# Self-Organizing Maps to Assess the Recycling of Waste in Ceramic Construction Materials
## 3 Results and Discussion
### Comparative Analysis of Clay Mixtures at Different Manufacturing Scales  
The comparative analysis by SOM of the 56 studied samples at laboratory, pilot-plant and industrial scale lead to a trained map (Figure 6) based on \"range\" normalization of data of 8 \(\times\) 5 (40 neurons), similar to the size of the 37-unit map proposed by the algorithm [59], with QE = 0.289 and TE = 0. The shape of the four clustered maps in Figure 6a indicates that samples are effectively sorted by, firstly, the type of clay and therefore, firing temperature; secondly, by Waelz slag content, being independent of the foundry sand addition, and thirdly, by the type of processing (laboratory-pressing, semi-industrial extruder, industrial, extruder). Cluster I grouped Clay 1 based samples that includes all semi-industrial samples up to 30% of WS, and laboratory and industrial samples without Waelz slag. Samples in this cluster show the lowest values of D and MOR, but the highest values of WA and OP. The metal release shows good behaviour with low metal leached concentrations. Cluster II includes laboratory samples of all clays with almost no content of WS, as well as the industrial samples IC4, IC4W20 which means that up to 20%WS does not substantially modify the behaviour of the brick. This cluster is of remarkable importance because of its minimum metal leaching while preserving intermediate values of the technological properties. Cluster III includes C2 and mainly C3-based laboratory pieces with high content of WS. These pieces fired at the highest temperature, show the highest MOR values, as well as the lowest WA and OP, even those with high waste dosages. Mo release exhibit medium-high values, even, samples with the highest WS content exceed the threshold for non-hazardous landfill in the European Council Decision (Annex I). However, it is not just one cluster that surpasses those values. Some neurons of clusters III and IV surpass the values, as are highlighted in the Mo component plane (Figure 6b). Figure 5: (**a**) Clustered map with labels of the studied pilot-plant and industrial clay mixtures with WS incorporation. The 4 clusters (I–IV) have been derived from the k-means algorithm applied to the trained SOM. The labels in the hexagons of the map show the BMU corresponding to each sample. (**b**) Component planes of the SOM for the 12 input variables with Mo highlighted along with neurons that exceed non-hazardous landfill limits. Each map corresponding to one variable (component) should be compared to the map representing the distribution of the pilot-plant and industrial clay mixtures with WS incorporation presented in Figure 5**a**; hexagons in the same place on different component planes correspond to the same map unit. The colours indicate the value of the component in the weight vector of each unit of the map, according to the colour bars on the right. Figure 6: (**a**) Clustered map with labels of the 56 studied samples from laboratory, pilot-plant and industrial clay mixtures with WS incorporation. (**b**) Component planes of the SOM for the eight input variables with Mo highlighted along with neurons that exceed non-hazardous landfill limits. Samples with the highest content of WS, independent of being pressed or extruded, are grouped on cluster IV. C1-based mixtures with the highest amount of WS are located in this cluster, as well as some C2 based mixtures. The main characteristic of this cluster is the high release of metals Ba, Cr and Mo, along with a good performance on the technological properties (Figure 6b). The high values of water absorption (WA) and open porosity (OP) and low values of density (D) and flexural strength (MOR) in cluster I and IV are inverse correlated with the firing temperature (the lower in these clusters) and directly correlated with the addition of wood pulp in the semi-industrial mixtures (cluster I) used to improve product performances.