Optimizing Carbon Capture Efficiency through AI-Driven Process Automation for Enhancing Predictive Maintenance and CO2 Sequestration in Oil and Gas Facilities
DOI:
https://doi.org/10.54536/ajec.v3i3.3766Keywords:
Artificial Intelligence (AI), CO2, Carbon Capture & Sequestration (CCS), Oil & Gas Facilities, OptimizationAbstract
The increasing worldwide focus, on cutting down carbon emissions has heightened the need for cutting edge carbon capture and storage (CCUS or CCSU) in the oil and gas industry sector. This examination delves into how AI powered automation processes can boost the effectiveness of carbon capture systems and improve maintenance practices, in oil and gas installations. Combining intelligence (AI) with procedures and systems in place to predict outcomes accurately can enhance the dependability and effectiveness of CCS technologies by tackling essential issues like constant monitoring in real-time and identifying faults for system optimization purposes efficiently. AI-powered automation processes implemented by facilities have the potential to boost the rates of CO2 sequestration while minimizing interruptions, resulting in a more effective carbon capture infrastructure. The methodology involves a systematic review of existing literature, peer-reviewed articles, case studies, and industry reports on AI techniques, such as machine learning and neural networks, in CCS. Databases like Google Scholar and IEEE Xplore were used, focusing on keywords like “AI in CCS” and “predictive maintenance. The analysis also explores real-life examples from oil and gas firms that have effectively integrated AI solutions into their carbon capture and storage endeavors, hence shedding light on strategies, hurdles, and upcoming developments in the field. The evaluation highlights how AI-driven automation processes significantly improve the efficiency and environmental sustainability of oil and gas facilities.
Downloads
References
Abdallah, S., Godwins, O. P., & Ijiga, A. C. (2024). AI-powered nutritional strategies: Analyzing the impact of deep learning on dietary improvements in South Africa, India, and the United States. Magna Scientia Advanced Research and Reviews, 11(02), 320–345. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0125.pdf
Aboi, E. J. (2024). Religious, ethnic and regional identities in Nigerian politics: A shared interest theory. African Identities, 1-18.
Adu-Twum, H. T., Sarfo, E. A., Nartey, E., Adesola Adetunji, A., Ayannusi, A. O., & Walugembe, T. A. (2024). Role of advanced data analytics in higher education: Using machine learning models to predict student success. International Journal of Computer Applications Technology and Research, 13(08), 54-61. https://doi.org/10.7753/IJCATR1308.1006
Ahmed, M., & Qureshi, F. (2021). Failure prediction and fault detection using AI in carbon capture technologies: A review of current methodologies. Energy, 227, 120487. https://doi.org/10.1016/j.energy.2021.120487
Alison, I. (2022). Unlocking the potential of AI for carbon capture and sequestration: A path to a sustainable future. Traction Technology. https://www.tractiontechnology.com/blog/unlocking-the-potential-of-ai-for-carbon-capture-and-sequestration-a-path-to-sustainable-future
Atache, S., Ijiga, A. C., & Olola, T. M. (2024). Enhancing performance in the Nigerian civil service through advanced AI technologies: A case study of Biggan applications. Malaysian Journal of Human Resources Management, 1(2), 143–151. https://mjhrm.com.my/archive/2mjhrm2024/2mjhrm2024-143-151.pdf
Awotiwon, B. O., Enyejo, J. O., Owolabi, F. R. A., Babalola, I. N. O., & Olola, T. M. (2024). Addressing supply chain inefficiencies to enhance competitive advantage in low-cost carriers (LCCs) through risk identification and benchmarking applied to Air Australasia’s operational model. World Journal of Advanced Research and Reviews, 23(03), 355–370. https://wjarr.com/content/addressing-supply-chain-inefficiencies-enhance-competitive-advantage-low-cost-carriers-lccs
Ayoola, V. B., Ugoaghalam, U. J., Idoko, P. I., Ijiga, O. M., & Olola, T. M. (2024). Effectiveness of social engineering awareness training in mitigating spear phishing risks in financial institutions from a cybersecurity perspective. Global Journal of Engineering and Technology Advances, 20(03), 094–117. https://gjeta.com/content/effectiveness-social-engineering-awareness-training-mitigating-spear-phishing-risks
Balogun, T. K., Kalu, O. C., Ijiga, A. C., Olola, T. M., & Ahmadu, E. O. (2024). Building advocacy coalitions and analyzing lobbyists’ influence in shaping gun control policies in a polarized United States. International Journal of Scholarly Research in Multidisciplinary Studies, 5(01), 088–102. https://srrjournals.com/ijsrms/content/building-advocacy-coalitions-and-analyzing-lobbyists-influence-shaping-gun-control-policies
Brown, P., Yang, C., & Patel, R. (2021). Lessons learned from AI implementation in carbon capture technologies: Insights from industrial case studies. Journal of Cleaner Production, 284, 124758. https://doi.org/10.1016/j.jclepro.2020.124758
Chen, H., Liu, S., & Yang, Q. (2020). Machine learning applications in carbon capture and storage: Trends, challenges, and future directions. Energy, 198, 117300. https://doi.org/10.1016/j.energy.2020.117300
Chen, Y., Wu, L., & Zhang, S. (2022). Challenges and opportunities in scaling AI solutions for carbon capture and storage. Energy Reports, 8, 950-960. https://doi.org/10.1016/j.egyr.2022.01.024
Coker, J. O., Ijiga, A. C., Uzougbo, N. S., Okolie, C. A., Oguejiofor, B. B., & Akagha, O. V. (2013). Exploring alternative dispute resolution mechanisms in resolving commercial and labor disputes: Comparative analysis of Nigeria and the United States. Socio Economy and Policy Studies, 3(2), 109-113.
Coker, J. O., Ijiga, A. C., Uzougbo, N. S., Okolie, C. A., Oguejiofor, B. B., & Akagha, O. V. (2013). Exploring alternative dispute resolution mechanisms in resolving commercial and labor disputes: Comparative analysis of Nigeria and the United States. Socio Economy and Policy Studies, 3(2), 119-123.
Coker, J. O., Ijiga, A. C., Uzougbo, N. S., Okolie, C. A., Oguejiofor, B. B., & Akagha, O. V. (2023). The impact of labor laws on fundamental human rights in the workplace: A comparative analysis between Nigeria and the United States. Social Values and Society, 5(2), 54-58.
Davies, P., Zhang, H., & Li, X. (2021). Overcoming technical and operational barriers to AI adoption in carbon capture systems. Journal of Cleaner Production, 293, 126093. https://doi.org/10.1016/j.jclepro.2021.126093
Ebenibo, L., Enyejo, J. O., Addo, G., & Olola, T. M. (2024). Evaluating the sufficiency of the data protection act 2023 in the age of Artificial Intelligence (AI): A comparative case study of Nigeria and the USA. International Journal of Scholarly Research and Reviews, 5(1), 088–107. https://srrjournals.com/ijsrr/content/evaluating-sufficiency-data-protection-act-2023-age-artificial-intelligence-ai-comparative
Enyejo, J. O., Adeyemi, A. F., Olola, T. M., Igba, E., & Obani, O. Q. (2024). Resilience in supply chains: How technology is helping USA companies navigate disruptions. Magna Scientia Advanced Research and Reviews, 11(02), 261–277. https://doi.org/10.30574/msarr.2024.11.2.0129
Enyejo, J. O., Obani, O. Q., Afolabi, O., Igba, E., & Ibokette, A. I. (2024). Effect of augmented reality (AR) and virtual reality (VR) experiences on customer engagement and purchase behavior in retail stores. Magna Scientia Advanced Research and Reviews, 11(02), 132–150. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0116.pdf
Gao, H., Liu, Y., & Li, L. (2021). Leveraging artificial intelligence for process automation in carbon capture technologies: Challenges and prospects. Journal of Cleaner Production, 294, 126353. https://doi.org/10.1016/j.jclepro.2021.126353
Garg, A., Bhattacharya, S., & Gupta, P. (2022). A comparative study of predictive and preventive maintenance for industrial systems: Application in carbon capture technologies. Journal of Cleaner Production, 366, 132987. https://doi.org/10.1016/j.jclepro.2022.132987
Giuliano, L. (2022). Predictive maintenance in industry 4.0: Applications and advantages. LinkedIn. https://www.linkedin.com/pulse/predictive-maintenance-industry-40-applications-giuliano-liguori-/
Godwins, O. P., David-Olusa, A., Ijiga, A. C., Olola, T. M., & Abdallah, S. (2024). The role of renewable and cleaner energy in achieving sustainable development goals and enhancing nutritional outcomes: Addressing malnutrition, food security, and dietary quality. World Journal of Biology Pharmacy and Health Sciences, 19(1), 118–141. https://wjbphs.com/sites/default/files/WJBPHS-2024-0408.pdf
Gupta, S., & Li, L. (2022). The potential of machine learning for enhancing CO2 sequestration, storage, transportation, and utilization-based processes: A brief perspective. JOM, 74(2), 414-428.
Huang, Y., Wang, Z., & Liu, F. (2021). AI applications in monitoring and validation of CO2 sequestration sites: Enhancing long-term storage security. Journal of Environmental Management, 295, 113026. https://doi.org/10.1016/j.jenvman.2021.113026
Ibokette, A. I., Aboi, E. J., Ijiga, A. C., Ugbane, S. I., Odeyemi, M. O., & Umama, E. E. (2024). The impacts of curbside feedback mechanisms on recycling performance of households in the United States. World Journal of Biology Pharmacy and Health Sciences, 17(2), 366-386.
Ibokette, A. I., Ogundare, T. O., Danquah, E. O., Anyebe, A. P., Agaba, J. A., & Olola, T. M. (2024). The impacts of emotional intelligence and IoT on operational efficiency in manufacturing: A cross-cultural analysis of Nigeria and the US. Computer Science & IT Research Journal, 5(8), 1464. https://doi.org/10.51594/csitrj.v5i8.1464
Ibokette, A. I., Ogundare, T. O., Danquah, E. O., Anyebe, A. P., & Agaba, J. A. (2024). Optimizing maritime communication networks with virtualization, containerization, and IoT to address scalability and real-time data processing challenges in vessel-to-shore communication. Global Journal of Engineering and Technology Advances, 20(2), 135–174. https://gjeta.com/sites/default/files/GJETA-2024-0156.pdf
Idoko, P. I., Igbede, M. A., Manuel, H. N. N., Ijiga, A. C., Akpa, F. A., & Ukaegbu, C. (2024). Assessing the impact of wheat varieties and processing methods on diabetes risk: A systematic review. World Journal of Biology Pharmacy and Health Sciences, 18(02), 260–277. https://wjbphs.com/sites/default/files/WJBPHS-2024-0286.pdf
Idoko, D. O., Adegbaju, M. M., Nduka, I., Okereke, E. K., Agaba, J. A., & Ijiga, A. C. (2024). Enhancing early detection of pancreatic cancer by integrating AI with advanced imaging techniques. Magna Scientia Advanced Biology and Pharmacy, 12(2), 051–083. https://magnascientiapub.com/journals/msabp/sites/default/files/MSABP-2024-0044.pdf
Idoko, D. O., Agaba, J. A., Nduka, I., Badu, S. G., Ijiga, A. C., & Okereke, E. K. (2024). The role of HSE risk assessments in mitigating occupational hazards and infectious disease spread: A public health review. Open Access Research Journal of Biology and Pharmacy, 11(02), 011–030. https://oarjbp.com/content/role-hse-risk-assessments-mitigating-occupational-hazards-and-infectious-disease-spread
Idoko, D. O., Mbachu, O. E., Ijiga, A. C., Okereke, E. K., Erondu, O. F., & Nduka, I. (2024). Assessing the influence of dietary patterns on preeclampsia and obesity among pregnant women in the United States. International Journal of Biological and Pharmaceutical Sciences Archive, 8(1), 85–103. https://ijbpsa.com/content/assessing-influence-dietary-patterns-preeclampsia-and-obesity-among-pregnant-women-united
Igba, E., Adeyemi, A. F., Enyejo, J. O., Ijiga, A. C., Amidu, G., & Addo, G. (2024). Optimizing business loan and credit experiences through AI-powered chatbot integration in financial services. Finance & Accounting Research Journal, 6(8), 1436-1458. https://doi.org/10.51594/farj.v6i8.1406
Igba, E., Danquah, E. O., Ukpoju, E. A., Obasa, J., Olola, T. M., & Enyejo, J. O. (2024). Use of building information modeling (BIM) to improve construction management in the USA. World Journal of Advanced Research and Reviews, 23(3), 1799–1813. https://wjarr.com/content/use-building-information-modeling-bim-improve-construction-management-usa
Ijiga, A. C., Aboi, E. J., Idoko, P. I., Enyejo, L. A., & Odeyemi, M. O. (2024). Collaborative innovations in artificial intelligence (AI): Partnering with leading U.S. tech firms to combat human trafficking. Global Journal of Engineering and Technology Advances, 18(3), 106-123. https://gjeta.com/sites/default/files/GJETA-2024-0046.pdf
Ijiga, A. C., Abutu, E. P., Idoko, P. I., Ezebuka, C. I., Harry, K. D., Ukatu, I. E., & Agbo, D. O. (2024). Technological innovations in mitigating winter health challenges in New York City, USA. International Journal of Science and Research Archive, 11(01), 535–551. https://ijsra.net/sites/default/files/IJSRA-2024-0078.pdf
Ijiga, A. C., Abutu, E. P., Idoko, P. I., Agbo, D. O., Harry, K. D., Ezebuka, C. I., & Umama, E. E. (2024). Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America. International Journal of Biological and Pharmaceutical Sciences Archive, 7(1), 048–063. https://ijbpsa.com/sites/default/files/IJBPSA-2024-0015.pdf
Ijiga, A. C., Enyejo, L. A., Odeyemi, M. O., Olatunde, T. I., Olajide, F. I., & Daniel, D. O. (2024). Integrating community-based partnerships for enhanced health outcomes: A collaborative model with healthcare providers, clinics, and pharmacies across the USA. Open Access Research Journal of Biology and Pharmacy, 10(2), 081–104. https://oarjbp.com/content/integrating-community-based-partnerships-enhanced-health-outcomes-collaborative-model
Ijiga, A. C., Olola, T. M., Enyejo, L. A., Akpa, F. A., Olatunde, T. I., & Olajide, F. I. (2024). Advanced surveillance and detection systems using deep learning to combat human trafficking. Magna Scientia Advanced Research and Reviews, 11(01), 267–286. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0091.pdf
Ijiga, O. M., Idoko, I. P., Ebiega, G. I., Olajide, F. I., Olatunde, T. I., & Ukaegbu, C. (2024). Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention.
Islam, M. S., Islam, M. S. S., & Jaman, A. (2024). Enhancing SCR Switching Performance of Gas Drilling Rigs in Humid Climates Using Sensor and Relay-Based Control Circuitry with Failure Prevention and Warning Feedback Methodology. American Journal of Innovation in Science and Engineering, 3(3), 49–54. https://doi.org/10.54536/ajise.v3i3.3568
Jayaram, E., Reed, A., Kane, L., Krawchuk, P., Kannike, A., and Stander, B. (2023). Successfully Transitioning the O&G Workforce to a Cleaner, Greener Future. ADIPEC. doi: https://doi.org/10.2118/216547-MS
Jiang, L., Zhao, X., & Wu, Z. (2021). Enhancing system reliability through predictive maintenance in carbon capture facilities: A machine learning approach. Energy Reports, 2123-2134. https://doi.org/10.1016/j.egyr.2021.04.034
Johnson, M., Wang, Y., & Patel, R. (2020). Data privacy, security, and ethical concerns in AI for carbon capture technologies. Energy Policy, 147, 111834. https://doi.org/10.1016/j.enpol.2020.111834
Kaggwa, S., Eboigbe, E. O., Eyo-Udo, N. L., Ijiga, A. C., Uwaoma, P. U., & Daraojimba, D. O. (2023). A review of the impact of digital transformation on HR practices and strategies in the Nigerian renewable energy sector. Journal of Third World Economics (JTWE), 1(1), 36-43.
Kim, S., Lee, J., & Park, D. (2022). AI-driven optimization of CO2 transport and storage in carbon capture systems: A review of methodologies. Energy Reports, 8, 5629-5638. https://doi.org/10.1016/j.egyr.2022.09.082
Lee, H., Kim, S., & Choi, J. (2020). AI-driven predictive maintenance in oil and gas facilities: A case study on carbon capture technology. Energy, 197, 117189. https://doi.org/10.1016/j.energy.2020.117189
Li, J., Wang, T., & Zhang, Q. (2021). Lessons from AI integration in carbon capture: Key insights from industrial applications. Journal of Cleaner Production, 312, 127829. https://doi.org/10.1016/j.jclepro.2021.127829
Miller, S., Cooper, J., & Zhang, T. (2020). Balancing AI investment costs with long-term benefits in carbon capture technologies. Energy Economics, 87, 104748. https://doi.org/10.1016/j.eneco.2020.104748
Mugo, M. E., Nzuma, R., Adibe, E. A., Adesiyan, R. E., Obafunsho, O. E., & Anyibama, B. (2024). Collaborative efforts between public health agencies and the food industry to enhance preparedness. International Journal of Science and Research Archive, 12(2), 1111-112. https://doi.org/10.30574/ijsra.2024.12.2.1370
Mugo, M. E., Nzuma, R., Tade, O. O., Epia, G. O., Olaniran, G. F., & Anyibama, B. (2024). Nutritional interventions to manage diabetes complications associated with foodborne diseases: A comprehensive review. World Journal of Advanced Research and Reviews, 23(1), 2724-2736. https://doi.org/10.30574/wjarr.2024.23.1.2274
Okunade, B. A., Adediran, F. E., Coker, J. O., Bakare, S. S., Ijiga, A. C., Odulaja, B. A., & Adewusi, O. E. (2023). Ethical considerations in community engagement: A literature review of practices in Nigerian NGOs. World Journal of Advanced Research and Reviews, 20(3), 384-400. https://wjarr.com/sites/default/files/WJARR-2023-2429.pdf
Olajire, A. A. (2010). CO2 capture and separation technologies for end-of-pipe applications: A review. Energy, 35(6), 2610-2628. https://doi.org/10.1016/j.energy.2010.02.030
Oloba, B. L., Olola, T. M., & Ijiga, A. C. (2024). Powering reputation: Employee communication as the key to boosting resilience and growth in the U.S. service industry. World Journal of Advanced Research and Reviews, 23(03), 2020-2040. https://doi.org/10.30574/wjarr.2024.23.3.2689
Owolabi, F. R. A., Enyejo, J. O., Babalola, I. N. O., & Olola, T. M. (2024). Overcoming engagement shortfalls and financial constraints in small and medium enterprises (SMEs) social media advertising through cost-effective Instagram strategies in Lagos and New York City. International Journal of Management & Entrepreneurship Research, 6(8). https://doi.org/10.51594/ijmer.v6i8.1462
Patil, V., Desai, R., & Pawar, S. (2021). AI-powered predictive maintenance systems for industrial applications: Carbon capture and storage case study. Journal of Industrial Information Integration, 23, 100219. https://doi.org/10.1016/j.jii.2021.100219
Pooja, K., & Dean, M. (2019). 10 ways AI will transform the oil & gas industry. Oil & Gas Middle East. https://www.oilandgasmiddleeast.com/news/10-ways-ai-will-transform-the-oil-gas-industry
Ramirez, A., Chen, W., & Peters, G. (2020). Leveraging AI for optimized CO2 capture processes: A review of recent advancements. Journal of Cleaner Production, 256, 120321. https://doi.org/10.1016/j.jclepro.2020.120321
Singh, P., Gupta, R., & Kumar, A. (2020). The benefits of AI-driven predictive maintenance for reducing downtime and maintenance costs in carbon capture facilities. Journal of Cleaner Production, 258, 120678. https://doi.org/10.1016/j.jclepro.2020.120678
Unachukwu, C., Ijiga, A. C., Okunade, B. A., Osawaru, B., & Akinwolemiwa, D. I. (2023). A comprehensive review of multilingual leadership: The role of French language skills in global business dynamics. World Journal of Advanced Research and Reviews, 20(3), 941-951.
Umar, J. A., Olorunsola, K., Hassan, U. M., Yusuf, Y. Y., Ahmed, A. H., & Garba, H. (2024). An Assessment of Groundwater Potential for Water Supply in Misau and Dambam Local Government Areas of Bauchi State, Nigeria. Applied Research and Innovation, 2(2), 13–22. https://doi.org/10.54536/ari.v2i2.1812
Wang, J., Li, G., & Zhang, S. (2020). Predictive analytics for process efficiency in carbon capture technologies: A machine learning approach. Energy, 195, 116975. https://doi.org/10.1016/j.energy.2020.116975
Wang, T., Wang, Y., & Zhang, X. (2021). The application of artificial intelligence in carbon capture and storage: A comprehensive review. Renewable and Sustainable Energy Reviews, 144, 111014. https://doi.org/10.1016/j.rser.2021.111014
Yadav, S., & Mondal, S. S. (2022). A review on the progress and prospects of oxy-fuel carbon capture and sequestration (CCS) technology. Fuel, 308, 122057.
Yang, Z., Sun, L., & Huang, X. (2021). Neural networks for real-time system control in carbon capture and storage technologies. Energy Conversion and Management, 236, 114018. https://doi.org/10.1016/j.enconman.2021.114018
Zhang, X., Song, C., & Liu, L. (2022). Artificial intelligence-driven technologies for CO2 capture and storage systems: A review. Applied Energy, 314, 118858. https://doi.org/10.1016/j.apenergy.2022.118858
Zhang, X., Sun, Q., & Zhao, Y. (2021). Case study of AI integration in carbon capture systems: Improving efficiency and scalability in industrial applications. Journal of Cleaner Production, 279, 123487. https://doi.org/10.1016/j.jclepro.2020.123487
Zhao, L., Zhang, Y., & Li, Y. (2022). Machine learning algorithms for optimizing carbon capture and storage processes: A review of current trends and future opportunities. Journal of Cleaner Production, 350, 131437. https://doi.org/10.1016/j.jclepro.2022.131437
Zhou, J., Zhang, X., & Liu, H. (2021). Artificial intelligence for enhancing carbon capture efficiency: Opportunities and challenges. Energy Reports, 7, 3784-3793. https://doi.org/10.1016/j.egyr.2021.06.058
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Abraham Peter Anyebe, Owura Kwaku Kodie Yeboah, Oladipupo Idris Bakinson, Tayo Yusuf Adeyinka, Francisca Chinonye Okafor

This work is licensed under a Creative Commons Attribution 4.0 International License.