Self-Organizing Maps to Assess the Recycling of Waste in Ceramic Construction Materials

Abstract

Circular economy promotes the use of waste materials into new production processes as a key factor for resource efficiency. The construction sector, and specifically the fired clay industry, is able to assimilate large amounts of waste in their processes, without significantly altering the technical properties of products. The introduction of different waste in ceramic products at the laboratory level has been extensively studied in the literature, but most of these studies have not yet been scaled-up to industrial production. Differences in processing with respect to laboratory conditions introduces uncertainty in relation to the expected properties of the final products. This paper uses a Self-Organizing Map (SOM)-based methodology for analysing and assessing the incorporation of industrial waste, Waelz slag (WS) and foundry sand dust (FSD), in ceramic products obtained sequentially at laboratory, semi-industrial and industrial level, over technological properties and metals release. As a result, from the SOM analysis, a clustered map of the samples developed is obtained that highlights the most important parameters affecting the technological and environmental properties to be the type of clay and therefore, the firing temperature; secondly, the Waelz slag content, being independent of the foundry sand addition; and finally, the type and level of processing (laboratory-pressing, semi-industrial extruder, industrial extruder).

Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning

“…Compared with DL algorithms that require a vast of data, one important advantage for SOMs is to conduct steady learning with relatively lower computational resources and calculation costs. Recent research examples of clustering, visualization, recognition, classification, and analyses using SOMs comprise medical system applications [ 28 , 29 , 30 , 31 , 32 ], social infrastructure maintenance [ 33 , 34 , 35 , 36 , 37 , 38 ], consumer products and services [ 39 , 40 , 41 , 42 , 43 ], food and smart farming [ 44 , 45 , 46 ], and recycling and environmental applications [ 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. We employed SOMs and their variants for the task of classification and visualization of mood states.…”