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Metals
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Applications of Intelligent Process Systems in Metallurgy
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Applications of Intelligent Process Systems in Metallurgy

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 15285

Special Issue Editor

Prof. Dr. Chris Aldrich

E-Mail Website
Guest Editor
Western Australian School of Mines, Minerals, Energy and Chemical Engineering, Curtin University, Perth, WA 6845, Australia
Interests: mineral processing; extractive metallurgy; physical metallurgy; corrosion; artificial intelligence; process automation; machine learning; complex systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapidly increasing digitization of society, artificial intelligence, and systems designed to learn from data, more specifically, play a key role in the ongoing automation of industries based on modern smart technology. This includes smart sensors, where recent advances in deep learning continue to redefine the state-of-the-art in computer vision. It also includes advances in diagnostic systems, where traditional frameworks can make use of enhanced models or tools, as well as learning from data-based models themselves. In addition, advanced control based on reinforcement learning is set to achieve further success in data-rich environments.

In this Special Issue of Metals, we aim to elucidate the impact of these developments on metallurgical industries, including extractive and process metallurgy. Therefore, papers dealing with all aspects of intelligent process systems and their applications in metals industries are welcome. Topics of interest include, but are not limited to, the following:

  • Smart sensors, soft sensors, inferential sensors, body sensors, and sensor networks;
  • Visualization of data and information processing, exploratory data analysis, and data mining;
  • Processing of nonlinear signals, such as electrochemical noise in corrosion systems, acoustic signals in grinding circuits, or hyperspectral imaging;
  • Intelligent decision support realized through fuzzy systems, expert systems, and case-based reasoning;
  • Diagnostic and predictive modelling and advanced process control based on machine learning.

Prof. Dr. Chris Aldrich
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process metallurgy
  • extractive metallurgy
  • artificial intelligence
  • machine learning
  • deep learning
  • nonlinear signal processing
  • expert systems
  • data visualization
  • intelligent decision support
  • evolutionary computation

Published Papers (5 papers)

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Research

14 pages, 3808 KiB  
Article
Assessing the Influence of Operational Variables on Process Performance in Metallurgical Plants by Use of Shapley Value Regression
by Xiu Liu and Chris Aldrich
Metals 2022, 12(11), 1777; https://doi.org/10.3390/met12111777 - 22 Oct 2022
Cited by 2 | Viewed by 1044
Abstract
Shapley value regression with machine learning models has recently emerged as an axiomatic approach to the development of diagnostic models. However, when large numbers of predictor variables have to be considered, these methods become infeasible, owing to the inhibitive computational cost. In this [...] Read more.
Shapley value regression with machine learning models has recently emerged as an axiomatic approach to the development of diagnostic models. However, when large numbers of predictor variables have to be considered, these methods become infeasible, owing to the inhibitive computational cost. In this paper, an approximate Shapley value approach with random forests is compared with a full Shapley model, as well as other methods used in variable importance analysis. Three case studies are considered, namely one based on simulated data, a model predicting throughput in a calcium carbide furnace as a function of operating variables, and a case study related to energy consumption in a steel plant. The approximately Shapley approach achieved results very similar to those achieved with the full Shapley approach but at a fraction of the computational cost. Moreover, although the variable importance measures considered in this study consistently identified the most influential predictors in the case studies, they yielded different results when fewer influential predictors were considered, and none of the variable importance measures performed better than the other measures across all three case studies. Full article
(This article belongs to the Special Issue Applications of Intelligent Process Systems in Metallurgy)
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13 pages, 4320 KiB  
Article
Use of Numerical Methods for the Design of Thermal Protection of an RFID-Based Contactless Identification System of Ladles
by Dalibor Jančar, Mario Machů, Marek Velička, Petr Tvardek and Jozef Vlček
Metals 2022, 12(7), 1163; https://doi.org/10.3390/met12071163 - 08 Jul 2022
Cited by 1 | Viewed by 1047
Abstract
A method of contactless identification is proposed for steel ladles to eliminate manual inputs that negatively affect the monitoring system of ladles. It is an RFID (Radio Frequency Identification) method based on the principle of radio data transmission between the sensor and a [...] Read more.
A method of contactless identification is proposed for steel ladles to eliminate manual inputs that negatively affect the monitoring system of ladles. It is an RFID (Radio Frequency Identification) method based on the principle of radio data transmission between the sensor and a moving object (in our case, a ladle), which is equipped with a so-called transponder (RFID tag). The RFID tag was part of the ladle; it was placed on its shell, reaching a temperature often exceeding 250 °C. The temperature limit for using an RFID transponder is 120 °C. For this reason, thermal insulation protection was made for the RFID transponder. Its design was preceded by simulations of temperature fields using numerical methods. The aim was to compare the resulting values obtained from numerical simulations with the actually measured temperatures and, on this basis, to subsequently perform a numerical simulation for conditions that are not operationally measurable. Full article
(This article belongs to the Special Issue Applications of Intelligent Process Systems in Metallurgy)
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22 pages, 11991 KiB  
Article
Deep Learning Approaches to Image Texture Analysis in Material Processing
by Xiu Liu and Chris Aldrich
Metals 2022, 12(2), 355; https://doi.org/10.3390/met12020355 - 18 Feb 2022
Cited by 10 | Viewed by 5480
Abstract
Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning [...] Read more.
Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning with deep neural networks have become established as highly competitive approaches to classical texture analysis. In this study, three traditional approaches, based on the use of grey level co-occurrence matrices, local binary patterns and textons are compared with five transfer learning approaches, based on the use of AlexNet, VGG19, ResNet50, GoogLeNet and MobileNetV2. This is done based on two simulated and one real-world case study. In the simulated case studies, material microstructures were simulated with Voronoi graphic representations and in the real-world case study, the appearance of ultrahigh carbon steel is cast as a textural pattern recognition pattern. The ability of random forest models, as well as the convolutional neural networks themselves, to discriminate between different textures with the image features as input was used as the basis for comparison. The texton algorithm performed better than the LBP and GLCM algorithms and similar to the deep learning approaches when these were used directly, without any retraining. Partial or full retraining of the convolutional neural networks yielded considerably better results, with GoogLeNet and MobileNetV2 yielding the best results. Full article
(This article belongs to the Special Issue Applications of Intelligent Process Systems in Metallurgy)
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19 pages, 4888 KiB  
Article
Data Driven Performance Prediction in Steel Making
by Fernando Boto, Maialen Murua, Teresa Gutierrez, Sara Casado, Ana Carrillo and Asier Arteaga
Metals 2022, 12(2), 172; https://doi.org/10.3390/met12020172 - 18 Jan 2022
Cited by 6 | Viewed by 2918
Abstract
This work presents three data-driven models based on process data, to estimate different indicators related to process performance in a steel production process. The generated models allow the optimization of the process parameters to achieve optimal performance and quality levels. A new approach [...] Read more.
This work presents three data-driven models based on process data, to estimate different indicators related to process performance in a steel production process. The generated models allow the optimization of the process parameters to achieve optimal performance and quality levels. A new approach based on ensembles has been developed with feature selection methods and four state-of-the-art regression approximations (random forest, gradient boosting, xgboost and neural networks). The results show that the proposed approach makes the prediction more stable reducing the variance for all cases, even in one case, slightly reducing the bias. Furthermore, from the four machine learning paradigms presented, random forest is the one with the best results in a quantitative way, obtaining a coefficient of determination of 0.98 as a maximum, depending on the target sub-process. Full article
(This article belongs to the Special Issue Applications of Intelligent Process Systems in Metallurgy)
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16 pages, 18589 KiB  
Article
Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network
by Leilei Zou, Jiangshan Zhang, Yanshen Han, Fanzheng Zeng, Quanhui Li and Qing Liu
Metals 2021, 11(12), 1976; https://doi.org/10.3390/met11121976 - 08 Dec 2021
Cited by 5 | Viewed by 3424
Abstract
The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction [...] Read more.
The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability. Full article
(This article belongs to the Special Issue Applications of Intelligent Process Systems in Metallurgy)
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