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Machine learning applications in catalytic hydrogenation of carbon dioxide to methanol: A comprehensive review
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 …
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
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 …
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
Accurate runoff forecasting facilitates effective water resource management, and ensures the
sustainable allocation of water for agricultural, industrial, and domestic use. Accurate runoff …
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 …
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 …
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
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 …
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
Unexpected accidents and events in industrial chemical processes have resulted in a
considerable number of causalities and property damage. Safety process management in …
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
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 …
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
Abstract Machine learning (ML) models for accurately predicting heavy metals with
inconsistent outputs have improved owing to dataset outliers, which influence model …
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
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 …
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
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 …
reactor has been investigated by noninvasive X-ray imaging techniques in the temperature …