review · Sustainable Development
Artificial intelligence offers operational and management enhancements across carbon capture technologies to reduce emissions and improve resource efficiency. Digital algorithms can analyse extensive datasets from capture plants to identify optimal operational settings and recognise patterns at large scale. Within industrial supply chains, sensor mechanisms equipped with artificial intelligence can identify operational failures early, allowing for timely protective interventions. In materials research, generative design assisted by artificial intelligence supports the development of novel carbon-absorbing materials, including polymers and metal-organic frameworks. Furthermore, these computational methods increase the accuracy of reservoir simulations and help control carbon dioxide injection systems for long-term storage or enhanced oil recovery. Artificial intelligence can also link renewable energy carbon capture initiatives with smart grid systems to enhance operational efficiency.
Tackling climate change requires more effective methods to reduce industrial emissions and manage captured carbon dioxide. Integrating artificial intelligence into carbon capture operations allows facilities to operate with greater efficiency and reduced waste. Refining material discovery and system monitoring through computational tools supports broader sustainability goals while ensuring that carbon storage systems perform reliably.
Potential applications include operational software for carbon capture plants, predictive failure sensors for supply chains, computational discovery platforms for capture materials, and injection control tools for reservoir storage operators. Prospective users include industrial emitters, carbon storage developers, and grid operators. As a broad review, the covered applications range from early-stage computational materials design to applied control systems for operational injection and monitoring.
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Abstract Artificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO 2 , such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO 2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO 2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.
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DOI: 10.1002/sd.3222
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