Machine learning-based prediction of a BOS reactor performance from operating parameters

A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors. View Full-Tex

Control of intermetallic nano-particles through annealing in duplex low density steel

In high Al-low-density steels for future vehicle light weighting, it is vital to design a thermal profile to form and retain the uniformly dispersed nanosize B2-type intermetallic precipitates that are crucial for the material strength. In this paper, the influence of heating rate, during annealing to 1050◦C was simulated in a Au-image furnace. The post annealing structure was then characterized and two different morphologies of B2 particles were observed: triangle-like with a few micrometres and disk-like precipitates with a diameter of around a few hundred nanometres. It was found that a slower heating rate (2.5 ◦C/s) led to an increase in the volume fraction and to uniform distribution of particles within the microstructure and considerably affected the shape and size of the precipitates

Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model:Experimental Validation in a Residential Building

Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F1 score. Indoor climate data used in this work are CO2, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion

Effect of Ni alloying on the microstructural evolution and mechanical properties of two duplex light-weight steels during different annealing temperatures : experiment and phase-field simulation

This paper presents a study of two lightweight steels, Fe-15Mn-10Al-0.8C-5Ni and Fe-15Mn-10Al-0.8C where strength is dependent upon the microstructure of 2nd phase precipitates. We investigate the effects of annealing temperature from 500 °C to 1050 °C on the precipitation of ordered phases size and morphology through phase-field modelling and experimental studies based on laboratory scale annealing and characterization. The chemical composition of carbides and B2 compounds as a function of isothermal annealing temperature and the matrix within which they formed are elucidated in this study. It is found that nano-sized disk-shaped B2 particles form at higher annealing temperatures (e.g. 900 °C and 1050 °C). The simulation results on carbides demonstrated the effects of energetic competition between interfacial energy and elastic strain energy on the morphological evolution of carbides. In addition to that, different ordering behaviours observed depending on the Ni content into the steel. The results demonstrate processing route designed through the phase-field simulations led to a better combination of strength and ductility. The tensile testing results indicate an increase in the strength and elongation when B2 precipitate morphology changes from micro-size faceted shape to nano-size disk-like particles

A phase-field method coupled with CALPHAD for the simulation of ordered κ-carbide precipitates in both disordred γ and α phases in low density steel

In order to simulate multi-component diffusion controlled precipitation of ordered phases in low density steels using the phase-field method, the Gibbs free energy of the γ, α and κ phases in the quaternary Fe-Mn-Al-C system was linked to the CALPHAD method using a three-sublattice model which is based on the accumulation of considerable thermodynamic data in multi-component systems and the assurance of continuous variation of the interface area. This model includes the coherent precipitation of κ phase from a disordered FCC γ phase and semi-coherent precipitation of the same κ phase from a disordered BCC α structure. The microstructure evolution of κ- carbide was simulated with three-dimensional phase-field model. The simulation was first performed for a single particle in both γ and α phases to investigate the evolution of interfacial and elastic strain energy during the precipitation process. The simulation results show that κ has a cuboidal morphology in γ and elongated plate-like morphology in α which is in agreement with the morphologies reported in the literature. The multi-particle simulations were also performed for the precipitation of κ phase from both disordered γ and α. The results also demonstrate that the size of κ precipitates in γ is remarkably smaller than that in α phase

Effect of electric current pulses on the microstructure and niobium carbide precipitates in a ferritic-pearlitic steel at an elevated temperature

Niobium is an important alloying element in steels. In the present work an effort has been made to investigate the effect of electropulsing on the niobium carbide (NbC) at an elevated temperature (800 °C). The results show that the electropulsing treatment can generate an evenly distributed NbC by decreasing the kinetics barriers for precipitation. It has been also found that a semitransformed pearlite structure forms in such a way that the grains are oriented toward a direction parallel to that of the electric current flow. Furthermore, the electropulsed sample benefits from refined grain size. This is thought to be due to the electropulse-enhanced nucleation rate. Tensile testing has been carried out to compare the properties of electropulsed sample with that of without electropulsing. The results show that the sample with treatment has greater yield strength and ultimate tensile stress while its elongation is only 1% less that of the unelectropulsed samples. The improved mechanical properties of the sample with pulsing are attributed to its finer grain sizes as well as the elimination of precipitation free zones caused by the electropulsing treatment

A phase-field model for interphase precipitation in V-micro-alloyed structural steels

A multi-component phase field model was developed based on CALPHAD method and directly coupled with the CALPHAD thermodynamic database using a four-sublattice model. Interphase carbide precipitation at the γ/α interface is simulated and the predictions are tested against reported experimental results for a medium carbon, vanadium micro-alloyed steel during an isothermal γ→α+MC transformation at 973 K. The model is found to be able to accurately predict: interphase precipitate composition, morphology and size of the precipitates. Furthermore, the tip-to-tip pairing of interphase precipitates in γ/α interphase boundaries is elucidated and found to be attributable to the minimisation of interfacial energy

A Gibbs Energy Balance Model for Growth Via Diffusional Growth-Ledges

Growth ledges are commonly observed on interphase boundaries during diffusional phase transformations and are of great importance for understanding inter-sheet spacing of interphase precipitates. A simple model based on Gibbs Energy Balance (GEB) for describing growth kinetics via diffusional growth-ledges of height λ is presented for the case of ferrite growth into austenite. The model is validated against the case of austenite to ferrite transformation involving interphase precipitation in a V, Mn, Si alloyed HSLA steel where, λ is assumed to be equal to the inter-sheet spacing of interphase carbide precipitates. The presented model provides a computationally efficient and versatile method for predicting the ledge height, λ, and the growth kinetics of ferrite from initial nucleation through to final soft impingement considering the evolution of solute drag at growth ledge risers. It is suggested that the intrinsic mobility of growth ledge risers is: M_m^αR=0.58exp((-140×〖10〗^3)/RT) mmol.J^(-1) s^(-1), with R the gas constant and T the absolute temperature in K