The materials selection for Additive Manufacturing (AM) is highly dependent on the AM process that will process the material. It has been identified that there is a necessity for building robust materials selection methodologies according to the new Industry 4.0, since the volume and complexity of data have evolved rapidly, like e.g. in the development of additive manufacturing processes.
Chemistry is a key contributor to the SDG. “Sustainable Chemistry” provides solutions trough energy efficient processes (catalysis), sustainable chemicals, improved fossil fuels and biofuels, renewable energy storage, circular chemistry (plastics and wastes recycling or transformation), CO2 capture and reuse, etc. In many cases collaboration between universities and industry is important.
A theoretical analysis is developed that is postulated before the study of the feasibility that a polluting gas, such as SO2, could be removed by an absorption process and then used or valorized as a commercial product after a joint process of subsequent desorption (stripping) stage. This Absorption/Deabsorption set could thus constitute an industrial plant for the generation of a commercial product (e.g., enriched SO2 or sulfuric acid), environmentally coupled with a large coal combustion plant.
An Asset Health Index (AHI) is a tool that processes data about asset’s condition. That index is intended to explore if alterations can be generated in the health of the asset along its life cycle. These data can be obtained during the asset’s operation, but they can also come from other information sources such as geographical information systems, supplier’s reliability records, relevant external agent´s records, etc. The tool (AHI) provides an objective point of view in order to justify, for instance, the extension of an asset useful life, or in order to identify which assets from a fleet are candidates for an early replacement as a consequence of a premature aging.
The recent and remarkable use of Artificial Intelligence (AI) techniques, and particularly, of data mining, allows the improvement of industrial processes through pattern analysis. These tools become very useful when considering condition-based maintenance (CBM) processes, where it is necessary to detect the inflection point in normal operation conditions. In this paper, a novel methodology for CBM is proposed, consisting of 3 data mining techniques: Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Random Forests (RF). Initial analysis of the experiment outcomes suggests that it is recommended to continue the researching efforts in this field because of the improvement obtained in predictive maintenance.
This article reflects on the tools designed to ensure safety in industrial facilities, with the aim of reducing the likelihood of accidents and achieving the safety of people and the environment, as well as meeting the production and profitability requirements of their investments.