SearcharxivSearch

arXiv · 2301.04354

First 3D reconstruction of a blast furnace using muography

Abstract

The blast furnace (BF) is the fundamental tool used in the iron manufacture. Due to the difficulty of accessing direct measurements of the inner phenomena, we determined the density distribution of its internal volume in order to improve its productivity using muography. This is an imaging technique based on the differential absorption of a flux of incident particles, muons, by the target under study, similar to clinical X-ray imaging. Muons are elementary particles that have the property of passing through dense materials, up to hundreds of meters away. Their relative absorption and deviation allows the generation of density distribution images of an object by tracking the number of muons received by a detector, before and after passing through a structure. The incident direction of the detected muons is reconstructed by means of a detector composed of 3 scintillator panels that we moved on 3 positions around the BF. With this technique, we obtained the first 3D image of the internal structure of a BF using a Markov Chain Monte Carlo (MCMC) inverse problem solving algorithm on muon flux data. We were also able to perform a density monitoring of the BF and some of its operating parameters. We distinguished the position and shape of the cohesive zone, a key element in the productivity of a furnace, validating this innovative measurement concept in the application to a BF and opening the field to a series of future experiments to gain both spatial and temporal resolution.

Explore related subjects

Keep this discovery

BibTeXRIS

Amélie Cohu, Antoine Chevalier, Oleksandr Nechyporuk, Andreas Franzen, Jan Sauerwald, Jean-Christophe Ianigro, Jacques Marteau. 2023-01-11. First 3D reconstruction of a blast furnace using muography. https://doi.org/10.1088/1748-0221%2F18%2F07%2Fp07004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Holistic law of aftershocks

The paper is devoted to the phenomenological theory of aftershocks occurring in the source of a tectonic earthquake following the main shock. The theory was developed by the author jointly with A.D. Zavyalov and O.D. Zotov during the course of a long-term study of aftershocks. The theory is based on the concepts of source deactivation and the source's proper time. The holistic law governing the decay of aftershock activity over proper time follows from the theory. The damping decrement is equal to the source deactivation coefficient. The main focus of this paper is the analysis of the logical structure of the theory. The paper also contains a brief description of the experimental results obtained using the theory. Keywords: earthquake source, aftershocks, Omori's law, Utsu's law, deactivation coefficient, proper time, underground clock, foreshock convergence, aftershock divergence.

physics.geo-ph

Bayesian deep learning integration of geophysical and drilling data for 3D prediction of copper mineralization and drill targeting: a case study from the Kogodai prospect, Rudny Altai

Exploration drill targeting in structurally complex terranes is hindered by sparse sampling, heterogeneous datasets, and the ambiguity of geophysical inversions. Here, we present an uncertainty-aware 3D workflow for the acceleration of time-to-discovery in brownfield explorations and apply it to the Kogodai prospect in the Rudny Altai metallogenic province. We jointly analyse existing drilling and geophysical data in a comprehensive approach, revealing hidden patterns in already available data. Drillholes and trenches were desurveyed to a common 3D reference frame, and assays were composited to a consistent spatial support to facilitate joint modelling with geophysical inputs. We develop Bayesian deep-learning models to predict 3D fields of Cu grade together with chargeability and apparent resistivity while quantifying epistemic uncertainty via Monte Carlo sampling. The original contribution of this work is to treat the problem not as pointwise regression between co-located observations, but as joint learning of spatially continuous 3D fields from sparse, heterogeneous exploration evidence. The resulting 3D predictions delineate a principal mineralized trend and several localized candidate zones that coincide with elevated induced polarization (IP) responses, while uncertainty mapping highlights where predictions are robust versus where additional drilling would be most informative. The continuous Cu-grade field can also be thresholded to produce binary prospectivity maps, allowing the sensitivity of target delineation to the chosen cutoff to be evaluated. The outputs are intended for qualitative interpretation and risk-aware drill targeting rather than resource estimation, and we discuss key limitations arising from incomplete provenance metadata for geophysical products and heterogeneity of historical sampling.

physics.geo-ph

PyelogP: Automated Energy-Based Determination of Preconsolidation Pressure in Clay Deposits

Estimating the preconsolidation pressure ($\sigma'_p$) from one-dimensional consolidation (oedometer) tests is critical in geotechnical engineering for settlement analysis. Traditional graphical methods, such as the Casagrande procedure, may introduce uncertainties, particularly when interpreting rounded $e$-log($P$) curves typical of disturbed specimens of soft clays and silt deposits. This paper introduces PyelogP, an open-source Python library designed to calculate $\sigma'_p$ using the strain-energy method proposed by Becker et al. (1987) as an automated and reproducible alternative. The algorithm combines natural cubic spline interpolation, knee-point detection via the Kneedle algorithm, and split-point linear regression within the work-pressure space. Physically informed thresholds, including overconsolidation ratio limits and second-derivative maxima (${d^2 e}/{d(\log \sigma')^2}$), are incorporated to establish pre-yield and post-yield fitting boundaries. The performance of PyelogP is evaluated against a suite of 22 experimental consolidation datasets covering various clay deposits, including Saint-Alban clay and San Francisco Old Bay Clay. The results demonstrate strong agreement with the published values ($R^2$ = 0.912, RMSE = 0.374, MBE = -0.080), while the $O(N^2)$ algorithm requires only a few milliseconds per curve for typical oedometer datasets and less than 150 milliseconds for the largest datasets.

physics.geo-ph