The Potential of Machine Learning for Enhancing CO2 Sequestration, Storage, Transportation, and Utilization-based Processes: A Brief Perspective

Gupta, Surojit; Li, Lan

doi:10.1007/s11837-021-05079-x

Cited by 28 publications

(8 citation statements)

References 114 publications

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“…By optimizing the energy consumption of CDR processes, AI algorithms can minimize energy usage without compromising carbon removal efficiency, thus reducing the carbon footprint of CDR facilities. AI can also optimize various carbon removal processes, such as capture, storage, and utilization, leading to more efficient and environmentally friendly operations [3,82]. For instance, AI algorithms can determine optimal operating conditions for carbon capture technologies, such as solvent selection, temperature, and pressure, to maximize efficiency and minimize energy consumption, thereby reducing the environmental impact of carbon removal.…”

Section: Environmental Impact Reductionmentioning

confidence: 99%

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

Li,

Luo,

Liu

et al. 2024

Processes

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In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios.

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Section: Environmental Impact Reductionmentioning

confidence: 99%

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

Li,

Luo,

Liu

et al. 2024

Processes

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“…As depicted in Fig. 2, artificial intelligence has the potential to enhance the efficiency and efficacy of these processes by identifying appropriate geological formations for carbon storage (Jin et al 2022), predicting the behavior of carbon dioxide once it is introduced into the storage sites (Chinh Nguyen et al 2022), optimizing the injection process (Elsheikh et al 2022), monitoring storage sites (Kishor and Chakraborty 2022), and devising new and innovative carbon sequestration methods (Gupta and Li 2022). Moreover, artificial intelligence can aid in accomplishing sustainability objectives and achieving carbon neutrality by reducing greenhouse gas emissions and mitigating climate change (Jahanger et al 2023;Sahil et al 2023).…”

Section: Carbon Sequestration and Storagementioning

confidence: 99%

Artificial intelligence-based solutions for climate change: a review

Chen

Zhang

et al. 2023

Environ Chem Lett

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Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.

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“…They labeled hybrid processes as such when a variety of CO 2 capture and utilization approaches could be used in synergy. [335] To track the critical operational parameters for the CO 2 collection process, Wang et al proposed the ML model utilizing the dataset from soft sensors. [336] Using dynamic operational data from a simulator, a substitute intelligent model for this monitoring strategy was discovered.…”

Section: Machine Learning In the Adsorption Process And Synthesismentioning

confidence: 99%

Natural Products Derived Porous Carbons for CO₂ Capture

Khosrowshahi,

Mashhadimoslem,

Shayesteh

et al. 2023

Advanced Science

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As it is now established that global warming and climate change are a reality, international investments are pouring in and rightfully so for climate change mitigation. Carbon capture and separation (CCS) is therefore gaining paramount importance as it is considered one of the powerful solutions for global warming. Sorption on porous materials is a promising alternative to traditional carbon dioxide (CO2) capture technologies. Owing to their sustainable availability, economic viability, and important recyclability, natural products‐derived porous carbons have emerged as favorable and competitive materials for CO2 sorption. Furthermore, the fabrication of high‐quality value‐added functional porous carbon‐based materials using renewable precursors and waste materials is an environmentally friendly approach. This review provides crucial insights and analyses to enhance the understanding of the application of porous carbons in CO2 capture. Various methods for the synthesis of porous carbon, their structural characterization, and parameters that influence their sorption properties are discussed. The review also delves into the utilization of molecular dynamics (MD), Monte Carlo (MC), density functional theory (DFT), and machine learning techniques for simulating adsorption and validating experimental results. Lastly, the review provides future outlook and research directions for progressing the use of natural products‐derived porous carbons for CO2 capture.

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The Potential of Machine Learning for Enhancing CO2 Sequestration, Storage, Transportation, and Utilization-based Processes: A Brief Perspective

Cited by 28 publications

References 114 publications

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

Artificial intelligence-based solutions for climate change: a review

Natural Products Derived Porous Carbons for CO₂ Capture

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The Potential of Machine Learning for Enhancing CO2 Sequestration, Storage, Transportation, and Utilization-based Processes: A Brief Perspective

Cited by 28 publications

References 114 publications

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

Artificial intelligence-based solutions for climate change: a review

Natural Products Derived Porous Carbons for CO2 Capture

Contact Info

Product

Resources

About

Natural Products Derived Porous Carbons for CO₂ Capture