Simultaneous influence of gas mixture composition and process temperature on Fe2O3®FeO reduction kinetics - Neural network modeling



Document title: Simultaneous influence of gas mixture composition and process temperature on Fe2O3®FeO reduction kinetics - Neural network modeling
Journal: Brazilian journal of chemical engineering
Database: PERIÓDICA
System number: 000308991
ISSN: 0104-6632
Authors: 1
2



Institutions: 1Southern Illinois University, Coal Research Center, Carbondale, Illinois. Estados Unidos de América
2Southern Illinois University, Department of Mechanical Engineering and Energy Processes, Carbondale, Illinois. Estados Unidos de América
Year:
Season: Sep
Volumen: 22
Number: 3
Pages: 419-432
Country: Brasil
Language: Inglés
Document type: Artículo
Approach: Experimental, aplicado
English abstract The kinetics of Fe2O3-FeO reaction was investigated. The thermogravimetric (TGA) data covered the reduction of hematite both by pure species (nitrogen diluted CO or H2) and by their mixture. The conventional analysis has indicated that initially the reduction of hematite is a complex, surface controlled process, however once a thin layer of lower oxidation state iron oxides (magnetite, wüstite) is formed on the surface, it changes to diffusion control. Artificial Neural Network (ANN) has proved to be a convenient tool for modeling of this complex, heterogeneous reaction runs within the both (kinetic and diffusion) regions, correctly considering influence of temperature and gas composition effects and their complex interactions. ANN's model shows the capability to mimic some extreme (minimum) of the reaction rate within the determined temperature window, while the Arrhenius dependency is of limited use
Disciplines: Química
Keyword: Ingeniería química,
Oxido de fierro,
Reducción química,
Cinética de reacción,
Procesos isotérmicos,
Perceptrón multicapa,
Reacciones topoquímicas,
Redes neuronales artificiales
Keyword: Chemistry,
Chemical engineering,
Iron oxide,
Chemical reduction,
Reaction kinetics,
Isothermal processes,
Multilayer perceptron,
Topochemical reactions,
Artificial neural networks
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