Continuous casting of aluminum (Al) deoxidized steels demands careful inspection due to the occurrence of submerged entry nozzle (SEN) clogging, leading to unexpected production stops. Recognizing the castability of aspecific “cast” by monitoring the condition of the SEN is essential for uninterrupted casting. With this information prior to casting, operators can take preventive action against possible clogging occurrences, thus reducing unplanned downtimes. In response to the severe implications of SEN clogging, this work introduces a novel way to forecast castability of Al-killed steels. A hybrid model is proposed that integrates the adaptive neuro-fuzzy inference system (ANFIS) and long short-term memory (LSTM) networks. The output of the model helps to anticipate the event of clogging by analyzing both the past condition of the SEN and changes in the steel chemistry during the transport of the steel ladle from refining to the casting process. A comprehensive analysis of 150 casts helped to build the ANFIS algorithm for estimating the castability index (CI) parameter from steel chemistry. LSTM algorithm is used as asubsequent step to forecast castability in the next 20–25 min. Discrepancies between the predictive response and the actual conditions are reported. Although the real-time implementation of the proposed model is the ultimate goal, the focus of this work was to present the methodology and demonstrate its potential.

Macroscopic interactions of liquid iron and solid oxides, such as alumina, calcia, magnesia, silica, and zirconia, manifest the behavior and efficiency of high-temperature metallurgical processes. The oxides serve dual roles, both as components of refractory materials in submerged entry nozzles and also as significant constituents of nonmetallic inclusions in the melt. It is therefore crucial to understand the physicochemical interplay between the liquid and the oxides in order to address the nozzle clogging challenges and thereby optimize cast iron and steel production. This paper presents a methodology for describing these interactions by combining the materials' dielectric responses, computed within the density functional theory, with the Casimir-Lifshitz dispersion forces to generate Hamaker constants. The approach provides a comprehensive understanding of the wettability of iron against these refractory oxides, revealing the complex relation between the molecular and macroscopic properties. Our theoretically determined crystalline structures are confirmed by room-temperature X-ray diffraction, and the contact angles of liquid iron on the oxides are validated with a sessile drop system at a temperature of 1823 K. For comparison, we also present the wettability of the oxides by a liquid tin-bismuth alloy. The findings are essential in advancing the fundamental understanding of interfacial interactions in metallurgical science and pivotal in driving the development of more efficient and reliable steelmaking processes.

In this paper, we investigate the Casimir-Lifshitz free energy mechanism that governs both ice growth and melting near metal surfaces, with a particular focus on the role of oxidation. Our study reveals that metals such as gold, iron, and aluminum induce incomplete premelting, resulting in micron-sized liquid water layers when in contact with ice. These layers could have significant implications for the defrosting of metallic surfaces. When exposed to water vapor at the triple point, aluminum and other metals can induce the formation of notably thick layers of either liquid water or ice, which can theoretically become infinitely thick if other interactions are disregarded. However, when aluminum undergoes oxidation to form alumina, its behavior changes dramatically. Alumina surfaces cause complete melting when in direct contact with bulk ice and result in only micron-sized layers of water or ice in vapor conditions. In contrast, magnetite, the oxidized form of iron, retains metalliclike behavior due to its high dielectric constant, similar to other metals, and continues to support thick layers of water or ice. This distinction highlights the significant influence of oxidation on the dynamics of ice growth and melting near different metal surfaces.

The operational efficiency of continuous casting in steel production is often hindered by the clogging of submerged entry nozzles (SEN), caused due to the agglomeration of nonmetallic inclusions (NMIs). SEN clogging is challenging to monitor and requires probabilistic models for accurate real-time prediction. In this context, data-driven models emerged as a promising tool to be used in the existing industrial settings. Despite frequent occurrence of SEN clogging, collecting large datasets under varied operational conditions remains challenging. The scarcity of data hampers the ability to develop and train traditional data-driven models effectively. To overcome these challenges, physics-informed data-driven models are proposed in this work. The integration of outputs generated from theoretical calculations is sufficient to compensate for the lack of available datasets. To further enhance accuracy, an advanced methodology involving use of ab initio repository is developed. This repository contains material-specific data including high-temperature nonretarded Hamaker constants of NMIs in specific particle size range of 1–10 μm. A novel parameter, “Clogging Factor” is proposed to monitor and integrated into the modeling architecture to track the reduction in the available volume inside SEN due to the accumulation of NMIs. The proposed model has yet to be validated online but has shown potential in reducing SEN clogging.

In this work, innovative data-driven process models were designed, developed, and examined online for their application in the steel industry. The objective was to help operators in decision making during calcium additions in liquid steel and casting of steel grades which are susceptible to submerged nozzle (SEN) clogging. The developed data-driven process models were examined in a real industrial environment to demonstrate the use of supervised machine learning (ML) and deep learning (DL) algorithms for online predictions of specific process parameters. The adaptation of two novel data-driven decision support systems in production helped steelmakers to address the critical challenge of minimizing costly production stops caused by SEN clogging. Calcium (Ca) additions during secondary steelmaking is a well-adopted practice to transform solid oxide non-metallic inclusions (NMIs) into globular shaped liquid oxides. This practice of Ca treatment helps to avoid SEN clogging. Hence, operators in steel plants follow standard operating procedures (SOP) that detail the use of static quantity of Ca wire additions. However, these SOPs, providing a baseline for production, do not account for the dynamic nature of steelmaking processes and the varying chemistry of NMIs for each 'heat' produced. To address this gap, the impact of varying CaSi wire additions, beyond the SOP's guidelines, on the transformation and behavior of NMIs in low-alloyed steel grades was explored by developing the 'ClogCalc' decision support system. The aim was to establish a more reliable and responsive approach to Ca treatment, potentially leading to more effective control in preventing SEN clogging. The implementation of 'ClogCalc' has demonstrated a significant 30% reduction in SENclogging, as evidenced by industrial trials at Voestalpine steel plant.

Recognizing 'castability' of steel by monitoring the conditions of SEN clogging is essential for uninterrupted continuous casting. With this information prior to casting, operators can take preventive action against possible SEN clogging occurrences, thus reducing unplanned downtimes. In response to the severe implications of SEN clogging, a novel approach to forecast 'castability' of steels was introduced by developing the 'Checkcast' decision support system. The adaptive neuro fuzzy inference system (ANFIS) and long short-term memory (LSTM) network model was used as a base algorithm for estimating 'castability' of steel grade. The output of 'Checkcast' helps to anticipate the event of clogging by analyzing both the past condition of the SENs and changes in the steel chemistry during the transport of the steel from refining ladle to the tundish. The verification was conducted at the Sidenor steel plant. While the primary focus of this study was to develop data-driven process models, efforts were also made to study the foundational principles governing the SEN clogging and evolution of NMIs in studied steel grade. Laboratory experiments were also conducted on liquid steel samples obtained from steel plants. Parametric liquid windows were derived using systematic thermodynamic assessments using FactSage software. In addition, to understand the wetting characteristics at interfaces of NMIs and the SEN refractory, interfacial properties were calculated using ab-initio calculations.

I den här studien utvecklades och undersöktes innovativa datadrivna processmodeller för en integreradanvändning inom stålindustrin. Målet var att underlätta för operatörer i stålverken att bestämmakalciumtillsatser i flytande stål, samt att ge indikationer om gjutbarheten hos stålsorter som är känsligaför ingensättning i de gjutrör (SEN) som leder stålet till kokillen. De utvecklade processmodellernaimplementerades i en verklig industriell miljö utan att förändra den befintliga IT-infrastrukturen istålverket i syfte att visa användningsområden för övervakad maskininlärning (ML) och djupinlärning(DL) av integrerad bevakning och förutsägelser. Anpassningen av dessa datadrivna modeller iproduktionen hjälpte ståltillverkarna att hantera det kritiska problemet med att minimera kostsammaproduktionsstopp orsakade av SEN-igensättning. Kalciumtillsättning under sekundär ståltillverkningär en väl vedertaget metod för att omvandla icke-metalliska inklusioner (NMIs) i fasta oxider tillglobulär-formade flytande oxider. Detta förfaringssätt hjälper till att undvika att SEN-röret täpps igen.Därför följer operatörerna i stålverket de standard operative procedurer (SOP) som föreskriveranvändningen av bestämd mängd av Ca-tillsatser för specifika stålsorter. Dessa SOP utgör en baslinjeför produktionen, men de tar inte hänsyn till ståltillverkningsprocessens dynamiska karaktär eller denvarierande kemin hos NMIs för varje ‘värme’ som produceras. För att ta itu med denna kunskapsbristutforskades i denna studie effekten av att variera Ca-tillsatser, utöver de standardiserade SOPriktlinjerna,på omvandlingen och beteendet hos NMIs i låglegerade stålsorter genom att utveckla dendatadrivna processmodellen ‘ClogCalc’. Syftet var att etablera en mer tillförlitlig och responsiv metodför kalciumbehandling, vilket potentiellt leder till mer effektiv kontroll för att förhindra SENigensättning.Resultaten efter implementeringen av ‘ClogCalc’-modellen har betydande konsekvenseri att reducera SEN-igensättning med upp till 30%.

Att känna igen gjutbarheten hos stål genom att övervaka tillståndet hos SEN är också väsentligt för enoavbruten gjutning. Med denna information före gjutning kan operatörer vidta förebyggande åtgärdermot möjliga igensättningshändelser, vilket minskar oplanerade driftstopp. Som svar på de allvarligakonsekvenserna av SEN-igensättning introducerades ett nytt tillvägagångssätt för att förutsegjutbarheten hos stål genom att utveckla modellen ‘Checkcast’. ANFIS (från engelska ‘AdaptiveNeuro-Fuzzy Inference System’) och nätverk med LSTM (‘Long Short-Term Memory’) användes sombasalgoritmer för denna modell. Utdata från denna processmodell hjälpte till att förutse händelsen avigensättning genom att analysera både det tidigare tillståndet hos SEN och förändringarna i stålkeminunder transporten av stålskänken från raffinering till gjutningsprocessen. Medan den primära fokusen idenna studie var att utveckla datadrivna processmodeller, gjordes även ansträngningar för att studerade grundläggande principerna som styr SEN-igensättning och förloppet av icke-metalliskakontaminationer (NMIs) i de olika stålsorterna. Laboratorieexperiment utfördes på flytande stålproverfrån stålverk. Det parametriska vätskefönstret härleddes genom systematiska termodynamiska analysermed hjälp av FactSage-programvaran. För att förstå agglomerations- och vätningsegenskaperna vidgränssnitten mellan NMI och stålsmältan, beräknades materialens gränsytegenskaper med hjälp av en ab-initio-metod.

A decision support system was developed using supervised machine learning (ML) approach for optimization of calcium (Ca) additions by continuously monitoring the physical state of non-metallic inclusions (NMIs) inside low-alloyed liquid steels. In this work, two instances were considered to design the base algorithm for the proposed supervisory system: (1) Clogging of submerged entry nozzle (SEN) during continuous casting of steels due to accumulation of solid oxide non-metallic inclusions (NMIs) and ( 2) Ca treatment during secondary steelmaking for modification of oxide NMIs from solid to liquid state to avoid SEN clogging. At first, experimental investigations were carried out on liquid steel samples from three low-alloyed Ca-treated steel grades from the same steel family to evaluate the characteristics of solid oxide NMIs that cause SEN clogging. In the next step, data-driven models were developed using an in-house ML algorithm trained primarily with process data for calculating the value of the newly proposed dummy parameter 'Clog.' These models, after testing, were architected to develop a supervisory system based on experimental investigations and data-driven models. The objective of this proposed supervisory system was to predict the optimum quantity of Ca needed for successful modification of NMIs from solid to liquid state to avoid SEN clogging based on the forecasted 'Clog' value. Finally, industrial data from ~ 3000 heats were tested to verify the results obtained from the developed supervisory system. The results confirmed that this novel supervisory system could predict the optimum class of Ca for all studied steel grades with 95 to 98 pct accuracy. The integration of this online supervisory system in steel production is expected to minimize operators' corrective actions in achieving realistic control of Ca additions.

Calcium (Ca) additions during secondary steelmaking are a well-adopted practice to transform solid oxide non-metallic inclusions (NMIs) into globular-shaped liquid oxides. The claimed hypothesis that liquid NMIs reduce SEN clogging has been proven in the past by researchers. However, the exact quantity of Ca needed to transform the physical state of NMIs during steelmaking remains uncertain. Operators in the steel plant use a consistent quantity of Ca additions for specific steel grades, but this approach does not account for the varying physical states and evolving dynamics of NMI's characteristics in each 'heat'. To overcome this, a study was conducted to explore the impact of varying Ca additions on the transformation and behavior of NMIs in low-alloyed Ca-treated steel grades. The aim was to establish a more reliable and responsive approach to Ca treatment, potentially leading to more effective control in preventing submerged entry nozzle (SEN) clogging. The proposed methodology involved online monitoring of NMIs state coupled with controlled variations in Ca addition, deviating from fixed quantity, to observe its effects on NMIs state transformations. Through careful analysis of collected data and the implementation of a data-driven optimizer, this study reports the practical implications of using optimal amounts of Ca during secondary steelmaking. The resulting change due to dynamic calcium silicide (CaSi)-cored wire additions and their impact on SEN clogging were evaluated. The findings reveal the significant role of optimal CaSi wire additions, leading to improved steel castability and a notable 30 pct reduction in SEN clogging tendencies. The results obtained after the implementation of the data-driven optimizer 'ClogCalc' have significant implications for steel manufacturers, offering new insights into enhancing Ca treatment efficiency.

Development of reliable energy storage technologies is the key for the consistent energy supply based on alternate energy sources. Among energy storage systems, the electrochemical storage devices are the most robust. Consistent energy storage systems such as lithium ion (Li ion) based energy storage has become an ultimate system utilized for both domestic and industrial scales due to its advantages over the other energy storage systems. Considering the factors related to Li ion-based energy storage system, in the present review, we discuss various electrode fabrication techniques including electrodeposition, chemical vapor deposition (CVD), stereolithography, pressing, roll to roll, dip coating, doctor blade, drop casting, nanorod growing, brush coating, stamping, inkjet printing (IJP), fused deposition modelling (FDM) and direct ink writing (DIW). Additionally, we analyze the statistics of publications on these fabrication techniques and outline challenges and future prospects for the Li ion battery market.

Thin films of ice and water on soil particles play crucial roles in environmental and technological processes. Understanding the fundamental physical mechanisms underlying their formation is essential for advancing scientific knowledge and engineering practices. Herein, we focus on the role of the Casimir-Lifshitz force, also referred to as dispersion force, in the formation and behavior of thin films of ice and water on soil particles at 273.16 K, arising from quantum fluctuations of the electromagnetic field and depending on the dielectric properties of interacting materials. We employ the first-principles density functional theory (DFT) to compute the dielectric functions for two model materials, CaCO3 and Al2O3, essential constituents in various soils. These dielectric functions are used with the Kramers-Kronig relationship and different extrapolations to calculate the frequency-dependent quantities required for determining forces and free energies. Moreover, we assess the accuracy of the optical data based on the DFT to model dispersion forces effectively, such as those between soil particles. Our findings reveal that moisture can accumulate into almost micron-sized water layers on the surface of calcite (soil) particles, significantly impacting the average dielectric properties of soil particles. This research highlights the relevance of DFT-based data for understanding thin film formation in soil particles and offers valuable insights for environmental and engineering applications.

Additive manufacturing (AM) is a useful technology to produce artificial aortic models for the training of transcatheter aortic valve replacement (TAVR) surgery. With AM, the models can be tailored towards the individualized aortic anatomy of patients. Most of these reported models so far are manufactured using single rubber-like materials. However, such materials do not replicate the mechanical properties of natural aortic tissue, especially the stress-strain response in higher strain (>0.1) regions. This could be problematic for surgeons training for surgeries using a model which does not exhibit properties of the real aorta. To overcome this limitation, we developed a 3D-printed, mechanically representative aortic model comprising gelatin fibers and silicone. The model is promising as a realistic analog of aortic sinus for mock TAVR surgery. Computerized tomography data was analyzed beforehand using medical imaging to identify the anatomy of a specific patient's aortic sinus and the surrounding blood vessels. A novel silicone matrix composite reinforced with gelatin fibers designed in this work was tested and compared with the stress-strain response of aortic tissue. Such a model comprising both patient-specific geometries as well as realistic material properties of aortic tissue can be helpful for the development of next-generation medical phantoms.