3.1. Classification of BOF Slag
Once the dataset was analyzed and the anomalous cases were removed, the networks were then trained and compared and contrasted with all data. Several tests were performed, modifying the number of artificial neurons in the network; thus, excellent results were found in identifying the clusters generated from 64 neurons onward when taking the chemical components of slag as inputs.
Once trained, the k-means technique was applied in order to group together the representative elements of each cell and, therefore, obtain the minimum number of clusters that generated the minor error in classification. In this case, eight clusters were identified, which are displayed in
Figure 5, with each color representing a different slag cluster.
Each cell was linked to an artificial neuron of the neural network, and the closeness between the cells indicated the similarities among data that had fallen in each cell. This allowed the k-means technique to group together cells with similar elements into a bigger cluster.
From the analysis of the data of each slag cluster, a taxonomy could be performed on the chemical components that defined each of the eight clusters. In order to make it easier to use, the variation ranks of the slag chemical components were coded into qualitative ranks. Therefore, k-means clustering was undertaken for three of the clusters (which should have been reduced to two clusters in some cases as, for example, in the magnesium oxide) coded as high (H), medium (M), and low (L). The main results derived from the previous studies are displayed in
Table 2.
Once these groups were identified, it was considered necessary to identify what type of steel was produced when a certain type of slag was obtained as a useless co-product. Thus, it would be possible to identify the potential applications of each one of these groups of slag.
After the dataset analysis, 84 steel grades were found to have been produced in the plant where the data were taken from during the sampling period. Given the high number of grades, it was decided to group the steel grades with similarities when regarding their chemical components.
In order to find the steel grades with similar chemical components, the SOM technique was also used; thus, in each of the neural network cells, steel cases with similar behavior could be found.
For this case, a grid was taken as network topology, since the aim was to identify generic clusters instead of very detailed ones. It was for this reason that the decision to distribute a topology of 3 × 3 artificial neurons was made.
Figure 6 shows the average value of every chemical composition of steel in each of the nine artificial neurons.
Artificial neurons with similar behaviors were found (remarked in blue and red in
Figure 6), since similar castings could be distributed into two different artificial neurons—e.g., the two cases circled in blue and the ones circled in red.
The following case involved clustering castings according to their types of steel and slag by means of creating neural networks. With this dataset, a pivot table was created, where steel grades and types of slag were exposed and the number of castings were calculated. The steel grades with similar distribution in all of the neurons were linked, since they presented similar behaviors in the network.
When this reduction was finished, there were 18 steel grades and 8 types of slag. Therefore, a cross-reference (
Table 3) could be generated, where the types of steel grades that generated a certain type of slag could be learned.
An irregular distribution of castings in the different cells of the pivot table can be observed. There were some cases, such as the steel class 17, that almost corresponded to the slag cluster 5 (both classifications were done independently, with one of them using slag components and the other one with components of the resulting steel). Slag clusters 3 and 6 corresponded to high ferric oxide percentages and represented most of the castings. However, there were cases, such as steel class 15, that always presented slag with relatively low steel concentrations, corresponding to slag clusters 1 and 2. However, the ultimate aim of this study was to link classifications with prospective applications, which will be detailed in the following section.
3.2. Relations between Slag Clusters and Potential Applications
Due to the vast research performed with the aim to valorize slag, it is interesting to obtain a relationship between potential applications and slag clusters [
18,
19,
29,
30]. If this is performed for every possible application, a better valorization of each slag cluster can be obtained.
There are many possibilities when valorizing BOF slag; in order to analyze the potential of the clusters, this relation was studied for the three potential applications proposed as application examples (aggregate for road constructions, railway ballast, and environmental remediation).
The use of slag as an aggregate for road construction is one the most studied valorizations. It is one of the applications with more volume of material to be valorized. Its main restriction in the use of the aggregate in the superficial layer is the expansion that the slag may cause. This expansion is linked to the quantities of CaO and MgO; thus, it is necessary to achieve lower values of both CaO and MgO.
Therefore, the most interesting slag clusters for these applications are displayed in
Table 4.
Thus, the slag that best adjusts in terms of its chemical composition for its use in road construction can be selected.
Due to the similar characteristics of slag and gravel, it is possible to use it as a railway ballast [
30]. However, the iron content in the slag presents a problem for its valorization due to the conductivity that it may generate. Thus, slag used for this purpose must have low iron content.
The most interesting clusters for this application are displayed in
Table 5.
Slag cluster 1 presents the best properties for its use as a railway ballast.
Due to the high values of lime (and pH), slag is interesting as an acid neutralizer: e.g., mining acid water. The characteristics of slag must include a high CaO content and a low SiO2 content (to obtain a high pH value).
The most interesting clusters for this study are the ones displayed in
Table 6.
The most suitable characteristics for valorization as a material for environmental remediation due to a high pH are in cluster 8.
This analysis must be performed with each of the potential valorizations, which may be different for each steel shop depending on the availability of aggregates in the environment, the available cement factories and other factories, and the distance in transportation. The aim is to know what the ideal slag distribution is in order to minimize treatments and to maximize by-product valorization. In this way, it is possible to maximize the valorization of BOF slag and to minimize the amount of this co-product in landfills.