Machine learning applications in catalytic hydrogenation of carbon dioxide to methanol: A comprehensive review

EG Aklilu, T Bounahmidi - International Journal of Hydrogen Energy, 2024‏ - Elsevier
The catalytic hydrogenation of carbon dioxide (CO 2) to methanol presents a significant
opportunity for both mitigating climate change and producing a valuable chemical feedstock …

Advances in plastic to fuel conversion: reactor design, operational optimization, and machine learning integration

K Paavani, K Agarwal, SS Alam, S Dinda… - Sustainable Energy & …, 2025‏ - pubs.rsc.org
Plastic waste management is a pressing global problem that requires sustainable solutions
to mitigate environmental harm. To this end, pyrolysis offers a practical method for …

DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion

W Wang, W Tian, X Hu, Y Hong, F Chai, D Xu - Journal of Hydrology, 2024‏ - Elsevier
Accurate runoff forecasting facilitates effective water resource management, and ensures the
sustainable allocation of water for agricultural, industrial, and domestic use. Accurate runoff …

[HTML][HTML] Machine learning-based multi-objective optimization of concentrated solar thermal gasification of biomass incorporating life cycle assessment and techno …

Y Fang, X Li, X Wang, L Dai, R Ruan, S You - Energy Conversion and …, 2024‏ - Elsevier
The combination of solar and biomass energy systems is regarded as a highly promising
technology for tackling the challenges related to greenhouse gas emissions from energy …

Experimental analysis and physics-informed optimization algorithm for ejector in fuel cells based on boundary-breaking and dimension reduction

C Li, J Fu, Y Huang, X Sun - Renewable Energy, 2024‏ - Elsevier
As an important auxiliary component in renewable devices such as fuel cells and solar
cooling systems, ejectors have demonstrated excellent potential due to their parasitic power …

[HTML][HTML] Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process

H Ali, Z Zhang, R Safdar, MH Rasool, Y Yao… - Digital Chemical …, 2024‏ - Elsevier
Unexpected accidents and events in industrial chemical processes have resulted in a
considerable number of causalities and property damage. Safety process management in …

[HTML][HTML] Recent advances in Fe-based metal-organic frameworks: Structural features, synthetic strategies and applications

K Mosupi, M Masukume, G Weng, NM Musyoka… - Coordination Chemistry …, 2025‏ - Elsevier
Metal organic frameworks (MOFs) are very exciting porous materials owing to their unique
properties such as high surface areas, high pore volume, tunable functionalities and great …

Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils

R Proshad, SMAA Asha, R Tan, Y Lu… - Journal of Hazardous …, 2025‏ - Elsevier
Abstract Machine learning (ML) models for accurately predicting heavy metals with
inconsistent outputs have improved owing to dataset outliers, which influence model …

[PDF][PDF] Comparative Analysis of Cross-Validation Techniques: LOOCV, K-Folds Cross-Validation, and Repeated K-Folds Cross-Validation in Machine Learning Models

VW Lumumba, D Kiprotich, NG Makena… - Am. J. Theor. Appl …, 2024‏ - researchgate.net
Effective model evaluation is crucial for robust machine learning, and cross-validation
techniques play a significant role. This study compares Repeated k-folds Cross Validation, k …

A Hybrid Physics–Machine Learning Approach for Modeling Plastic–Bed Interactions during Fluidized Bed Pyrolysis

S Iannello, A Friso, F Galvanin, M Materazzi - Energy & Fuels, 2025‏ - ACS Publications
The axial mixing/segregation behavior of single plastic particles in a bubbling fluidized bed
reactor has been investigated by noninvasive X-ray imaging techniques in the temperature …