The increased use of electric arc furnace (EAF) steelmaking using up to 100% direct reduced iron (DRI) has prompted an interest in better control of phosphorus since iron ore and, consequently, DRI have higher phosphorus and silica compared to scrap. There is limited work reported on slag chemistries corresponding to that in the EAF when DRI is used. In the current research, phosphorus equilibria between molten Fe–P alloys and CaO-SiO₂-Al₂O₃-P₂O₅-FeO-MgO_saturated slags were investigated. The results indicate that there is a significant decrease in the phosphorus partition coefficient (L_P) as alumina in the slag increases. The observed effect of alumina on the phosphorus partition is probably caused by the decrease in the activities of iron oxide and calcium oxide. Finally, an equilibrium correlation for phosphorus partition as a function of slag composition and temperature has been developed. It includes the effect of alumina and silica and is suitable for both oxygen and electric steelmaking-type slags.

Climate change is one of the most pressing matters of modern society. The current models for assessing CO₂ emissions do not consider the oxygen drainage from the atmosphere. Oxygen depletion is happening at rates much higher than the natural capacity of the planet to regenerate the oxygen back to the atmosphere through photosynthesis. On average, each 1.0 ppm increase of CO₂ brings a corresponding loss of 2.15 ppm of O₂ in the atmosphere. The present work provides a new methodology for emissions assessment that includes oxygen usage called Atmospheric Profit and Loss Score. Simple examples are provided to demonstrate that, once oxygen is included in the assessment, renewable and photosynthesis-based processes that follow the sustainability criteria become much more attractive to society.

Control of calcium treatment in steel is challenging due to the reactivity of Ca and difficulty of measuring total oxygen of steel in-process to make actionable decisions. In this work, a method combining statistics and process engineering are developed using partial least squares regression (PLS) to predict non-metallic inclusion content (oxides and CaS) and composition at the end of ladle treatment and in the tundish using extensive process data and SEM/EDS-based non-metallic inclusion analysis. Total oxygen at the end of the ladle treatment can be predicted to an accuracy of 7 ppm, and the Mg/(Mg+Al) ratio in inclusions to an accuracy of 3at% providing enough data to recommend Ca addition based on a thermodynamic calculation for the Ca liquid window. Alternatively, the model can predict total inclusion fraction to 20 ppm accuracy, and accurately predict average CaS content and Ca/Al ratio of inclusions in the tundish. Model interpretability is hindered by high dimensionality and multicollinearity of the data. Non-metallic inclusion compositions correspond to the expected composition at the onset of CaS formation based on steel composition.

Automated experimentation and self-driving laboratories (SDLs) have a transformative potential to accelerate and revolutionize scientific discoveries. However, their adoption by the general scientific community has been limited due to the cost of automation, such as instrumentation and laboratory infrastructure. In this work, we demonstrate the utilization of a centralized (semi-)automated lab as a cost-effective and accessible platform to democratize autonomous research. Specifically, by integrating a remote automated lab with artificial intelligence (AI), we developed a workflow called autoHSP, to demonstrate an autonomous and closed-loop determination of Hansen Solubility Parameters (HSPs) via batch-mode active learning (BMAL), without the need for any hands-on instrumentation setups. We developed algorithms to design experiments and analyze experiment outcomes, while outsourcing the experimentation to the automated lab remotely. A server was set up to handle communication between the automated lab and the AI framework securely with encryption. The lab received instructions and executed the experimental protocols to prepare the requested samples and report their images as characterization results. Then, a computer vision (CV) algorithm hosted on the server analyzed the images to determine the experiment outcome, and the BMAL algorithm selected solvents to optimize experiment design based on the most recent experimental knowledge until the end of experiment (EOE). Together, these demonstrate that this autonomous workflow can be adapted for general research where researchers can focus innovations on the AI-based software design.