As a new type of solvent, ionic liquids (ILs) are considered to be promising in the field of CO 2 capture. Due to the lack of typical industrial process cases, it is necessary to conduct a life cycle assessment (LCA) to determine the environmental performance of IL-based chemical processes and explore possible improvements. In this work, the LCA method is used to evaluate the carbon capture and storage (CCS) process from the solvent production stage to the use stage with [bmim][Tf 2 N] IL as the CO 2 absorption solvent. The effects of different processes and CO 2 capture ratios on the environmental performance of CCS-IL are also explored, and some suggestions for improvement are provided. The LCA results show that the main environmental impact stage of the CCS-IL process is the solvent production stage, which has effects that are far beyond the environmental impact of the solvent use stage, and anion production is the main factor. The LCA results of the solvent use process show that optimization of the CCS-IL process can reduce the environmental burden of the solvent use stage. From the perspective of the global environment, the introduction of waste heat resources into the CCS process will have a positive impact on the environment. The sensitivity analysis shows that reducing the CO 2 capture ratio can reduce the environmental burden of the CCS-IL process, but it requires factories to bear a higher carbon tax and environmental responsibility. LCA analysis will help decision makers to further understand the IL-based chemical process and conduct accurate evaluations.

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Generally, the selection of an entrainer in extractive distillation depends on relative volatility, but our previous research shows that relying on relative volatility alone may not achieve the best economic and environmental benefits in the process. In this work, the effects of different entrainers on the separation process of ternary mixtures containing two minimum boiling azeotropes by extractive distillation were studied using a dichloromethane (DCM)/methanol (MeOH)/water system as an example. Taking the gas emission and total annual cost as objectives, the separation processes of eight entrainers in the DCM/MeOH/water system were optimized and compared by a multiobjective optimization method. The optimization results show that 1,3-propanediol had the best economic and environmental benefits, although its relative volatility was not the best. The limitations of screening entrainers by relying on relative volatility alone were demonstrated. To achieve optimal economic and environmental benefits, entrainer screening needs to consider the relative volatility, thermodynamic properties of the entrainers, and possible thermal integration schemes. This work provides a reference for the screening of entrainers and process design in extractive distillation processes.

In this study, novel molecular structure encoding descriptors composed of feature encoding and one‐hot encoding was developed and then convolutional autoencoder was used to denoise based on the structure of ionic liquids (ILs). It could be used to predict the CO2 solubility in ILs at different temperatures and pressures, when combined with three different machine learning algorithms (multilayer perceptron [MLP], random forest [RF], and support vector machine [SVM]). Statistics of the prediction results show that the newly proposed molecular structure‐based coding has better regression prediction performance than the conventional molecular cheminformatics descriptors. SE‐MLP model with R2 of 0.9873 and mean square error of 0.0007 has the best performance in predicting the CO2 solubility in ILs. In addition, the relationship between features and dissolved CO2 capacity was analyzed through model interpretation to retrieve physical insights for the underlying system. This work provided a new predictive tool for enriching and refining data on CO2 solubility in ILs and for solving phase equilibrium problems.

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