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Stochastic Programming for Job Sequencing and Tool Switching Problem with Non-Identical Parallel Machines and Stochastic Processing Times
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Hierarchical Porosity Engineering of Birch‐Derived Carbons via KOH Activation for High‐Performance Aluminum Batteries
No full textInternational audienceAluminum batteries (ABs) present a cost‐effective, high‐energy alternative to lithium‐ion systems, owing to aluminum's abundance and high theoretical capacity. Here, it reports the synthesis of birch wood derived carbons (CBWs) via carbonization of sawdust followed by KOH activation and their evaluation as AB cathodes. Two samples CBW14 and CBW16 are prepared using biochar‐to‐KOH weight ratios of 1:4 and 1:6, respectively. Both materials are highly disordered, predominantly amorphous carbons, exhibiting Brunauer–Emmett–Teller‐specific surface areas of 3015 m 2 g −1 (CBW14) and 3306 m 2 g −1 (CBW16). When cycled between 0.01 and 2.2 V at 0.1 A g −1 , CBW14 and CBW16 delivered discharge capacities of 120 and 140 mAh g −1 , respectively. Notably, CBW16 sustained 35 mAh g −1 at a high rate of 10 A g −1 and achieved energy densities of 155 Wh kg −1 at 0.1 A g −1 and 95 Wh kg −1 at 1.0 A g −1 . These findings underscore the critical influence of KOH activation parameters on pore architecture and electrochemical performance, pointing the way toward scalable fabrication of efficient carbon cathodes for next‐generation aluminum batteries
NOx decomposition through seaweed biocarbon as biosourced catalyst: Experimental and density functional theory approaches
No full textInternational audienceUnderstanding the NOx abatement and deactivation mechanisms is essential to improve the performance and regeneration of bio-based catalysts. In this study, biocarbon obtained from marine algae (SW), rich in nitrogen and minerals, was used as a biosourced catalyst for the decomposition of a flux of 1021 ppm of NO in argon (deNOx). Several parameters were studied, including pyrolysis temperature (650 and 800 °C) and deNOx operating temperature (200, 350, and 500 °C). Additionally, a theoretical study using density functional theory (DFT) calculations investigated the role of nitrogen and catalytic (Fe) or promoter (K) elements and functional groups (O-groups) in NO adsorption. The results indicated that the bio-based catalyst produced at 800 °C (SW800) achieved the highest conversion rate of 22.5 ± 1% at 350 °C compared to the catalyst produced at 650 °C (SW650), which achieved a conversion rate of 15.6 ± 1%. This result is attributed to the higher mineral content (52.99 wt%, db) of the SW800 catalyst rather than its specific surface area, which was not identified as a discriminating parameter under the studied conditions. Regarding the reaction mechanism, nitrogen and oxygen atomic balances confirm the multistep decomposition hypothesis. Furthermore, the slight increase in lactone and carbonyl groups after the deNOx process suggests that oxygen remains on the biocarbon. This contributes to reduce active sites and to deactivate the biocarbon catalyst, as observed. Coke deposition on active sites and metal sintering can also contribute to deactivation. Based on the calculations on representative nitrogen-rich biocarbon structures, the adsorption energies (Eads) on pyridinic N (−71.10 kJ mol−1) and pyrrolic N (−69.30 kJ mol−1) sites are high, demonstrating their roles in NO abatement. However, the presence of an oxygen group close to a nitrogen site inhibits NO adsorption due to its high electronegativity. This information is in agreement with the deactivation of nitrogen sites by residual oxygen from NO decomposition, as observed experimentally
Non-destructive assessment of multi-material micro-tissue mechanics reveals the critical role of rigidity gradients in tumour growth and pressure
No full textInternational audienceProbing stiffness anisotropies in three-dimensional materials non-destructively is a major challenge in disciplines as diverse as aeronautics and medicine. While the former typically relies on various mechanical tests—such as tensile, compression, bending, and shear—performed on sample parts, the latter often employs acoustic techniques or wave propagation through matter. The choice of techniques depends on the size of the sample of interest and the desired resolution. In our case, to probe the mechanical properties of sub-millimetre micro-tissues, it is necessary to use methods with high resolution and as furtive as possible. We present a method based, 1/ on imaging the displacement of microbeads within a hydrogel resulting from the growth of a three-dimensional micro-tissue and, 2/ on finite element modelling of the deformations underlying bead displacements. This approach allows us to determine the elastic properties of the hydrogel and, in particular, to show that beyond a certain thickness, incomplete cross-linking of the hydrogel results in a stiffness gradient. We show that when the micro-tissue contacts with an immediately rigid alginate wall, the pressure exerted over time increases very rapidly, whereas when the micro-tissue encounters a substrate with a stiffness gradient, the pressure increase is more gradual. Uncovering this could provide a better understanding of the role of tumour microenvironment stiffness in metastatic escape processes. Statement of significance Understanding factors modulating tumours growth is crucial for developing better cancer treatments. This study introduces a non-destructive method to assess the stiffness of subcomponents of a tissue avatar, a question unwieldly to tackle. The authors show that small changes in stiffness of the tumour-mimic surrounding tissue can strongly affects how the tumour cell aggregate grows. This is an outreach in cancer biology because it connects the mechanical environment of tissues to cancer behaviour in a original way. It provides a powerful tool for studying 3D biological systems and could help design more suited materials for biomedical research and therapy. This work is relevant for the fields of cancer biology, biomaterials, and tissue engineering
Comportement et caractérisation de la formation de la croûte dans l'autoclave lors de la lixiviation du minerai de latérite de nickel dans des conditions HPAL
No full textInternational audienceScale formation on reactor walls remains a major operational challenge in high-pressure acid leaching (HPAL) of nickel laterites, leading to reduced heat transfer efficiency, increased maintenance, and process downtime. This study investigates the influence of slurry solid content and acid-to-ore (A/O) ratio on autoclave scale formation during laterite leaching. Experiments performed under typical HPAL conditions (265 °C and ∼50 bar) with laterite ore examined how these parameters affect metal extraction, scale quantity, and mineralogical composition. Scale deposits were quantified and analysed to determine their composition and to evaluate the precipitation tendency of potential scale-forming minerals through solution speciation and supersaturation behaviour during HPAL leaching. The results show that increasing slurry solids significantly promotes scale formation, producing denser and more strongly adherent deposits, while higher acid dosage further enhances precipitation of sulphate-bearing phases. Mineralogical analyses indicate that the scales are primarily composed of hematite, hydronium alunite, and magnesium sulphates, whose formation is driven by solution supersaturation during leaching. High solids content also promotes incorporation of valuable metals into the scale matrix, leading to reduced nickel and cobalt recovery. In contrast, operation at moderate solids content and near-stoichiometric acid addition limits scale accumulation while maintaining high metal extraction. These findings highlight the coupling between HPAL operating conditions, solution chemistry, and fouling behaviour, and suggest an operational window for reducing scale formation without the use of chemical additives that may interfere with downstream processing
Vapor–Liquid Equilibrium of the Hydrogen Sulfide (H2S) – Benzene (C6H6) Binary System: Experimental and Modeling Study
No full textInternational audienceThe phase behavior of the hydrogen sulfide (H₂S) - benzene (C₆H₆) binary system is critical for optimizing gas sweetening, aromatic solvent recovery, and high-pressure reservoir in the petroleum industry, while ensuring environmental compliance. This study presents new isothermal vapor-liquid equilibrium (VLE) measurements for the H₂S - C₆H₆ system at 278.21 K, 298.36 K, 323.38 K, and 343.39 K, covering pressures up to 4.5 MPa. The experimental data were obtained using a static-analytic method with two magnetic capillary samplers (ROLSI®), enabling precise sampling and analysis of both liquid and vapor phases via gas chromatography. The measurements have uncertainties of u(T, k=2)= 0.02 K for temperature, u(P, k=2)= 0.0008 MPa for pressure, and u(z) = 0.006 for molar compositions. The VLE data were modeled using the Peng–Robinson equation of state with classical van der Waals mixing rules and an alternative approach combining modified Huron–Vidal mixing rules with the NRTL model for the liquid phase. In addition, the predictive PPR78 and PSRK models were evaluated against the experimental dataset. With optimized binary interaction parameters, all models reproduced the measured data with acceptable deviations, effectively capturing the strongly non-ideal behavior of the H₂S–C₆H₆ system. These results extend the experimental database for H₂S–C₆H₆ mixtures, validate robust EOS-based and predictive modeling frameworks, and provide a reliable foundation for industrial process design, simulation, and optimization
Integrated Flexible Job Shop Scheduling with Autonomous Electric Tractors Routing and Battery Management
No full textInternational audienceIntegrated Flexible Job Shop Scheduling with Autonomous Electric Tractors Routing and Battery Managemen
Data augmentation for fuselage panel inspection via 3D point cloud segmentation
No full textInternational audienceAutomated inspection of mechanical assemblies can be significantly improved through the application of deep learning techniques to three-dimensional (3D) point cloud segmentation. In industrial environments, however, the use of such models is often constrained by the limited availability of annotated datasets, caused by the high cost, time demands, and confidentiality of data acquisition. To address these limitations, a fully instance and semantically annotated dataset of airplane fuselage panels collected in laboratory conditions is presented, together with a deep learning approach for detecting and classifying rivets in 3D scans. The proposed method enables the detection of defects such as missing or damaged rivets. To overcome limited training data, an augmentation strategy was deployed, designed to improve the detection of damaged and missing rivets. The influence of different scanning devices and resolutions on inspection accuracy is also evaluated across multiple deep learning models. This whole process was intended to be used as a preparation step in the reverse engineering pipeline
Understanding strain localization in metallic materials: a review of high-resolution digital image correlation and related techniques: Review
No full textInternational audiencePlastic deformation in metallic materials is generally governed by highly localized and intrinsically heterogeneous deformation processes, including crystallographic slip banding, deformation twinning, phase transformation and grain-boundary sliding. These mechanisms operate at the sub-grain scale where they are competing, interacting, and are sometimes incompatible for short-range transmission due to deformation confinement within individual grains. The heterogeneous nature of irreversible deformation at the microstructure scale also applies at the mesoscale, i.e. the scale of the crystalline aggregate. Capturing experimentally the discrete and heterogeneous deformation processes at the microstructure scale is essential to understand elementary deformation processes involved for specific loading conditions, quantifying their intensity to finally achieve a better dialogue with numerical models of crystal plasticity for the prediction of mechanical behavior and the lifetime of parts. High-resolution digital image correlation (HR-DIC), implemented on scanning electron microscopy images, has emerged as a key technique to quantify these phenomena by providing full-field measurements of in-plane displacement and strain at sub-micron spatial resolution over statistically representative fields of view. This review outlines the experimental foundations, data-processing strategies, and correlative analysis frameworks that underpin the use of HR-DIC for studying strain localization in metals
R-IO SUITE: integration of LLM-based AI into a knowledge management and model-driven based platform dedicated to crisis management
No full textInternational audienceThis article presents how the R-IO SUITE software platform, a decision support system for crisis management entirely based on model-driven engineering principles, considerably benefits from large language model (LLM)-based artificial intelligence (AI). The different components of the R-IO SUITE platform are used to climb the four abstraction layers: data, information, decision and action through interpretation (from data to information), exploitation (from information to decision) and implementation (from decision to action). These transitions between layers are supported by a knowledge base embedding knowledge instances structured according to a crisis management metamodel. From a functional perspective, this platform is fully operational, however, to be able to cover any type of crisis situation, the knowledge base should be enriched, first, from a "resource perspective" (to embed the various available means to deal with any faced situation), and second, from an "issue perspective" (to understand all risks and damage that can appear on a crisis situation). It is not reasonable to consider creating and maintaining such an exhaustive knowledge base. However, the connection of the R-IO SUITE platform with LLM software such as ChatGPT (c) makes it possible, by generating appropriate prompts, to update on-the-fly the knowledge base according to the faced context. This article shows how the LLM AI can provide complementary knowledge to formally fulfil the knowledge base to make it relevant to the faced crisis situation. This article presents the R-IO SUITE as a LLM-empowered model-driven platform to become an extended crisis management supporting system