Results 201 to 210 of about 36,988 (280)
Abstract Ilmenite electric arc furnaces (EAFs) are used for smelting titanium‐iron oxide ore at high temperatures generated by electrical arcs to produce titanium slag and pig iron. As these units are pushed to their limits, ensuring safe and reliable operation becomes challenging.
Antony Gareau‐Lajoie +4 more
wiley +1 more source
A quantile cure model with partially functional covariate effects. [PDF]
Chen CM, Peng Y.
europepmc +1 more source
Graphical representation of a data‐driven framework for Fischer‐Tropsch synthesis (FTS) modelling and optimization. Abstract This study presents a data‐driven approach for predicting the relationships between catalyst design, process conditions, and product selectivity in Fischer–Tropsch synthesis (FTS).
Doaa M. Hassan +2 more
wiley +1 more source
Feed-forward neural networks for genomic selection: insights into varying levels of dominance heritability. [PDF]
Sahebalam H, Gholizadeh M.
europepmc +1 more source
Abstract Global energy demand and environmental concerns have intensified the search for renewable and sustainable energy sources. This study thus, focuses on optimizing the transesterification process of waste cooking oil (WCO) using thermally activated basic oxygen furnace slag catalyst calcined at 850°C (BOF 850). The optimization and modelling were
Johra S. Ali, Hillary L. Rutto
wiley +1 more source
Simulation based new method for population variance using auxiliary information. [PDF]
Ahmadini AAH +5 more
europepmc +1 more source
Abstract This study investigates the effect of moisture on CO2 adsorption in South African coals using both experimental and machine learning approaches. Three coal samples (SL, TN, and EM) with varying ranks (RoVmr: 3.49%, 1.26%, and 0.64%, respectively) were collected from different regions of South Africa.
Kasturie Premlall +3 more
wiley +1 more source
Prediction and Optimization of Load-Bearing Capacity in Resistance Spot Welded Titanium Joints Using Neural Networks and Genetic Algorithms. [PDF]
Lacki P +4 more
europepmc +1 more source
This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin +2 more
wiley +1 more source

