loading page

High-throughput computational screening of porous polymer networks for natural gas sweetening based on neural network
  • +3
  • Xuanjun Wu,
  • Yujing Wu,
  • Xiuyang Lu,
  • Zhixiang Cao,
  • Xionghui Wei,
  • Weiquan Cai
Xuanjun Wu
Wuhan University of Technology
Author Profile
Yujing Wu
Wuhan University of Technology
Author Profile
Xiuyang Lu
Wuhan University of Technology
Author Profile
Zhixiang Cao
Wuhan University of Technology
Author Profile
Xionghui Wei
Peking University
Author Profile
Weiquan Cai
Guangzhou University
Author Profile

Abstract

17,846 PPNs with the diamond-like topology were computationally screened to identify the optimal adsorbents for the removal of H2S and CO2 from humid natural gas based on the combination of molecular simulation and machine learning algorithms. The top-performing PPNs with the highest adsorption performance scores (APS) were identified based on their adsorption capacities and selectivity for H2S and CO2. The strong affinity between water molecules and the framework atoms has a significant impact on the adsorption selectivity of acid gases. We proposed two main design paths (LCD ≤ 4.648 Å, Vf ≤ 0.035, PLD ≤ 3.889 Å or 4.648 Å ≤ LCD ≤ 5.959 Å, ρ ≤ 837 kg·m-3) of high-performing PPNs. We also found that artificial neural network (ANN) could accurately predict the APS of PPNs. N-rich organic linkers and highest isosteric adsorption heat of H2S and CO2 are main factors that could enhance natural gas sweetening performance.

Peer review status:IN REVISION

25 Dec 2020Submitted to AIChE Journal
29 Dec 2020Assigned to Editor
29 Dec 2020Submission Checks Completed
06 Jan 2021Reviewer(s) Assigned
11 May 2021Editorial Decision: Revise Major
31 May 20211st Revision Received
01 Jun 2021Assigned to Editor
01 Jun 2021Submission Checks Completed