Using the ionic liquid [emim][Tf2N] as a physical solvent, it was found by aspen plus simulation that it was possible to attempt to capture CO2 from the flue gas discharged from the coal-fired unit of the power plant. Using the combination of model calculation and experimental determination, the density, isostatic heat capacity, viscosity, vapor pressure, thermal conductivity, surface tension and solubility of [emim][Tf2N] were obtained. Based on the NRTL model, the Henry coefficient and NRTL binary interaction parameters of CO2 dissolved in [emim][Tf2N] were obtained by correlating [emim][Tf2N] with the gas-liquid equilibrium data of CO2. Firstly, the calculated relevant data is imported into Aspen plus, and the whole process model of the ionic liquid absorption process is established. Then the absorption process is optimized according to the temperature distribution in the absorption tower to obtain a new absorption process. Finally, the density, constant pressure heat capacity, surface tension, thermal conductivity, viscosity of [emim][Tf2N] were changed to investigate the effect of ionic liquid properties on process energy consumption, solvent circulation and heat exchanger design.The results showed that based on the composition of the inlet gas stream to the absorbers, CO2 with a capture rate of 90% and a mass purity higher than 99.5% was captured; These results indicate that the [emim][Tf2N] could be used as a physical solvent for CO2 capture from coal-fired units.In addition,The results will provide a theoretical basis for the design of new ionic liquids for CO2 capture.
Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.
Reducing the emissions of greenhouse gas is a worldwide problem that needs to be solved urgently for sustainable development in the future. The solubility of CO2 in ionic liquids is one of the important basic data for capturing CO2. Considering the disadvantages of experimental measurements, e.g., time-consuming and expensive, the complex parameters of mechanism modeling and the poor stability of single data-driven modeling, a multi-model fusion modeling method is proposed in order to predict the solubility of CO2 in ionic liquids. The multiple sub-models are built by the training set. The sub-models with better performance are selected through the validation set. Then, linear fusion models are established by minimizing the sum of squares of the error and information entropy method respectively. Finally, the performance of the fusion model is verified by the test set. The results showed that the prediction effect of the linear fusion models is better than that of the other three optimal sub-models. The prediction effect of the linear fusion model based on information entropy method is better than that of the least square error method. Through the research work, an effective and feasible modeling method is provided for accurately predicting the solubility of CO2 in ionic liquids. It can provide important basic conditions for evaluating and screening higher selective ionic liquids.
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