Recent Advances in Integrated Carbon Dioxide Capture: Exploring Carbon Capture Methods and AI Integration

Abstract

The decarbonization of the industry sector is required to attain the Sustainable Development Goals, as it is the largest contributor to greenhouse gas emissions and energy consumption. Among the primary strategies for industrial decarbonization are hydrogen, energy efficiency improvement, and carbon capture. The advancement of novel carbon dioxide capture or utilization technologies is crucial for decreasing carbon dioxide emissions from the utilization of fossil energy and alleviating global warming.  Among various post-combustion carbon capture technologies, absorption-based carbon capture (ACC) is the most advanced technology for CO2 abatement. Nevertheless, ACC is energy-intensive and necessitates substantial heating and cooling utility consumption, which leads to elevated operational expenses. These utility consumptions would be beneficial for the technical and financial feasibility assessment of various ACC process designs if they could be estimated with reliability and speed. The integration of Artificial Intelligence offers revolutionary solutions to current difficulties, namely in sectors like agriculture that are vital for human survival. This paper highlights the significant influence of Artificial Intelligence on controlled agricultural practices in greenhouses, based on data from an agricultural competition where teams utilized AI-driven processes to maximise greenhouse performance. The purpose of this investigation is to create an artificial intelligence (AI)-based methodology that forecasts the utility consumption of various ACC process designs. The findings indicate that implementing control systems enhanced by Artificial Intelligence can effectively decrease energy consumption, namely in terms of thermal loads, while maintaining crop production, quality, and profitability. In the present day, digital technologies such as artificial intelligence (AI) and data aggregation are employed to address all of the issues. Consequently, this investigation is conceptualised by AI-assisted carbon capturing. This study would facilitate academics in forecasting the growth of AI-supported carbon capture, within the framework of lowering greenhouse gas emissions and accomplishing climate action (SDG 13)