**1. Introduction**

The processing of lower grade ores is a topic of particular interest, as fluctuation in raw material cost is a key challenge to sustainability in the steel industry. Raw materials flexibility is to a great extent enabled in the basic oxygen steelmaking (BOS) process, wherein oxidizable impurities, such as phosphorus (P) and silicon (Si), are separated into a slag phase. However, the primary purpose of the BOS process is to convert pig iron into crude steel and, therefore, impurity removal being a secondary function of the converter, has to be balanced by the decarburization process.

As a result, processing parameters have to be balanced, and many different chemical reactions compete for the available oxygen. There are also many processing parameters that are interconnected in complicated ways that cannot be readily predicted. The BOS process is in general advised by static mass- and heat-balanced models, which predict, based on inputs, the resultant end-point. The input parameters include at least the quantities of hot metal, steel scrap, iron ore, fluxes and oxygen to blow, while the resultant end-point is the temperature, weight and composition of steel produced. The process control is supported by various measurements, such as in-blow sampling, end-blow sampling, oxygen flow rate/ total oxygen blown, lance height, waste gas flow rate, waste gas pressure, waste gas composition, etc. The operating information is recorded and stored in a plant manufacturing system normally at the interval of one second. Dynamic process modeling of the BOS process, depending on the researchers, can be based on thermodynamics [1], multizone reactions [2–4], empirical or semi-empirical plant data regression, in combination with computational fluid dynamics (CFD) in

some cases [5]. Data-driven modeling techniques have been applied in the dynamic modeling of basic oxygen processes. These include the artificial neural network (ANN) [6–8], fuzzy logic [9]), support vector machine [10,11] and case base reasoning [12]. However, most of these models are limited to the end-point control of carbon and temperature.

The pilot plant work published by Millman et al. [2] ("IMPHOS" or Improved phosphorus refining) provides a set of data on top-blown oxygen steelmaking under conditions similar to an actual BOS plant. The trials were carried out in a six-ton pilot converter at the MEFOS in Sweden and provides data for heats through samples obtained from seven vertical positions in the vessel at 2-min intervals. In addition, 28 process data parameters (including decarburization rate) were recorded every second. The major parameters recorded are the total of materials added as a function of time (hot metal and fluxes); oxygen flow rate/total amount of oxygen blown (total oxygen flow); lance height; waste gas (temperature; flow rate and composition of CO, CO2, N2 and O2) and the data calculated from the above measurements, for example, dc/dt (the decarburization rate).

Using machine learning (ML) approaches to predict material properties is a research field that has been increasing in the last years [13]. Thermodynamic stability of perovskite, transition temperatures of inorganic glasses, glass-forming ability of metallic alloys and interphase precipitation in HSLA (High Strength Low Alloy) steels, among others, are examples of material properties studied by machining learning approaches [14–18]. Machine learning algorithms enable us to design new grades of alloys purely based on past data, either available as an open dataset or reported in the scientific literature [19].

The objective of this study is to carry out a machine learning (ML) analysis on the data generated from the six-ton pilot BOS trials (IMPHOS data) with the purposes of (i) identifying correlations between features and (ii) based on a training data and test data developing a neural network-based regression model (algorithm) to predict the decarburization rate. The tools that we employed in this study were Microsoft Power BI for statistical analysis; Python (Scikit-learn, Matplotlib and Seaborn) for performing correlation analysis and statistical analysis and Microsoft Azure Machine Learning Studio for regression models and their subsequent performance evaluation [20]. The algorithm developed here is used to successfully predict the decarburization rate (dc/dt) in an actual manufacturing plant based on two operating parameters of lance height and total oxygen flow.

#### **2. The Multiphysics of the Basic Oxygen Steelmaking Process**

The overall purpose of the BOS process is to refine hot metal (pig iron) to a steel of desired chemistry (C, Si, Mn, P and S) and temperature in a controlled manner in a refractory converter (as shown schematically in Figure 1). In the BOS process wherein C-rich pig iron is converted to steel, pure oxygen is injected though a water-cooled lance into the molten metal bath, and the bath is covered by molten oxides (slag phase). The role of the oxygen is to selectively react with oxidizable elements and primarily remove the C (from >4% to less than 0.1%) as a gas phase but also remove other unwanted impurities, stemming from the iron ore as oxides, that separate and dissolve into the slag phase.

The kinetics of the BOS process is very complex, because it involves multiple simultaneous processes. Simultaneous multiphase interactions, heat and mass transfer, gas-slag-metal chemical reactions in multiple zones and vigorous fluid flow caused by the impingement of the oxygen jet occur in a BOS reactor at high temperatures. In addition, it is a dynamic and transient process, which makes the kinetics involved in a reactor more complex. Direct measurements of temperatures and chemistries are very difficult due to the nature of the process, which involves harsh conditions. That is why many researchers have been trying to address these difficulties through modeling the process.

A multiphysics description of the converter process, with the ultimate aim of predicting carbon content in the melt, will have to involve several sub-models at different scales. These models will have to capture various phenomenon occurring in the gas/metal, metal/slag and metal/slag/gas mixtures, as well as transport processes in the bulk metal and bulk slag. The models include, but are not limited to, the following:

**Figure 1.** A schematic of the pilot plant BOF converter used in the Improved Phosphorus-refining (IMPHOS) trials with dimensions and indicated location heights of the sampling levels [2].

(i) Multiphase fluid flow in the various multiphase mixtures. Since this is a complex and computational task by itself, it is often simplified to 2D and informed by physical water models [5,21,22].

(ii) Reaction kinetics and competition between the injected oxygen and various dissolved elements, such as C, but also P, Si, Mn, etc. [4,23].

(iii) Microscale interactions between the phases, which include the separation of elements and precipitated phases across interfaces and emulsification [4,24,25].

The decarburization process mainly takes place in two reaction zones: namely, the jet impact zone and the gas-slag-metal emulsion zone [4,24]. At the emulsion zone, a sequence of chemical reactions take place: Firstly, FeO reacts with CO to form CO2 at the slag-gas interface. Secondly, CO2 transfers from the slag-gas interface to the gas-steel droplet interface via the gas phase. Subsequently, CO2 reacts with carbon in an iron droplet at the metal-gas interface, and the formed CO is transferred back to the slag-gas interface via the gas phase. It should be noted that there is disagreement among researchers about the description of the mechanisms and the rate-controlling step(s) under different operating conditions [26]. Some other studies proposed that the reaction rate is controlled by the interfacial area change because of material exchanges during the process. The phase field models developed according to the interfacial instability normally consider only the interfacial tension and fluid flow [24]. The coupling of models across scales is a hard enough (and computationally intensive) problem, which can be simplified and described under simplified process windows. However, the predictive capability under widely ranging process parameters is not possible. Consequently, all the previous studies, although mechanistic in nature, have different levels of simplification to address the complex phenomena in the BOS reactor.

The removal of elements (C, Si, Mn, P and S) from pig iron in the industrial BOS process is mainly controlled by two operating parameters: lance height (i.e., the height of the lance tip above the static steel bath) and oxygen blowing rate (or total oxygen flow/blown). The control strategy can be varying one or both of these two parameters. The lance profile (i.e., lance height as a function of blowing time) is critically important for the efficient production of high-quality steel. For example, a high lance position can help stir and oxidize the slag with a high FeO content, which can accelerate the removal of carbon. A low lance height can substantially increase the impingement of the oxygen jet into the metal bath and splash the metal droplets into the slag layer, which accelerates the formation of the emulsion zone and the removal of carbon and other elements through the interfacial reactions between the massive numbers of metal droplets with emulsified slag [27]. The refining behavior (e.g., the decarburization rate) has a close relationship with the lance profile and oxygen blowing rate (or total oxygen flow). Thus, in this work, we intend to develop a novel algorithm to predict the refining behavior of the BOS reactor (using the decarburization rate as an example) by using machine learning to analyze the massive dataset of operating parameters, including lance height and total oxygen flow.

Using machine learning, we propose a technique that does not employ any simplifying assumptions. On the contrary, the algorithm was trained on a real dataset. In addition, the machine learning algorithm does not require to take into account any of the physics involved in the process. It actually provides a ring road to all the complexities involved in a BOS reactor. In addition, as shown later, the technique is able to predict the decarburization rate precisely. The aim of the current study is, thus, twofold: (1) Firstly, to identify the correlations and trends from a set of processing parameters on carbon removal. (2) Secondly, to try and circumvent the granularity of multiphysics and develop an artificial intelligence-based predictive model for carbon removal.

### **3. Dataset**

A six-ton BOF (Basic Oxygen Furnace) pilot plant converter located at Swerea MEFOS, Sweden, as part of the European commission funded project IMPHOS: Improved Phosphorus refining [2]. In this reactor, operating conditions, such as oxygen blow rate (total oxygen flow), lance height, the quantities of hot metal and fluxes used and off-gas analysis, are recorded. Gas/slag/metal emulsion samples were taken from seven various heights and at 2-min intervals from the start of blowing during a blow via robotic delivery. The sampler lances consist of an inner and outer structure of three mild steel bars, the inner being joined to the sides of inline sample pots and the outer joined to the sides of the disc sample pot lids. The sampler lance is lowered into the converter through an opening in the top (with a slight offset from the oxygen lance), with the lids in the closed position. Once lowered into the position, the outer three bars are retracted, lifting the lids in situ and allowing the sample pots to fill. A schematic of the converter is given in Figure 1 with dimensions. The converter had a lining of magnesia-carbon (fired and fused) bricks. Gas inlets include a single bath agitation tuyere, blowing nitrogen at a rate of 0.5 Nm3min−1, and a water-cooled oxygen lance blowing oxygen at a norminal rate of 17 Nm3min−1. During the blow, the following was measured as output data from the reactor: oxygen flow rate/total oxygen flow; lance height and waste gas (temperature; flow rate and composition of CO, CO2, N2 and O2).

There are various models, such as static (mass balance model, thermodynamic model, etc.); empirical (regression based on individual plant data) and dynamic. These models are intended to predict the end-point of steel or the steel/slag compositions as a function of blowing time and to control/optimize BOF steelmaking. In the BOF process, the blown oxygen is used for decarburization; the removal of other impurities of Si, Mn and P; the formation of iron oxides in the slag and post-combustion. A typical example is off-gas information. The industry has been exploring the use of off-gas information for process control. The most important one is to use the off-gas composition to monitor the decarburization rate in the BOS converter. If the decarburization rate can be precisely controlled or predicted, then under the known oxygen-blowing conditions, the amount of oxygen in the slag (iron oxides) and the amount of oxygen to remove other elements, especially for phosphorus, can be predicted. The decarburization rate can be calculated by the off-gas analysis (CO, CO2, N2 and O2). Then, we will link the decarburization rate (calculated from off-gas composition) to the operating parameter(s). This will provide the foundation to predict the decarburization rate (dc/dt) and, as a result, dynamically control the converter operation. The features we are investigating from the dataset are presented in Table 1. The other operating conditions are listed in Table 2, including the chemistries of hot metal; steel and slag; temperature; charges (hot metal, scrap and fluxes) and lance height.


**Table 1.** The operating parameters and their features obtained from the six-ton pilot plant BOF converter system.

Note: dC/dt (kg C/s) is calculated from the waste gas composition, which was recorded in the operating system in every second, while dc/dt (kg C/min) is converted from dC/dt (kg C/s). The decarburization rate is generally expressed as dc/dt (kg C/min), and therefore, the target of this study is to predict dc/dt (kg C/min) by a machine-learning (ML)-based algorithm from the operating parameters. It should be pointed out that dC/dt (kg C/s) is still mentioned in this study for the purpose of explanation.

In order to validate the correlation between the dc/dt and the operating parameters (the ML-based methodology, as explained in Section 4, Methods, to predict dc/dt from operating parameters) established from the six-ton BOF converter, production information has been taken from a 330-ton converter at the Tata Steel UK Port Talbot Plant. The operating parameters and the resultant information include oxygen flow rate; total oxygen flow; off-gas analysis (temperature, rate, composition CO, CO2, N2 and O2) and dC/dt (kg C/s) calculated from the off-gas analysis. A total of 1100 data points were obtained at different times for each value corresponding to Table 1, from the 6-ton pilot reactor, which were used for training and test data. In addition, a total of 1200 points corresponding to known conditions in the 330-ton full scale reactor were used as additional test data to investigate if the trained model could predict the decarburization rate in a scaled-up system. It should be noted that differences in the levels/quantities of the parameters exist between the pilot and industrial converters; for example, the heat size (6t for pilot converter and 330t for industrial converter), blowing time (~18 min for the 6t pilot converter and ~20 min for the 330t industrial converter), lance height (110~180 mm for the 6t pilot converter and 2.0~2.6 meters for the 330t industrial converter), O2 flow rate (14.00~17.30 Nm3/min for the 6t pilot converter and 598~1025 Nm3/min for the 330t industrial converter) and total oxygen

flow (278 Nm<sup>3</sup> for the 6t pilot converter and 18863 Nm3 for the 330t industrial converter). However, the parameters and their features recorded in their systems are similar for both conditions, and in this paper, we use the dataset from one heat of both the 6t pilot and the 330t industrial converters, respectively, to develop and demonstrate the machining learning-based model for the prediction of the decarburization rate from operating parameters under different conditions.


**Table 2.** The operating conditions for both the six-ton (6t) and 330t converters.

### **4. Method**

While neural networks are mainly known for applications in deep learning, they can be easily used for regression problems and are especially suitable when linear regression does not work. Neural network regression is another supervised learning algorithm available in Microsoft Azure Machine Learning Studio and requires a label column which has to be numerical [20]. Since our label column (dc/dt) was numerical, and the results generated by classical linear regression had low accuracy, we used a neural network regression. Linear regression resulted in a coefficient of determination of 0.45 (Table 3, column 2), which was much lower than that by using neural network regression (0.99) and deemed to be below what is acceptable. The output of a single neuron has a form of *g(*Σ*jwjixj)*, where *wji* are the weights and *xj* are the input. A continuous activation function, e.g., sigmoid function of a form of 1/(1 + *e*−*<sup>x</sup>* ), was employed. We used "Parameter Range" in the Create trainer mode and subsequently employed the Tune Model Hyperparameters module to iterate over the possible combinations of parameters to achieve the optimal configuration. In "Hidden layer specification" we selected "fully connected case" to create a model that has exactly one hidden layer, and the output layer is fully connected to the hidden layer, and the hidden layer is fully connected to the input layer. Number of nodes in the hidden layer and learning rate were used as the hyperparameters. The optimized number of hidden nodes was calculated to be 100. Another hyperparameter that was tuned was the learning rate. Learning rate is the step taken at each iteration before correction. A large value of the learning rate makes the convergence faster; however, it can result in overshooting the local minima. The tuned learning rate was calculated to be 0.1. Min-max normalizer was selected to linearly rescale each feature to the [0, 1] interval. Rescaling to the [0, 1] interval was carried out by shifting the values of each features, i.e., the minimum value is 0, and then dividing by the new maximum value. The error after each iteration is calculated and "back-propagated" to the network using the chain rule.


**Table 3.** Evaluation metrics of the predicted values of the dc/dt by using different features in the pilot dataset and using different regression methods.

(1) Using linear regression and (2) using neural network regression.
