Publications
2020
1.
Chaves, Deisy; Trujillo, María; García, Edward; Barraza, Juan; Lester, Edward; Barajas, Maribel; Rodriguez, Billy; Romero, Manuel; Fernández-Robles, Laura
Automated inspection of char morphologies in colombian coals using image analysis Artículo de revista
En: Intelligent Automation & Soft Computing, vol. 26, no 3, pp. 397–405, 2020.
Resumen | Enlaces | BibTeX | Etiquetas: automated classification, char morphology, coal combustion, machine learning
@article{chaves_automated_2020,
title = {Automated inspection of char morphologies in colombian coals using image analysis},
author = {Deisy Chaves and María Trujillo and Edward García and Juan Barraza and Edward Lester and Maribel Barajas and Billy Rodriguez and Manuel Romero and Laura Fernández-Robles},
url = {https://d1wqtxts1xzle7.cloudfront.net/84400929/pdf-libre.pdf?1650296199=&response-content-disposition=inline%3B+filename%3DAutomated_Inspection_of_Char_Morphologie.pdf&Expires=1739453001&Signature=WRmkYL0vVspGUAWK2T6VzJ7LlDmM3124W~OVgR50dihJQFxgHvpXDGOjN8HhZa3-1MVnKi0FAOlZOlYD3Uv49praFJTy-WokdxMEcJ6DLPl7hJosZQahVgkjY-mVWHZJ~tq6FxhHV471iEpDts1G8MhynylHeFPJRbpmDRxSsNujdBUhj6j9s1a97oZGsQV8gpI8fJegGdr3sysuw46eWo8vF5wlVv7sSz40QP53B0hzipH9k-JTds2WE59sWOu2NxhORsVBYTRfAeXE2XJHLG3F0W44aKpsVc3c3MI4nkDlWqFMbMarR4VIQxm5q1S9LD8qMzzx7F4MVGjYTF4YcA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Intelligent Automation & Soft Computing},
volume = {26},
number = {3},
pages = {397–405},
abstract = {This paper proposes machine learning algorithms to automate char morphology classification during coal combustion, improving industrial control efficiency. The approach outperforms the traditional ICCP method by evaluating various morphological features, including the unfused material feature. Results confirm the model’s accuracy in identifying and classifying char particles automatically.},
keywords = {automated classification, char morphology, coal combustion, machine learning},
pubstate = {published},
tppubtype = {article}
}
This paper proposes machine learning algorithms to automate char morphology classification during coal combustion, improving industrial control efficiency. The approach outperforms the traditional ICCP method by evaluating various morphological features, including the unfused material feature. Results confirm the model’s accuracy in identifying and classifying char particles automatically.
2018
2.
Chaves, Deisy; Fernández-Robles, Laura; Bernal, José; Alegre, Enrique; Trujillo, María
Automatic characterisation of chars from the combustion of pulverised coals using machine vision Artículo de revista
En: Powder technology, vol. 338, pp. 110–118, 2018, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: char morphology, coal combustion, machine vision, reactivity classification
@article{chaves_automatic_2018,
title = {Automatic characterisation of chars from the combustion of pulverised coals using machine vision},
author = {Deisy Chaves and Laura Fernández-Robles and José Bernal and Enrique Alegre and María Trujillo},
url = {https://www.sciencedirect.com/science/article/pii/S0032591018304753},
year = {2018},
date = {2018-01-01},
journal = {Powder technology},
volume = {338},
pages = {110–118},
abstract = {This paper presents a method for automatically characterizing char particles produced during pulverized coal combustion using machine vision. The study combines two approaches: morphological analysis and intensity distribution through texture features. By using bit-plane slicing, the method captures both fine and rough details of the char particles. The particles are then classified based on their reactivity (high, medium, or low). Tested on char images from Colombian coal regions, the method achieves precision comparable to manual analysis, offering a more efficient, automated solution for evaluating coal reactivity.},
note = {Publisher: Elsevier},
keywords = {char morphology, coal combustion, machine vision, reactivity classification},
pubstate = {published},
tppubtype = {article}
}
This paper presents a method for automatically characterizing char particles produced during pulverized coal combustion using machine vision. The study combines two approaches: morphological analysis and intensity distribution through texture features. By using bit-plane slicing, the method captures both fine and rough details of the char particles. The particles are then classified based on their reactivity (high, medium, or low). Tested on char images from Colombian coal regions, the method achieves precision comparable to manual analysis, offering a more efficient, automated solution for evaluating coal reactivity.