Artificial intelligence is widely useful in environment-related fields. Recently, there has been increasing research on using AI in carbon capture technology. Carbon capture technology is critical in tackling climate change by trapping carbon dioxide (CO2) emissions from power plants. However, the current carbon capture systems are inefficient and may consume significant energy.
Consequently, researchers from the University of Surrey have developed new research in carbon capture technologies. This study is on using artificial intelligence (AI) to enhance the efficiency of CO2 capture. By employing AI algorithms, the researchers achieved a 16.7% increase in CO2 capture and a substantial reduction of 36.3% in energy usage sourced from the UK’s national grid.
The system’s core is a packed bubble column (PBC) reactor. PBC is the interface between freshwater containing crushed limestone and flue gasses loaded with CO2. Through this reaction, CO2 converts into bicarbonate. Also, the researchers used machine learning techniques to develop data-driven surrogate dynamic models capable of forecasting the reactor’s CO2 capture rate and power consumption to optimize the system’s performance. Then, they trained these models on the data obtained from physics-based simulations. Additionally, they used separate long short-term memory (LSTM)–based models to predict wind energy availability and incoming flue gas CO2 concentrations.
One of the researchers and chair of sustainable processes at the University of Surrey’s School of Chemistry and Chemical Engineering emphasizes the conventional rigidity of carbon capture systems. He said that usually, carbon capture systems run constantly, at the same rate, regardless of the externally changing environment. However, they showed that teaching the system to make small adaptations can produce big energy savings and capture more carbon simultaneously. Another researcher said that while this research centered on enhanced weathering, the insights gained have broader implications for other carbon capture applications. He said the model can assist those who want to capture and store CO2 more efficiently with lower energy requirements.
By integrating these prediction models, the algorithm learned to modify the amount of water pumped based on variables like CO2 levels and wind speeds. Consequently, the system can conserve energy when facing reduced CO2 or diminished wind power input. The researchers found that in a month, it showed a 16.7% boost in carbon dioxide capture rates compared to traditional static methods. Also, the reliance on renewable energy significantly dropped from an average of 92.9% to just 56.6%.
In conclusion, this study by researchers at the University of Surrey showcases the potential of AI in carbon capture technologies. It also gives solutions to the challenges posed by differing CO2 levels. This study has paved the way for more flexible and sustainable CO2 capture systems, contributing significantly to the pursuit of UN sustainability goals. As the world finds solutions to climate change issues, this research stands for hope for a greener and more sustainable future. This technique can contribute to global sustainability efforts with further refinements, ensuring greener skies for future generations.
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