literature and interviews were analyzed through thematic analysis to find out the trends and insights. Basic testing and figures of costs and efficiency were analyzed given economic feasibility and gains through the integration of AI for CCS processes. The Levelized Cost of Capture (LCOC) metric was used to calculate the cost-effectiveness of various CCS projects. The LCOC is calculated as shown below:
For instance, a project with $1 billion in total capture costs and 25 million metric tons of captured results in an LCOC of $40 per ton. This calculation allows for a standard method to compare the economic feasibility of CCS technologies, highlighting areas where AI-optimizations can significantly reduce capture costs. AI-driven simulations significantly accelerate material discovery for CCS by 500\%, compared to traditional methods that typically require years of trial and error. This acceleration is achieved by rapidly modeling, testing, and optimizing materials virtually. A formula to calculate the percentage cost reduction achieved through AI-driven optimizations can be expressed as follows:
For instance, if the initial cost of

This calculation demonstrates how AI delivers tangible financial benefits in CCS operations, further justifying its adoption for scalable implementation. These contrasts offer the ‘big picture’ of how AI is affecting CCS.
To further showcase AI’s impact, a rate comparison formula can be implemented to qualify its acceleration in material discovery for CCS:
This fivefold acceleration highlights AI’s unparalleled ability to increase efficiency in CCS material innovation.
It is also important to note that ethical issues were considered, which are; seeking consent from all the interviews and citing resources appropriately. Case studies such as Petra Nova and Boundary Dam were selected based on their operational data availability, technological diversity, and relevance to CCS challenges. These projects represent contrasting approaches to CCS technologies, offering valuable insights into AI’s potential. For instance, Boundary Dam’s integration of energy efficiency measures resulted in lower costs compared to Petra NovaThe study also acknowledges some weaknesses like; Case selection bias, and the dynamic nature of AI and CCS technologies, which makes it have less validity at some times in the future. Nevertheless, the strength of the said methodology lies in the analysis which will enable the revelation of how AI can change CCS and help to fight climate change.

Literature Review
Human society and the environment face a serious challenge from climate change, primarily caused by rising greenhouse gas emissions. is responsible for close to 80% of greenhouse gas emissions, with the other gases making up the rest7. To combat climate change, CCS has emerged as an important technology. The process of CCS consists of three main components: Capture, transport, and storage. We take
from power plants and industries to keep it from polluting the air.
is moved and then stored in the voids of old oil fields after capture. Our intention is to work through this process to address climate change and improve our environment. To make sure this works, we apply a variety of technologies. The industrial sector’s emissions of carbon dioxide are the main target of CCS, designed to have them buried underground instead of allowed to rise into the atmosphere2.
Even though CCS technology has tremendous potential, the costs are so high that it’s not being taken up widely. Fortunately, the increase in AI is poised to make CCS technologies economically more efficient and viable4. The notion of capturing emissions was first explored in the 1970s during research on using
in the oil industry. In 1982, the first meaningful field effort, the
Capture Project, was enacted in America, primarily aimed at carbon dioxide capture and storage within geological formations5. In the late 1990s, research in CCS took shape and started to include chemical and physical absorption technologies. Over the course of the 2010s, the technology of CCS was rolled out in major projects including Boundary Dam, Petra Nova, and Gorgon. In Saskatchewan, Canada, the Boundary Dam is the first major relay to integrate CCS technology within the coal sector8. This large post-combustion carbon capture system, Petra Nova, is in operation around Houston, Texas9. The Gorgon project in Western Australia is the largest natural gas project in the world, utilizing 11 important CCS elements 10.
Over the last few years, the attention has moved to blending renewable energy sources into CCS, making the carbon capture method more sustainable and efficient5. The world of renewable energy and CCS includes two key technologies: Direct Air Capture (DAC) and Bioenergy with CCS (BECCS). The latest technology in capture involves using either chemical or physical processes directly in the air, called DAC11. Being super versatile, this technology is pricey, with costs of
600 per ton 12. In a sustainable move, BECCS combines the generation of biomass energy with capturing and storing carbon dioxide. Biomass specifically is helpful because it absorbs
during growth, which is then captured and stored after combustion, leading to negative emissions13. Unfortunately, the costs associated with biomass feedstock and transportation make BECCS challenging to implement14.
Chemical Absorption is a more common and dated CCS technology that involves capturing as a liquid solvent.
’s reaction with the solvent forms a compound that can be separated and regenerated, releasing pure
. Just like a majority of CCS technologies, chemical absorption also has a downside. Significant amounts of energy are required to regenerate the solvent, meaning there will be a significant increase in operational costs15. Physical Adsorption, another CCS technology, utilizes solid adsorbents to capture
. While this process requires less energy than chemical adsorption, it is very difficult to scale up for large industrial applications16. Membrane Separation is a CCS technology that involves using select membranes to filter out
from other gases. While it has lower costs compared to chemical absorption, membrane separation is less efficient, with a lower likelihood of achieving high
purity and efficient separation13.
These are just a few of the many CCS technologies available, but each has its own challenges. Fortunately, with the rise of AI technology, some of these gaps could be filled. AI has the ability to optimize numerous aspects of CCS technologies such as the efficiency of the carbon capture processes and storage site selection and monitoring. This paper intends to illustrate the multitude of ways AI can help improve CCS technologies, and ultimately the environment.
CCS Technology | Estimated Cost (per ton of ![]() | Estimated Cost With AI (per ton of ![]() | Cost Factors |
Chemical Adsorption | $40 – $100 | $32 – $80 | High energy consumption, solvent costs, infrastructure |
Physical Adsorption | $30 – $70 | $24 – $56 | Adsorbent material costs, regeneration energy |
Membrane Separation | $50 – $150 | $40 – $120 | Membrane costs, performance degradation, and purity challenges |
Direct Air Capture (DAC) | $100 – $600 | $80 – $480 | High energy requirements, renewable energy integration |
Bioenergy with CCS (BECCS) | $60 – $120 | $48 – $96 | Biomass feedstock, transportation, land use |
AI Applications in CCS
Implementing AI in CCS offers a promising pathway to enhance efficiency and reduce the costs of capturing, transporting, and storing carbon dioxide. By maximizing AI utility, various processes within CCS can be optimized, making it a more viable method to combat climate change. This section will explore how AI can be applied to the three main stages of CCS: capture, transport, and storage.
Capture
AI optimizes capture across various methods. For instance, chemical absorption sees a 20% reduction in operational costs due to AI-guided energy management. Direct Air Capture (DAC), while costly ($100-$600 per ton), benefits from AI-driven process optimization, cutting costs by 20%. Membrane separation achieves a 10% improvement in
purity levels, potentially lowering capture costs by $20 per ton18. Artificial intelligence can help create the design and development of innovative materials for capturing
. The integration of AI simulations and machine learning models lets researchers rapidly find the best materials for upcoming CCS technologies.
AI sensors provide real-time monitoring capabilities for capture systems, able to detect changes in levels and adjust capture mechanisms in those situations. To utilize AI fully, the capture process should put its most attention on maintenance. According to Figure 1, capture technologies such as post-combustion, pre-combustion, and Oxy-fuel Combustion spend anywhere from at least $3 all the way to $10 per ton of
on maintenance.


In 2023 alone, there was a 48% increase in capture through CCS, capturing 361 million tons per annum (Mtpa)20. The calculations shown in the equations below prove the extensive maintenance costs in CCS each year, ranging anywhere from $1.08 billion to $3.6 billion dollars.
Fortunately, AI can analyze data from sensors and other monitoring devices to predict when maintenance will be needed. It can also identify issues before they become critical, reducing downtime and avoiding costly emergency repairs21.
Furthermore, AI has the ability to develop detailed simulations of a variety of capture technologies, allowing researchers to explore different configurations digitally without the costs and other constraints of physical testing22. These simulations can help identify the most efficient and economically viable methods of capturing . Artificial intelligence models the interactions in the
capture process to predict new materials and methods, speeding up and making the development of
capture techniques cheaper23.
To put it simply, integrating AI technologies into how we capture increases efficiency while reducing expenses. With AI for optimization and sensor technology, CCS can be made far better at being reliable, efficient, and able to grow. These new advancements significantly improve the prospects for CCS as a solution to help us fight climate change by lowering emissions. AI largely aids in CCS capture, and it’s equally valuable for transport and storage.
Transport
The transportation process is a crucial aspect of CCS because without it, carbon dioxide can not be stored underground, and if carbon dioxide isn’t stored safely underground, then there is a potential risk of it entering the atmosphere. AI-enabled pipeline route optimization reduces transport costs by 15%, saving $15 million annually for a $100 million pipeline system. Similarly, AI predictive analytics for maintenance can lower operational downtime, reducing costs by 20%. By analyzing traffic and terrain data, AI also enhances truck and train efficiency, cutting fuel use and travel time. There are three main methods of transport in CCS: pipelines, ships, and trucks and trains. Each of the methods of transportation has its advantages and disadvantages.
Pipelines are the most common and cost-effective method of transporting . While they can move large volumes of
over long distances, they are only used in North America and Europe. Moreover, there are a lot of public concerns about pipelines such as potential leaks, accidents, disruption, and more24.
Ships are used to transport across bodies of water where pipelines might not be feasible. Currently, ships are mainly utilized in Northern Europe25. The public acceptance of
pipelines, especially in Northern Europe, is generally low. This has driven the region to explore alternate methods like shipping. Initiatives such as Northern Lights have been designed to overcome the resistance of pipeline projects by offering to ship the
by sea26. Although this seems like a safer option, this method of transportation requires specialized ships and infrastructure27.
Trucks and trains are also used to transport but are less commonly used. They are used to transport a small amount of
over short distances. However, this method incurs higher operational costs and emits more
, making it less optimal for large-scale operations28.
CCS transportation might be greatly enhanced through the use of AI across a variety of methods. By studying terrain and weather, AI pipeline route optimization reduces transport costs by 15%, saving $15 million annually for a $100 million pipeline system. AI’s ability to monitor and predict pipeline failures lowers leak risks by 80%, saving $8 million in annual maintenance costs. Similarly, AI-powered logistics for ships and trucks reduces emissions from transportation by 10%29. Also, based on its ability to forecast equipment failures, AI helps us avoid leaks and downtime. AI systems can catch leaks or other issues live so that we can respond quickly 30.
By using AI, ships can more efficiently route their travel, cutting down on fuel needs and transport time, all the while taking weather and ocean currents into account31. Making use of machine learning can help us predict when ships require maintenance, and that cuts down on the chance of accidents. Supply chain management in CCS can be enhanced by AI through its use of data from every stage of transportation. With this in mind, AI can acquire and analyze data from capture, via transport, to the last storage spot. This ensures all angles of the process run smoothly and are the most efficient32.
AI technology can optimize truck and train routes now by focusing on traffic and road conditions, which helps to reduce both fuel and travel time36. Just as other transport methods do, AI can predict when maintenance is needed to cut down on the risk of interruptions and boost time management33.
In conclusion, by integrating AI into transportation methods, the overall CCS process can be a lot more efficient, safe, and reliable. Overall, AI can help with the widespread adoption of CCS and help combat climate change.
Storage
The CCS storage phase includes several advanced methods such as choosing a location, tracking, and assessing the risks. AI technology has proven to be a key system for improving processes and fostering both safety and reliability.
AI investigates a large dataset to find the best locations for storage and guarantees that geological formations can keep safe for great periods of time34. Complex machine learning formulas evaluate rocks’ geological attributes—composition and porosity mainly—to assess their feasibility for storage35. AI can predict global stability by analyzing geological properties such as porosity and fault lines. Our data strategy lends itself to quicker and less costly site selection processes, yielding results that are reliable 36.
Thanks to machine learning, we can understand how behaves once stored, which means we can simulate it more effectively in geological systems37. These predictive algorithms permit us to forecast how
circulation will change over time, pointing out risks tied to leakage or pressure building. By thinking ahead about issues, engineers can improve storage layouts and built-in measures for longer facility durability38.
AI improves the relevance of monitoring systems by pooling data from multiple sensors to offer quick assessments of conditions inside storage locations. Regularly looking for changes helps you discover odd signs that might predict future challenges. The ability of systems to find trends and patterns using sensor data with AI can create frameworks for early warnings about leaks or problems. When we act early, we can jump in quickly to manage leak risks and keep storage locations reliable and safe39.
Artificial intelligence helps to analyze different scenarios, informing better risk planning and improvements in safety. The framework these models use to analyze storage risks considers a variety of elements including geological properties and previous trends. With a detailed view of what could happen, AI improves both decision-making and risk management.
AI increases the storage phase of CCS by enabling the automation of complex processes and giving insightful data. Thanks to its site selection, predictive modeling, monitoring, and risk assessment capabilities, CCS is a much better and scalable option for managing carbon. As we proceed with AI development, its integration into CCS methods will fundamentally affect our progress in climate change challenges and storage safety and efficiency.
AI reduces costs and enhances efficiency across all phases of CCS. In capture, AI-driven optimization decreases energy consumption by up to 20%, lowering costs by $10 per ton. In transport, AI logistics reduce delays by 15%, saving $15 million annually for a $100 million pipeline system. In storage, predictive modeling minimizes leak risks, cutting maintenance costs by 40%. These contributions underscore AI’s transformative potential for scalable CCS adoption.
Case Study: The Petra Nova Project.
There are a few CCS projects across the world in which AI has already started to play a crucial role, but the one that stands out the most is the Petra Nova Project.
The Petra Nova Project, located in Texas, is one of the world’s largest post-combustion CCS systems. Working since 2016, this project is a collaboration between JX Nippon Oil & Gas Exploration Corporation and NRG Energy. This project captures from a coal-powered plant and utilizes it for enhanced oil recovery40. Despite facing some challenges, this project has been extremely useful in the reduction of
entering the atmosphere. As shown in Figure 3, prior to the implementation of the project in January 2017, around 2,000 pounds of
per megawatt-hour (MWh) were emitted. After the project began, the emission intensity of the coal-fired generator dropped significantly, falling below 1,500 pounds of
per MWh41.

The Petra Nova Project faced financial and operational obstacles, with high costs and downtime being major issues. AI-driven predictive analytics could have reduced downtime by identifying equipment failures early, potentially saving $5 million annually. Optimized temperature and pressure parameters using AI could have lowered energy usage by 15%, further reducing per-ton capture costs by $10. According to The Institute for Energy Economics and Financial Analysis, NRG Energy recorded $310 million dollars in impairment charges over a few years 43. In addition, the project’s carbon capture costs were notably high. The Department of Energy (DOE) official suggested that to achieve commercial viability, the cost of carbon capture would need to be reduced by half to $30 per metric ton, implying that the actual costs were around $60 per metric ton 44. Comparatively, the Boundary Dam project in Canada reported capture costs of approximately $40 per metric ton due to improved energy integration. If Petra Nova had adopted AI-driven predictive analytics and process optimization, these tools could have helped to lower costs by reducing energy consumption and minimizing unplanned maintenance downtime45.
Petra Nova was expected to operate 85% of the time but it failed to meet this requirement due to numerous technical problems and prolonged downtime periods46. Additionally, the significant drop in oil prices in 2020 impacted the economic feasibility of using for enhanced oil recovery (EOR). The project was designed to sell the captured
to oil fields to boost oil production, but falling oil prices disrupted the revenue stream and rendered EOR less viable47. Lessons from the Northern Lights project in Norway suggest that diversifying
utilization strategies, such as using AI to optimize shipping schedules for alternative storage or industrial uses, could help projects like Petra Nova maintain economic feasibility even during market fluctuations48.
AI can address many of the challenges Petra Nova faced. Predictive analytics could have reduced downtime by identifying equipment failures early, minimizing the costs associated with unplanned shutdowns49. For example, by analyzing operational data, AI could have flagged maintenance needs before failures occurred, potentially saving millions in annual repair costs. Additionally, AI’s ability to optimize temperature and pressure parameters could have lowered energy usage, further reducing the per-ton cost of capture50. Real-time monitoring of systems using AI would have ensured consistent performance, addressing operational inefficiencies that contributed to downtime and financial losses. By implementing such AI-driven solutions, Petra Nova could have achieved greater reliability and scalability, making the project more economically viable.
In contrast, the Northern Lights project demonstrates how AI can proactively mitigate economic and operational risks. By employing AI for real-time monitoring and optimizing transport logistics via ships, the project addresses public resistance to pipelines while maintaining cost-effective operations51. These insights can inform improvements for projects like Petra Nova, ensuring they overcome similar challenges and contribute effectively to CCS scalability worldwide. The Petra Nova Project can improve its operations by working with AI, which will help lower costs and make CCS a more sustainable enterprise in the future.
Challenges and Limitations:
The introduction of AI in CCS technologies opens up large opportunities for efficiency improvement and cost savings, yet multiple challenges need to be addressed. AI might save us lots on capture costs, but DAC and BECCS technology costs are still quite high. On top of that, incorporating AI into the CCS process is complicated and needs a lot of both money and time.
Quantifying these challenges highlights AI’s potential to address them effectively. For instance, pipeline failure rates in CCS systems currently stand at approximately 5%, leading to annual costs of $10 million in repairs and operational losses. AI-driven predictive analytics and real-time monitoring systems have the potential to reduce these failures by up to 80%, saving $8 million annually while improving operational reliability. These cost savings underline the importance of integrating AI to mitigate risks in CCS operations44.
For the successful application of AI in CCS, data quality matters importantly. AI systems rely on vast amounts of accurate and reliable data to function effectively, but discrepancies in data collection process can hinder performance48. Some data integration challenges include differing data format, incomplete datasets, and errors. To address these issues, implementing standardized data collection protocols and advanced data-cleaning algorithms can significantly enhance AI’s reliability and also effectiveness. AI has the potential to help cut carbon capture expenses, but there are many tech, logistical, and economic hurdles to clear on the way to scaling.
For AI technology to be adopted in CCS plants, we need to balance regulatory compliance with public support. Exploring the complicated regulatory frameworks can waste a lot of time and resources, and public trust is important for successful CCS projects. In order to address public concerns, implementing real-time dashboards that display CCS operations and emissions reductions can increase transparency and build trust. Engaging communities through educational campaigns and open forums can help address any safety and environmental concerns. On the regulatory side, harmonizing frameworks across regions is critical to ensuring efficient implementation of AI-enhanced CCS. The public’s questions about storage and transportation safety and environmental risks should be handled transparently and with this effectiveness52.
The risks of CCS for the environment mean we need to monitor closely and think about risk management. AI can monitor real-time and predict leaks, yet these efforts won’t eliminate all risks. The long-term resilience of storage facilities depends on response planning and continuous monitoring53.
While AI will certainly lift CCS technologies, there are underlying challenges that it can’t resolve completely by itself. Tackling costs, data quality, and environmental risks can improve AI as an asset in CCS and ultimately the effort to combat climate change52.
Future Direction/Discussion:
The integration of AI into CCS technologies presents a transformative approach to addressing climate change. This section provides an overview of this paper’s key findings and discusses future directions.
- Enhanced efficiency in
capture: AI has shown immense potential in optimizing the
capture process. ML algorithms have the potential to analyze various parameters to reduce operational costs effectively. AI-driven simulations can help accelerate the time of development and lead to greater efficiency23 ,54.
- Improved transportation: AI can optimize the transport of
by analyzing environmental factors and predicting maintenance needs. This can enhance the efficiency of all transportation methods including, but not limited to, pipelines, ships, trucks, and trains53.
- Improved storage: AI can help predict the site selection, monitoring, and risk assessment processes for
storage. By analyzing geographical data and predicting the behavior of
within storage sites, AI can be an extremely useful tool in the storage sector55 ,56.
- Petra Nova Project: One real-life example of how AI can be used in a beneficial manner is the Petra Nova Project. This case illustrates how AI has the potential to overcome some of the challenges such as scalability and economic viability in a real-world setting45 ,57.
A phased roadmap for AI_CCS integration is essential to overcome barriers and become more scalable and economically feasible.
- Year 1: Pilot AI in transport optimization, reducing pipeline costs by 10%.
- Year 3: Scale AI to capture efficiency, achieving a 20% reduction in energy usage for carbon capture processes.
- Year 5: Fully integrate AI across CCS systems, targeting a 30% total cost reduction and ensuring scalability.
Over reliance on AI poses risks such as algorithmic errors in predictive monitoring or biases in data interpretation. To mitigate risks, its important to implement validation processes and maintain human oversight. For example, pairing AI outputs with human expertise in monitoring ensures both accuracy and adaptability in response to unexpected challenges.
Future directions for the integration of AI in CCS technologies include the development of more advanced materials, the incorporation of renewable energy sources, and the achievement of regulatory compliance coupled with public acceptance. In the coming years, there should be a greater reliance on AI to advance material development, addressing critical issues such as climate change. The integration of AI with renewable energy and CCS has the potential to foster a more sustainable environment. However, the successful implementation of AI in real-world applications necessitates gaining public trust. One effective strategy for building this trust is through increased public awareness of the significant benefits AI can bring to society21.
Conclusion
The application of AI in CCS exhibits a revolutionary approach that has the ability to transform most factors including efficiency, flexibility, and economic aspects of CCS which is a vital tool for achieving climate change objectives. CCS technology contains many components that can significantly reduce cost and can make operations more reliable and efficient through the installation and integration of AI into systems when compared with the traditional models. AI is most effective when dealing with real-life issues, and CCS is no exception. The Petra Nova Project illustrates AI’s capability in addressing the issues with CCS implementation which encompass maintenance prediction, process enhancement, and risk mitigation. However, the incorporation of AI in CCS has its challenges which include costs, data quality, regulatory challenges, and public acceptance. These challenges are yet to be addressed to effectively implement AI in real life. To ensure successful AI integration, next steps include investments in predictive maintenance and transport optimization, partnerships between AI developers and energy stakeholders, and expanding pilot programs to help ensure scalable solutions. Centralized data platforms and transparent communication are key for fostering collaboration and public acceptance. With these steps, AI can help CCS become a cost-effective solution. In the future, as CCS continues to incorporate AI, coupled with material science and renewable integration, the enhancement of the scalability of CCS in carbon management can be achieved. Therefore, the actualization of AI in CCS can help provide an impetus in meeting the global climate change objectives besides promoting sustainability.
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