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Abstract Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO2). The prediction of CO2 solubility in ILs is crucial for optimizing CO2 capture processes. This study investigates the use of deep learning models for CO2 solubility prediction in ILs with a comprehensive dataset of 10,116 CO2 solubility data in 164 kinds of ILs under different temperature and pressure conditions. Deep neural network models, including Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), were developed to predict CO2 solubility in ILs. The ANN and LSTM models demonstrated robust test accuracy in predicting CO2 solubility, with coefficient of determination (R2) values of 0.986 and 0.985, respectively. Both model's computational efficiency and cost were investigated, and the ANN model achieved reliable accuracy with a significantly lower computational time (approximately 30 times faster) than the LSTM model. A global sensitivity analysis (GSA) was performed to assess the influence of process parameters and associated functional groups on CO2 solubility. The sensitivity analysis results provided insights into the relative importance of input attributes on output variables (CO2 solubility) in ILs. The findings highlight the significant potential of deep learning models for streamlining the screening process of ILs for CO2 capture applications.

Abstract This paper studies an adjacent accumulation discrete grey model to improve the prediction of the grey model and enhance the utilization of new data. The impact of COVID-19 on the global economy is also discussed. Two cases are discussed to prove the stability of the adjacent accumulation discrete grey model, which helped the studied model attain higher forecasting accuracy. Using the adjacent accumulation discrete grey model, non-renewable energy consumption in G20 countries from 2022 to 2026 is predicted based on their consumption data from 2011 to 2021. It is proven that the adjacent accumulation exhibits sufficient accuracy and precision. Forecasting results obtained in this paper show that energy consumption of all the non-renewable sources other than coal has an increasing trend during the forecasting period, with the USA, Russia, and China being the biggest consumers. Natural gas is the most consumed non-renewable energy source between 2022 and 2026, whereas hydroelectricity is the least consumed. The USA is the biggest consumer of Nuclear energy among the G20 countries, whereas Argentina consumed only 0.1 Exajoules of nuclear energy, placing it at the end of nuclear energy consumers.