Generic AI models for mass transfer coefficient prediction in amine‐based CO <sub>2</sub> absorber, Part II: RBFNN and RF model-Reference-Cited by-同舟云学术

Generic AI models for mass transfer coefficient prediction in amine‐based CO ₂ absorber, Part II: RBFNN and RF model

Published:2022-09-17 Issue:1 Volume:69 Page:
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE Journal

Author:

Quan Hong¹,Dong Shoulong¹^ORCID,Zhao Dongfang¹,Li Hansheng¹,Geng Junming¹,Liu Helei¹²^ORCID

Affiliation:

1. International Innovation Institute of Carbon Capture and Utilization (I3CCU) School of Chemistry and Chemical Engineering, Beijing Institute of Technology Beijing People's Republic of China

2. Department of Chemical and Biological Engineering The University of British Columbia Vancouver British Columbia Canada

Funder

Beijing Institute of Technology

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aic.17904

Reference35 articles.

1. AI models for correlation of physical properties in system of 1DMA2P‐CO ₂ ‐H ₂ O

2. Prediction of solubility of carbon dioxide in different polymers using support vector machine algorithm

3. Application of ANFIS soft computing technique in modeling the CO 2 capture with MEA, DEA, and TEA aqueous solutions

4. Practical use of statistical learning theory for modeling freezing point depression of electrolyte solutions: LSSVM model

5. Analyzing the experimental data of CO 2 equilibrium absorption in the aqueous solution of DEA + MDEA with Random Forest and Leverage method

Cited by 47 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering;Neural Computing and Applications;2024-05-13

2. Target-specific modification of diethylenetriamine with hydroxyalkyls: Efficient absorbents for CO2 capture;Separation and Purification Technology;2024-05

3. Exploring the role of surface and porosity in CO2 capture by CaO-based adsorbents through response surface methodology (RSM) and artificial neural networks (ANN);Journal of CO2 Utilization;2024-05

4. Improved prediction of reaction kinetics for amine absorbent-based carbon capture using reactive site-based transition state conformer search method;Fuel;2024-04

5. A generic machine learning model for CO2 equilibrium solubility into blended amine solutions;Separation and Purification Technology;2024-04