Recent progress on advanced solid adsorbents for CO2 capture: from mechanism to machine learning

MS Khosrowshahi, AA Aghajari, M Rahimi… - Materials Today …, 2024 - Elsevier
Environmental pollution has become a serious issue due to the rapid development of
urbanization, industrialization, and vehicle traffic. Notably, fossil fuel combustion significantly …

Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques

M Rahimi, H Mashhadimoslem, HV Thanh, B Ranjbar… - Energy, 2023 - Elsevier
Pyrolysis, as a thermochemical conversion of biomass, is a superior biofuel production
procedure. The determining procedure for the optimal operational parameters, biomass …

Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

HV Thanh, M Rahimi, S Tangparitkul… - International Journal of …, 2024 - Elsevier
This study introduces machine learning (ML) algorithms to predict hydrogen (H 2)
thermodynamic properties for geological storage, focusing on its mixtures with natural gas …

A multi-criteria decision-making (MCDM) approach to determine the synthesizing routes of biomass-based carbon electrode material in supercapacitors

M Rahimi, HV Thanh, I Ebrahimzade… - Journal of Cleaner …, 2023 - Elsevier
The selection of desirable synthesis procedures to achieve the idea of physiochemical and
capacitive properties of activated carbons (ACs) can be carried out by the multi-criteria …

Toward sustainable culture media: Using artificial intelligence to optimize reduced-serum formulations for cultivated meat

A Nikkhah, A Rohani, M Zarei, A Kulkarni… - Science of The Total …, 2023 - Elsevier
When considering options for future foods, cell culture approaches are at the fore, however,
culture media to support the process has been identified as a significant contributor to the …

Hydrogen storage on porous carbon adsorbents: rediscovery by nature-derived algorithms in random forest machine learning model

HV Thanh, S Ebrahimnia Taremsari, B Ranjbar… - Energies, 2023 - mdpi.com
Porous carbons as solid adsorbent materials possess effective porosity characteristics that
are the most important factors for gas storage. The chemical activating routes facilitate …

Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches

M Rahimi, MH Abbaspour-Fard, A Rohani… - Industrial & …, 2022 - ACS Publications
The CO2 emission issue has triggered the promotion of carbon capture and storage (CCS),
particularly bio-route CCS as a sustainable procedure to capture CO2 using biomass-based …

[HTML][HTML] Experimental analysis of cycle tire pyrolysis oil doped with 1-decanol+ TiO2 additives in compression ignition engine using RSM optimization and machine …

KS Kumar, A Razak, A Yadav, PSR Rao… - Case Studies in Thermal …, 2024 - Elsevier
This study investigates the effect of TiO 2 nano additives in conjunction with 1-decanol on
the performance and emission characteristics of biodiesel. The analysis involves four …

Spatial-temporal modeling of oil condition monitoring: A review

Y Pan, B Liang, L Yang, H Liu, T Wu, S Wang - Reliability Engineering & …, 2024 - Elsevier
Lubricating oil plays a vital role as the information carrier for equipment tribological
performance. Therefore, oil condition monitoring (OCM) serves as a crucial technology for …

Evaluating wear volume of oligoether esters with an interpretable machine learning approach

H Wang, C Zhang, X Yu, Y Li - Tribology Letters, 2023 - Springer
Wear is a familiar tribological failure in liquid lubrication, especially for low-viscosity base
oils without any additives. Therefore, it is necessary to understand and evaluate the anti …