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



Título del documento: Simultaneous influence of gas mixture composition and process temperature on Fe2O3®FeO reduction kinetics - Neural network modeling
Revista: Brazilian journal of chemical engineering
Base de datos: PERIÓDICA
Número de sistema: 000308991
ISSN: 0104-6632
Autores: 1
2



Instituciones: 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
Año:
Periodo: Sep
Volumen: 22
Número: 3
Paginación: 419-432
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés 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
Disciplinas: Química
Palabras clave: 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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