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There has been a rising interest in the iron and steel industry in replacing fossil-based carbon carriers in their processes because they are the main origin of the anthropogenic carbon emissions within the industry. The use of bio-based carbon carriers could be one solution to partly tackle this challenge. Conventionally, biocarbon is produced by pyrolysis with fixed heating rate, pyrolysis temperature, and retention time. Although the mechanisms behind the formation of biocarbon and the decomposition temperatures of the main compounds of biomass-based materials are known, this knowledge is rarely being utilized in the design of commercial pyrolysis reactors, even though the pyrolysis mechanism-based approach increases the biocarbon yield. In this study, the mechanistic pathway of carbonization of lignocellulosic biomass is taken into account to produce biocarbon with higher yield and quality than conventional pyrolysis with the same process time. Results show that when the process time is the same in both methods, segmented pyrolysis increases biocarbon yield up to 5.4% within a pyrolysis temperature range from 300 °C to 900 °C. Also, fixed carbon yield increased 1.5% in this temperature area. When using segmented pyrolysis, the most suitable pyrolysis temperature is 700 °C based on the characteristics of the produced biocarbon. [ABSTRACT FROM AUTHOR]

The formation of defects such as cracks in continuous casting deteriorates the quality of cast products and efficiency of steelmaking. To evaluate the risks and identify the root causes of defect formation, phenomenological quality criteria computed with a solidification and microstructure model known as InterDendritic Solidification (IDS) have previously been applied. This approach is computationally efficient and provides a fundamental perspective to defect formation in continuous casting. The aim of this work is to study the capabilities of these criteria as features in predicting transverse cracking with interpretable machine learning models. IDS is coupled with a heat transfer model known as Tempsimu to simulate the continuous casting process. Measured compositions are utilized in the simulations and defects reported at a steelmaking plant are used as labels in classification. Logistic regression, decision tree, and Gaussian Naïve Bayes classifiers are developed to predict transverse cracking in peritectic C–Mn, low‐carbon B–Ti microalloyed, and peritectic Nb‐microalloyed steels. The corresponding balanced accuracies of the best classifiers from cross‐validation are 92%, 94.6%, and 82.8%. Due to the good performance and the interpretability of the developed classifiers, the fundamental causes of transverse cracking and possibilities of avoiding it by changes in the compositions are identified. [ABSTRACT FROM AUTHOR]

The evolution from natural gas usage to new technologies, such as the use of hydrogen as fuel or electricity‐based heating, strongly influences the oxidation of the stainless steel surface in the reheating furnace. Thermogravimetric tests using different simulated combustion and induction reheating conditions are performed for austenitic AISI 301, AISI 304, and ferritic AISI 444 steel grades. Simulated furnace atmospheres in combustion methods are based on methane–air, methane–oxygen, hydrogen–oxygen, and methane–hydrogen–oxygen combinations. For induction simulations, air and nitrogen are used as furnace atmospheres. The results indicate that changes in heating conditions to H2‐fueled combustion or induction only have a minor influence on the oxidation of the ferritic grade; whereas, their effects on the austenitic grades are more pronounced. The transition from a methane–air to H2–oxyfuel combustion increases the total oxidation by 1.7 and 4 times for steel grades 304 and 301, respectively; therefore, grade 304 can be considered better suited for transition for H2–oxyfuel use. The shorter induction heating considerably decreases the amount of oxide scale for austenitic grades, but the nitrogen atmosphere produces a subscale inside the steel matrix, which can hinder the descaling process. [ABSTRACT FROM AUTHOR]

Hot metal desulfurization operates as an extraction process in steel production after the blast furnace. Mathematical modeling of the process provides the basis for control and optimization solutions. Owing to complex dynamics, an exhaustive physico-chemical description of the process is computationally infeasible. Thus, a model with a gray–box structure is considered. In the model, a neural network model for sulfide capacity of the slag is implemented in series with the reaction model. The network is trained using an independent data set. The parameters for the reaction model are identified using genetic algorithm with real-encoded population and hybrid selection and recombination operators. The gray-box model parameter distributions are estimated using Metropolis Hastings Algorithm. This study suggests that a sufficiently low mean absolute error can be achieved for the end sulfur content. The relative contribution of the permanent contact reaction to overall reaction rate under industrial conditions remains uncertain. [ABSTRACT FROM AUTHOR]