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Densities and viscosities of aqueous solutions of potassium glycinate (PG) þ piperazine (PZ) composed of x PG /x PZ (mol/mol) = 1.0/0, 0.9/0.1, 0.8/0.2, 0.7/0.3, and 0.6/0.4 have been measured at (288.15, 303.15, 313.15, and 323.15) K. Surface tensions of aqueous solutions of (PG þ PZ) have been determined at 288.15 K. Correlations by means of mathematical fitting were developed for the predictions of density, viscosity, and surface tension of the aqueous solutions of (PG þ PZ) using the experimental data of this work. Results show that the densities and viscosities decrease as the temperature increases. The densities of the studied solutions decrease with the increase of PZ mole fraction in the solutions, while viscosities of the solutions increase. The surface tensions of the solutions almost decrease linearly as the PZ mole fraction in the solution increases from 0 to 0.4. The prediction values from correlation equations for density, viscosity, and surface tension are in good agreement with the experimental values. The correlation equations for each property can offer additional data for aqueous solutions of (PG þ PZ).

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The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

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Phosphate or borate as an activating agent was added into the aqueous glycinate to form amino acid salt-based complex absorbents. Capture of CO2 by the complex absorbents was studied theoretically and experimentally using a hollow fiber membrane contactor. Reaction mechanism concerning the activation effect of phosphate and borate was presented theoretically. A mathematical model was developed to simulate mass transfer of the membrane contactor. The effects of absorbent and gas CO2 concentrations on mass transfer flux and CO2 capture efficiency were investigated. Surface tension of the complex absorbents and operational stability of the membrane contactor were also discussed in this work. Results show that the activation follows the sequence B4O7 2− > PO4 3− > HPO4 2− > H2PO4 −. The mathematical model was validated by comparing theoretical with experimental data. The mass transfer flux increased almost linearly with the increase of gas CO2 concentration. Surface tension data revealed that the complex absorbents could not wet the membrane micropores. The flux was basically kept constant during the prolonged period of operation. The complex absorbents modified by the phosphates and borates are good potential absorbents for CO2 capture.