<scp>AI</scp> models for correlation of physical properties in system of <scp>1DMA2P‐CO<sub>2</sub>‐H<sub>2</sub>O</scp>

Liu, Helei; Jiang, Xiaotong; Idem, Raphael; Dong, Shoulong; Tontiwachwuthikul, Paitoon

doi:10.1002/aic.17761

Cited by 46 publications

(9 citation statements)

References 36 publications

(55 reference statements)

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“…Carbon dioxide (CO 2 ) has been accused as one of major responsible contributors for the increasingly serious climate problem due to the rising concentration of CO 2 in the atmosphere . CO 2 capture technology has been investigated widely for controlling CO 2 emissions from industrial flue gas, and the amine-based chemical absorption process has been regarded as the most mature method for the post-combustion CO 2 capture. , However, the large-scale application of amine-based CO 2 capture technology is still challenging, mainly because of the huge heat requirement in the amine solvent regeneration process (CO 2 desorption). , Many efforts have been made to overcome the drawbacks of the amine-based CO 2 capture technology including the development of new absorbents and the use of other new technologies for enhancing the CO 2 capture efficiency. − It was reported that the heat cost for solvent regeneration contributes more than 70% of the total heat cost, which constrains the industrial application of this technology. , …”

Section: Introductionmentioning

confidence: 99%

Energy-Saving Effect of Low-Cost and Environmentally Friendly Sepiolite as an Efficient Catalyst Carrier for CO₂ Capture

Zhang

et al. 2023

ACS Sustainable Chem. Eng.

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Recently, the development of efficient solid acid catalysts to promote the CO 2 desorption rate while reducing energy consumption has attracted much attention. In this work, low-cost environmentally friendly sepiolite (SEP) clay was evaluated as a support of metal oxide (Fe 2 O 3 , CuO) catalysts. By comparing their catalytic performances for CO 2 desorption from CO 2 -rich monoethanolamine (MEA) solution at 100 °C, the obtained results showed that the tested catalysts can accelerate the CO 2 release rate and reduce heat consumption in comparison with the non-catalytic MEA solution. The relative heat duty found decreased in the following order: blank (100%) > SEP (66.4%) > CuO-SEP (58.8%) > Fe 2 O 3 -SEP (54.0%). Recycling tests to study stability demonstrated that Fe 2 O 3 -SEP can maintain its catalytic efficiency after six recycling runs. Characterization studies revealed that a high mesoporous specific surface area and a high ratio of Brønsted and Lewis acid sites are beneficial to enhancing the activity of the catalysts. In addition, a possible catalytic mechanism for CO 2 desorption was proposed. As a result, this work proved that SEP has the potential to be a low-cost and competitive catalyst carrier for CO 2 capture.

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Section: Introductionmentioning

confidence: 99%

Energy-Saving Effect of Low-Cost and Environmentally Friendly Sepiolite as an Efficient Catalyst Carrier for CO₂ Capture

Zhang

et al. 2023

ACS Sustainable Chem. Eng.

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“…Useful solution properties and the value of mass transfer coefficients for stirred cell were reported too. These outcomes will assist in the development of artificial neural network (ANN) models, such as those developed by Liu et al for the properties of the tertiary amine 1DMA2P, and generic artificial intelligence (AI) models similar to those of Quan et al for predicting mass transfer coefficients in the absorbers. Finally, low-temperature desorption of AHPD/PZ mixtures over the γ-Al 2 O 3 catalyst was also explored to check for possible reduction in the stripping energy constraint.…”

Section: Introductionmentioning

confidence: 99%

Improved CO₂ Separation Using Aqueous Solutions of 2-Amino-2-hydroxymethyl-1,3-propanediol Promoted with Piperazine

2023

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An aqueous mixture of the sterically hindered amine 2-amino-2-hydroxymethyl-1,3-propanediol (AHPD) and the cyclic diamine piperazine (PZ) is a credible CO2 separation solvent. In this work, CO2 absorption kinetics in AHPD/PZ mixtures was investigated in a stirred cell reactor. The solute gas CO2 reacted in parallel with both amines AHPD and PZ. From the absorption rate measurements at 308 K, the second-order rate constant for the reaction between CO2 and PZ was found (k 2,PZ = 28 685 m3 (kmol s)−1). Several properties of this blend, such as density, viscosity, and CO2 diffusivity and solubility, were measured. Besides, the dependency of the equilibrium CO2 partial pressure on the loading capacity was studied in a low-pressure vapor–liquid equilibrium setup at 308 K. The highest value of CO2 solubility in this study was 0.83 mol CO2/mol amine, and the corresponding CO2 partial pressure was 130 kPa. The performance of the blend AHPD/PZ (1/0.5 kmol m–3) was tested in a continuous, closed-loop setup comprising an absorber (313 K) and a desorber (383 K). The efficiency of CO2 separation from a CO2/air mixture (12:88 v/v) was 62%, and the regeneration energy constraint was 4.21 MJ (kg CO2)−1. Finally, alumina catalyst was employed for faster CO2 desorption at 368 K in a batch setup. This comprehensive study will promote the application of this candidate AHPD/PZ solvent.

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“…ML models have the ability to learn complex nonlinear data patterns, and they can be effectively used for the estimation or prediction of state variables using easily available descriptor data . Although ML models have gained a lot of attention in recent years in the fields of finance, telecommunications, language processing, etc., the studies reporting the ML models for the prediction of CO 2 solubility in different solvents are very few. − Further, ML models have been employed for carbon capture, utilization and storage, discovery of porous materials for carbon capture, prediction of CO 2 absorption, prediction of thermodynamic properties of CO 2 in the solvent, study of the correlations between physical properties (density, viscosity, and specific heat capacity) and CO 2 loading, prediction of mass transfer coefficients in the CO 2 absorber, − design of new solvents, , etc. Furthermore, there are a few studies reporting ML models for the prediction of CO 2 solubility in different, physical, chemical, and ionic solvents, and the corresponding summary points are presented in Table , including the details on the type of CO 2 solvent, the ML model developed, input data used for model building, etc.…”

Section: Introductionmentioning

confidence: 99%

Reduced Order Machine Learning Models for Accurate Prediction of CO₂ Capture in Physical Solvents

Mehtab

Alam

Povari

et al. 2023

Environ. Sci. Technol.

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CO2 sorption in physical solvents is one of the promising approaches for carbon capture from highly concentrated CO2 streams at high pressures. Identifying an efficient solvent and evaluating its solubility data at different operating conditions are highly essential for effective capture, which generally involves expensive and time-consuming experimental procedures. This work presents a machine learning based ultrafast alternative for accurate prediction of CO2 solubility in physical solvents using their physical, thermodynamic, and structural properties data. First, a database is established with which several linear, nonlinear, and ensemble models were trained through a systematic cross-validation and grid search method and found that kernel ridge regression (KRR) is the optimum model. Second, the descriptors are ranked based on their complete decomposition contributions derived using principal component analysis. Further, optimum key descriptors (KDs) are evaluated through an iterative sequential addition method with the objective of maximizing the prediction accuracy of the reduced order KRR (r-KRR) model. Finally, the study resulted in the r-KRR model with nine KDs exhibiting the highest prediction accuracy with a minimum root-mean-square error (0.0023), mean absolute error (0.0016), and maximum R 2 (0.999). Also, the validity of the database created and ML models developed is ensured through detailed statistical analysis.

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AI models for correlation of physical properties in system of 1DMA2P‐CO₂‐H₂O

Cited by 46 publications

References 36 publications

Energy-Saving Effect of Low-Cost and Environmentally Friendly Sepiolite as an Efficient Catalyst Carrier for CO₂ Capture

Energy-Saving Effect of Low-Cost and Environmentally Friendly Sepiolite as an Efficient Catalyst Carrier for CO₂ Capture

Improved CO₂ Separation Using Aqueous Solutions of 2-Amino-2-hydroxymethyl-1,3-propanediol Promoted with Piperazine

Reduced Order Machine Learning Models for Accurate Prediction of CO₂ Capture in Physical Solvents

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AI models for correlation of physical properties in system of 1DMA2P‐CO2‐H2O

Cited by 46 publications

References 36 publications

Energy-Saving Effect of Low-Cost and Environmentally Friendly Sepiolite as an Efficient Catalyst Carrier for CO2 Capture

Energy-Saving Effect of Low-Cost and Environmentally Friendly Sepiolite as an Efficient Catalyst Carrier for CO2 Capture

Improved CO2 Separation Using Aqueous Solutions of 2-Amino-2-hydroxymethyl-1,3-propanediol Promoted with Piperazine

Reduced Order Machine Learning Models for Accurate Prediction of CO2 Capture in Physical Solvents

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Resources

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AI models for correlation of physical properties in system of 1DMA2P‐CO₂‐H₂O

Energy-Saving Effect of Low-Cost and Environmentally Friendly Sepiolite as an Efficient Catalyst Carrier for CO₂ Capture

Energy-Saving Effect of Low-Cost and Environmentally Friendly Sepiolite as an Efficient Catalyst Carrier for CO₂ Capture

Improved CO₂ Separation Using Aqueous Solutions of 2-Amino-2-hydroxymethyl-1,3-propanediol Promoted with Piperazine

Reduced Order Machine Learning Models for Accurate Prediction of CO₂ Capture in Physical Solvents