Globally, emissions of greenhouse gases, such as carbon dioxide (CO2), have been increasing exponentially, with an annual growth rate of 2.7%. (1) Current levels of CO2 emissions are 50% higher than the levels recorded in 2000. Primary contributors to such emissions are the combustion of fossil fuels such as coal, oil, and natural gas for electricity generation, transportation, and industrial processes. Carbon emissions profoundly challenge our planet, triggering a cascade of environmental, economic, and social problems. These emissions and other greenhouse gases accumulate in the atmosphere and amplify the Earth’s natural greenhouse effect, resulting in global warming and climate change. This rapidly escalating phenomenon disrupts weather patterns, aggravates extreme weather events, increases sea-level rise, and endangers biodiversity. Addressing carbon emissions is more than just an environmental essential. It is a multifaceted challenge that necessitates immediate global collaboration and innovative approaches. Various strategies are being explored globally to address the objective of reducing carbon footprints. One of the pivotal families of approaches is carbon capture and utilization (CCU). CCU technologies capture the carbon and store it in the form of liquid or gas, which is utilized later to make some chemicals, such as methanol, ethanol, and dimethyl ether. Methanol can be used as an alternative fuel for vehicles. Implementing the CCU technology will help create sustainable, economically supportive systems and environments. (2−5)

Various researchers have contributed to developing efficient technologies for capturing carbon content from gaseous effluents from industries and other resources. Currently, industries are adopting these approaches rapidly to neutralize their carbon footprints in their processes. These include chemical and physical methodologies used for capturing CO2, such as absorption, adsorption, membrane separation, cryogenic separation, and chemical looping. Generally, absorption involves the use of liquid solvents to capture gaseous compounds. Aron and Tsouris (6) have utilized monoethanolamine (MEA) as a solvent to capture CO2 from flue gas streams. Similarly, the amine-based scrubbing approach is discussed by Wang et al. (7) and Rajendran et al. (8) Sorbent-based technologies can potentially reduce the overall cost associated with carbon capture significantly. (9) In the adsorption process, solid adsorbents such as silica, zeolites, alumina, metal oxides, activated carbon, and metal–organic frameworks (MOFs) were reported to capture CO2 from gas streams. (10−12) Membrane separation involves the use of selective membranes to separate CO2 from gas mixtures and has been studied by Wu et al. (13) and Pfister et al. (14) Cryogenic separation utilizes low temperatures to condense CO2 from the gas streams. This approach is reviewed extensively by Cann and Udemu. (15) Chemical looping combustion is a combustion process that involves the use of metal oxide particles as oxygen carriers. This method is proposed by Lyngfelt et al. (16) These methods represent diverse approaches for capturing CO2 from the emission stream, each with its advantages and challenges. The captured carbon dioxide is compressed and employed in various applications, including gas and oil recovery, agriculture, soda ash manufacturing, and the food industry. (17,18) Additionally, it can be utilized to produce value-added chemicals and fuels. Alternatively, the captured carbon dioxide can be stored in geological reservoirs or saline aquifers. (19−23) As there are several research works done in this area, it may be beneficial that some of this information can be summarized and/or presented in a useful manner.

Natural language processing (NLP) is a subset of artificial intelligence (AI), primarily focusing on inferring from textual datasets. These datasets, often termed nonstructural, pose a challenge for traditional machine learning or deep learning algorithms. Hence, NLP tools are indispensable for processing them effectively. The increasing accessibility of computational power and resources has further incentivized the utilization of available textual datasets. (24) In early 2000, neural language modeling was developed, in which the probability of the occurrence of the next word (token) is determined by previous work of Bengio et al. (25) Mikolov et al. proposed a word embedding process addressing the dense vector representation of text. (26)...

Figure 1. A descriptive representation of each step’s involvement in creating the CCU-Llama framework is encapsulated in the schematic diagram.

Figure 3. Bar plots to showcase (a) the top 30 journals with increasing frequency of reliable abstracts on CCU obtained from the Elsevier database and (b) the trend of published reliable articles about CCU since 2001

Figure 9. Performance of our trained Q&A model (which works as a chatbot, a part of CCU-Llama) is tested on a random question, and its response is compared with the response of standard ChatGPT in (a) the light green box, which stands for the CCU-Llama response; the light yellow box, which stands for the ChatGPT response; and (b) the CCU-Llama response shown in a graph visualization form.

Figure 10. Performance of our trained Q&A model (which works as a chatbot, a part of CCU-Llama) is tested on a random question, and its response is compared with the response of standard ChatGPT in (a) the light green box, which stands for the CCU-Llama response; the light yellow box, which stands for the ChatGPT response; and (b) the CCU-Llama response shown in a graph visualization form.

...These tasks, explicitly tailored for CCU, collectively form a versatile framework with the potential for extension to diverse domains. Our study enhances language models with domain specificity, addressing information extraction challenges in CCU and beyond. It is expected that CCU-Llama will come as a handy tool for both optimization of a process/technology and for someone looking to learn more about CCUs in general. At the moment, the process optimization component is not part of the CCU-Llama and we are working toward integrating this as a future direction. The knowledge resulting from CCU-Llama may be further processed by ranking the alternatives and including them in a framework for developing roadmaps to meet the net-zero emissions goal. (63,64) Additionally, CCU-Llama can be further used to learn and improvise the knowledge of CCU technologies as they update with time. To update the model based on recent CCU literature published after 2023, we provide an easy technique that can retrain our publicly available CCU-Llama model (hugging face URL: CCU-Llama) using the pretraining or fine-tuning approach. To make a model more effective, we can also adopt model pruning and knowledge distillation techniques that help remove the weights associated with older, less-relevant information. (65,66)