Use of an Artificial Neural Network in determination of iron ore pellet bed permeability

Chagas, Marcelo A.; Machado, Marcelo Lucas Pereira; Souza, Joao B. Conti de; Frigini, Eduardo F. de Jesus

doi:10.1590/0370-44672016700032

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Important results were also obtained using an approach based on the use of artificial neural networks to study the strength of pellets (Chagas et al, 2015) and the porosity of the layer they are laid on a roasting machine (Dwarapudi et al, 2007).…”

Section: Motivationmentioning

confidence: 99%

Estimating Porosity of Agglomerated Products Using Optimized Sphere Packing

Marmolejo-Saucedo,

Litvinchev,

Bizhanov

et al. 2024

International Journal of Manufacturing, Materials, and Mechanical Engineering

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The article presents the results of studying the dynamics of changes in the porosity of two-component briquettes and pellets depending on the size of particles and the proportions of components in the mixture using an optimized packing of spheres. Knowing the patterns of change in the porosity allows to optimize the strength of the briquette and pellets as well as to improve their behavior in the reduction processes in blast furnaces, steel making furnaces and in direct reduction reactors. A computation experiment based on heuristic simulation model was designed to study the change of the estimated porosity under increasing/substituting the number of larger spherical particles in the mixture of spheres. The results obtained made it possible for the first time to reveal the extreme nature of the change in the porosity of the briquette/pellet with the addition of larger particles, depending on the fractional composition of the briquette. The results obtained open up new opportunities for optimizing the placement of fine-grained materials in the charge of metallurgical furnaces.

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Section: Motivationmentioning

confidence: 99%

Estimating Porosity of Agglomerated Products Using Optimized Sphere Packing

Marmolejo-Saucedo,

Litvinchev,

Bizhanov

et al. 2024

International Journal of Manufacturing, Materials, and Mechanical Engineering

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“…Numerous studies have harnessed the power of Artificial Neural Networks (ANN) in diverse applications within the field of materials science and metallurgy. Chagas et al [ 25 ] applied ANN to assess the sensitivity of variables in pellet bed formation, aiding in the generation of green pellets with reduced fuel and energy consumption and improved final quality. Dwarapudi et al [ 26 ] developed an ANN model to predict the cold compressive strength (CCS) of pellets in a straight grate furnace.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Biomass–Humic Acid Limonite Pellets Using Artificial Neural Network Model

et al. 2023

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Due to the detrimental impact of steel industry emissions on the environment, countries worldwide prioritize green development. Replacing sintered iron ore with pellets holds promise for emission reduction and environmental protection. As high-grade iron ore resources decline, research on limonite pellet technology becomes crucial. However, pellets undergo rigorous mechanical actions during production and use. This study prepared a series of limonite pellet samples with varying ratios and measured their compressive strength. The influence of humic acid on the compressive strength of green and indurated pellets was explored. The results indicate that humic acid enhances the strength of green pellets but reduces that of indurated limonite pellets, which exhibit lower compressive strength compared to bentonite-based pellets. Furthermore, artificial neural networks (ANN) predicted the compressive strength of humic acid and bentonite-based pellets, establishing the relationship between input variables (binder content, pellet diameter, and weight) and output response (compressive strength). Integrating pellet technology and machine learning drives limonite pellet advancement, contributing to emission reduction and environmental preservation.

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“…The flexible manufacturing system on which this work is developed is the FMS-200, this cell is divided by stations and is responsible for the process of a bearing assembly; based on this, the present paper establishes a proposal of optimization for the FMS-210 quality control station by using artificial vision, through modification, relocation and elimination of some operational elements, after previous studies and time analysis for each one of its components [10]. In addition to this operational analysis, it is intended to implement a pattern recognition algorithm supported on adaptive resonance neural networks (ART2), which would optimize the inspection process and quality control based on the validation of specific patterns associated with an optimal finished product [11,12], thus substantially improving the operating times of the inspection and validation processes of a final product [13].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the quality control for pieces recognition in manufacturing flexible cells by the implementation of neural networks

Ariza¹,

Parra²,

Torrejano³

2017

ces

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The high levels of automation in the companies that use manufacturing cells for their fabrication processes guarantee a high efficiency in production with high quality standards. The main objective of this paper is to develop an optimization for the tasks performed by the quality control station through artificial vision belonging to the manufacturing cell FMS-200; the development of the proposal focuses on optimizing the inspection process and control of quality by adapting image processing techniques and adaptive resonance neural networks. For this particular case, a review of the location of the optical system and a detailed time study for each movement of the station actuators was carried out, attempting to determinate a better position for the optical detection system (artificial vision camera) and the possibility elimination of some components of the station; all of this in order to considerably reduce the operating time and increasing the efficiency for the quality control of the system.

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Use of an Artificial Neural Network in determination of iron ore pellet bed permeability

Cited by 3 publications

References 14 publications

Estimating Porosity of Agglomerated Products Using Optimized Sphere Packing

Estimating Porosity of Agglomerated Products Using Optimized Sphere Packing

Prediction of Compressive Strength of Biomass–Humic Acid Limonite Pellets Using Artificial Neural Network Model

Optimization of the quality control for pieces recognition in manufacturing flexible cells by the implementation of neural networks

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