On the public citation graph

Economic Screening of Solid Sorbents for Point-source CO2 Capture using Machine Learning Surrogate Models

https://doi.org/10.2139/ssrn.5067564
0 signed · 10 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10450-022-00361-zopenalex-snapshot
Integrated uncertainty quantification and sensitivity analysis of single-component dynamic column breakthrough experiments
inferredcites →https://doi.org/10.1016/j.cej.2021.130248openalex-snapshot
Efficient hybrid multiobjective optimization of pressure swing adsorption
inferredcites →https://doi.org/10.1016/j.jcou.2023.102474openalex-snapshot
A systematic review of machine learning approaches in carbon capture applications
inferredcites →https://doi.org/10.1016/j.rser.2020.110490openalex-snapshot
Post-combustion carbon capture
inferredcites →https://doi.org/10.1016/j.seppur.2020.116651openalex-snapshot
Experimentally validated machine learning frameworks for accelerated prediction of cyclic steady state and optimization of pressure swing adsorption processes
inferredcites →https://doi.org/10.1017/9781009157926openalex-snapshot
Climate Change 2022 - Mitigation of Climate Change
inferredcites →https://doi.org/10.1021/acs.iecr.0c02339openalex-snapshot
Generalized, Adsorbent-Agnostic, Artificial Neural Network Framework for Rapid Simulation, Optimization, and Adsorbent Screening of Adsorption Processes
inferredcites →https://doi.org/10.1021/acs.iecr.9b04173openalex-snapshot
Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption
inferredcites →https://doi.org/10.1021/acssuschemeng.0c08933openalex-snapshot
Practically Achievable Process Performance Limits for Pressure-Vacuum Swing Adsorption-Based Postcombustion CO 2 Capture
inferredcites →https://doi.org/10.1039/d1ee02395kopenalex-snapshot
Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.