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Abstract

The safety of mining operations in hard coal mines must be constantly developed and improved. There is ongoing multi-directional research focused at best recognition of the phenomenon associated with the properties of the coal-gas system and its connections with mining and geological conditions. This article presents the results of sorption experiments on coals from the Upper Silesian Coal Basin, which are characterized by varying degrees of coalification. One of the parameters that describes the kinetics of methane sorption, determining and providing valuable information about gas hazard and in particular the risk of gas and rock outbursts, is the effective diffusion coefficient De. It is derived from the solution of Fick’s second law using many simplifying assumptions. Among them is the assumption that the carbon matrix consists of only one type of pore – micropores. In fact, there are quite often at least two different mechanisms, which are connected to each other, related to the diffusion of methane from the microporous matrix and flows occurring in voids and macropores. This article presents both the unipore and bidisperse models and a set of comparisons which fit them to experimental curves for selected coals. For some samples the more complex bidisperse model gave much better results than the classic unipore one. The supremacy of the bidisperse model could be associated with the differences in the coal structure related to the coalification degree. Initial results justify further analyses on a wider set of coals using the methodology developed in this paper.

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Authors and Affiliations

Marcin Karbownik
Jerzy Krawczyk
ORCID: ORCID
Tomasz Schlieter
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Abstract

Acquiring labels in anomaly detection tasks is expensive and challenging. Therefore, as an effective way to improve efficiency, pretraining is widely used in anomaly detection models, which enriches the model's representation capabilities, thereby enhancing both performance and efficiency in anomaly detection. In most pretraining methods, the decoder is typically randomly initialized. Drawing inspiration from the diffusion model, this paper proposed to use denoising as a task to pretrain the decoder in anomaly detection, which is trained to reconstruct the original noise-free input. Denoising requires the model to learn the structure, patterns, and related features of the data, particularly when training samples are limited. This paper explored two approaches on anomaly detection: simultaneous denoising pretraining for encoder and decoder, denoising pretraining for only decoder. Experimental results demonstrate the effectiveness of this method on improving model’s performance. Particularly, when the number of samples is limited, the improvement is more pronounced.
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Authors and Affiliations

Xianlei Ge
1 2
Xiaoyan Li
3
Zhipeng Zhang
1

  1. School of Electronic Engineering, Huainan Normal University, China
  2. College of Computing and Information Technologies, National University, Philippines
  3. School of Computer, Huainan Normal University, China
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Abstract

In the paper critical role of including the right material parameters, as input values for computer modelling, is stressed. The presented model of diffusion, based on chemical potential gradient, in order to perform calculations, requires a parameter called mobility, which can be calculated using the diffusion coefficient. When analysing the diffusion problem, it is a common practice to assume the diffusion coefficient to be a constant within the range of temperature and chemical composition considered. By doing so the calculations are considerably simplified at the cost of the accuracy of the results. In order to make a reasoned decision, whether this simplification is desirable for particular systems and conditions, its impact on the accuracy of calculations needs to be assessed. The paper presents such evaluation by comparing results of modelling with a constant value of diffusion coefficient to results where the dependency of Di on temperature, chemical composition or both are added. The results show how a given deviation of diffusivity is correlated with the change in the final results. Simulations were performed in a single dimension for the FCC phase in Fe-C, Fe-Si and Fe-Mn systems. Different initial compositions and temperature profiles were used.
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Bibliography

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[2] Nishibata, T., Kohtake, T. & Kajihara, M. (2020). Kinetic analysis of uphill diffusion of carbon in austenite phase of low-carbon steels. Materials Transactions. 61(5), 909-918. DOI: 10.2320/matertrans.MT-M2019255.
[3] Wróbel, M., & Burbelko, A. (2022). A diffusion model of binary systems controlled by chemical potential gradient. Journal of Casting & Materials Engineering. 6(2), 39-44. DOI: 10.7494/jcme.2022.6.2.39.
[4] Porter, D.A., Easterling, K.E. & Sherif, M.Y. (2009). Phase transformations in metals and alloys. Boca Raton: CRC Press.
[5] Bhadeshia, H.K.D.H. (2021). Course MP6: Kinetics & Microstructure Modelling. University of Cambridge. Retrieved July 23 2021 from: https://www.phase-trans.msm.cam.ac.uk/teaching.html
[6] Bergethon, P.R. & Simons, E.R. (1990). Biophysical Chemistry: Molecules to Membranes. New York: Springer-Verlag. DOl: 10.1007/978-1-4612-3270-4
[7] Shewmon, P. (2016). Diffusion in Solids. Cham: Springer International Publishers
[8] Mehrer, H. (2007). Diffusion in Solids: Fundamentals, Methods, Materials, Diffusion-Controled Processes. Berlin – Heidelberg: Springer-Verlag
[9] Hillert, M. (2008). Phase Equilibria, Phase Diagrams and Phase Transformations. Cambridge: Cambridge University Press.
[10] Lukas, H.L., Fries, S.G. & Sundman, B. (2007). Computational Thermodynamics. Cambridge: Cambridge University Press.
[11] Brandes, E.A. & Brook, G.B. (Eds.) (1998). Smithells Metals Reference Book. 7th Edition. Oxford: Elsevier.
[12] Bergner, D., Khaddour, Y. & Lorx, S. (1989). Diffusion of Si in bcc- and fcc-Fe. Defect and Diffusion Forum. 66-69, 1407-1412. DOI: 10.4028/www.scientific.net/DDF.66-69.1407.
[13] Nohara, K. & Hirano, K. (1973). Self-diffusion and Interdiffusion in γ solid solutions of the iron-manganese system. Journal of the Japan Institute of Metals. 37(1), 51-61. https://doi.org/10.2320/jinstmet1952.37.1_51
[14] Gegner, J. (2006). Concentration- and temperature-dependent diffusion coefficient of carbon in FCC iron mathematically derived from literature data. In the 4th Int Conf Mathematical Modeling and Computer Simulation of Materials Technologies, Ariel, College of Judea and Samaria.
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Authors and Affiliations

M. Wróbel
1
ORCID: ORCID
A. Burbelko
1
ORCID: ORCID

  1. AGH University of Science and Technology, Faculty of Foundry Engineering, al. A. Mickiewicza 30, 30-059 Krakow, Poland

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