In the discussion on environmental policies, the notion of eco-efficiency is often used. Eco-efficiency is defined as the delivery of products and services with competitive values, while reducing the ecological impacts and satisfying human needs. In an environment of great competitiveness in which the companies operate, the improvements in the productive process is one of the differentiating factors for guaranteeing a strong competition. Also it is essential rationalizing energy consumption and natural resources. For this it is necessary to understand the main operations and dynamics of the company. This work presents an empirical study of a Portuguese company in the industrial sector. The problematic here presented is based on the company’s growing concern to reduce the amount of scrap produced. From the literature research that was performed, a strong dependence relation between the different variables in the extrusion process was found. The main objective of this work is to model the aluminium extrusion process, with the aim of minimizing the production of scrap. For this several variables involved in the process are taking into account. Using statistical techniques, in particular multiple linear regressions, it was possible to identify the importance of the variables under study for the scrap production.

The dynamic global automotive sector is characterized by an ever-evolving demand for high-quality vehicles and accessible prices. This research tackles the issue of scrap reduction in electric motor manufacturing for the automotive industry, specifically in a Chinese company, by employing the PDCA methodology. The paper delves into the company's background and current landscape, while pinpointing the problem: specific production lines segments experiencing hi-pot issues are the primary source of scrap. The overarching goal is to design and implement a strategy that, leveraging the PDCA approach, reduces scrap generated during the eight-months subassembly (armature) manufacturing process, from its current average of 2.92% to 0.7%, ultimately enabling the company to eliminate hi-pot defects.

The recycling of wastes to obtain value-added products is a great challenge in the sustainable economy. In our studies, a mixed precursor containing aluminium scrap from end – of – life vehicles and TiO2 Degussa P25 was used to prepare TiO2 modified Al photocatalyst through solid state reaction. Al scrap and TiO2 Degussa P25 in the ration of n(Ti) / n(Al) = x (x = 0.0 - 2.5%), and acetylacetonate and Tween 80 was mixed under magnetic stirring and sintered using a con-ventional furnance at 550°C. DTA – TG – DTG, X – ray diffraction, Fourier – transform infrared, UV – Vis and photoluminescence spectroscopies were assed to investigate the thermal, structural, optical properties and photocatalytic activity. The effect of aluminium concentration on the characteristics of TiO2 Degussa P25 depending on the above instruments are studied. XRD revealed that the materials have crystal structure of anatase TiO2, rutile TiO2 and aluminium. The opto-electronic properties show that Al addition shifted the absorption spectra toward the visible range. The photocatalytic performance of Al – TiO2 composites was quantified by the degradation of Allura Red solution under a visible condition. The highest photocatalytic activity (79.99%) was achieved for the sample containing 0.5% Al and a higher concentration of Al particles shields the active centres of the semiconductor, thus reducing its photocatalytic activity. Aluminium scrap from end-of-life vehicles can be a source of raw material to be considered in semiconductor production. The spin-trapping technique evidenced that •O2- was the main species generated responsible for the Allura Red degradation.

Growing evidence about the negative socio-environmental consequences of plastic pollution led to various initiatives for better plastic scrap governance and its trade. At the same time, an examination of recent data shows that plastic scrap represents only a fraction of recyclable materials which are traded internationally and are also subject to similar problems of cross-border environmental governance. A limited analysis comparing plastics, textiles, paper and ferrous metals suggests that ongoing momentum for improvement of plastic scrap governance and circularity should also be framed to consider other types of secondary materiais.

In this article, it is proposed to use aluminum breakage scraps to obtain Green Hydrogen through the aluminum-water reaction with caustic soda as a catalyst. From this exothermic reaction, both hydrogen and the heat generated can be used. Due to the low price of aluminum chips, this allows us to produce Green hydrogen below the current price that is obtained using renewable energy sources and electrolyzers. On the other hand, we have developed a process that is sustainable since it is obtained as alumina and caustic soda waste that can be reused. This alumina obtained, once filtered, has high purity which allows us to produce high-quality primary aluminum without the need to use bauxite and reducing the production of red sludge. A comparative study-analysis has been carried out of two of the types of forms in which the most common aluminum is presented in the industry to analyze which one performs better by studying key factors such as the hydrogen produced, and the waste generated during the process. Finally, the mathematical model has been defined to be able to control the flow based on different key parameters such as temperature, molarity, and geometry. Undoubtedly, the study that we present represents a milestone for the recovery of metallic aluminum waste and may be of great interest to industries that use aluminum in their processes, such as recuperators, but also the vehicle or aerospace industry.

The issues of metallic scrap management and its utilization in manufacturing plants are nowadays intensely considered to address essential sustainability guidelines. Efficient recycling procedure for shop floor metallic scrap is not yet available because of abundance and contamination of nonmetallic constituents. Other ferrous metallic scrap are melted and purified during secondary steelmaking to get products in the form of blooms and billets are obtained. This study illustrates the potential of powder technology (powder metallurgy (PM) and metal injection molding (MIM)) based process for solid-state recycling and attainment of usable products. Industrially downgraded grinding sludge is pulverized and used as a raw material. Results showed properties of sintered parts are significantly improved due to in-situ reduction and densification during sintering. Recyclability Index (RI) was created to compare the effect of process variables on obtained products. Based on RI, recycled ferrous parts have about 70% comparable properties with equivalent pure iron parts. Complex reduction and sintering behavior in MIM, particularly, diffusion and pore volume kinetics limits applicability of MIM with this recycling approach. However, few industrial parts were developed and manufactured by PM based approach to validate the applicability of this novel recycling-cum-manufacturing process for the production of porous parts.

In the field of metallurgy, there are many factors that influence the final quality of cast iron. These are mainly charge materials, chemical composition, metallurgical preparation up to the final casting process. Even small deviations from metallurgical processing lead to fluctuations in melt quality and the occurrence of casting defects. Charge materials has a significant impact on the quality of cast iron, especially steel scrap, which is increasingly used from an economic and ecological point of view, especially when melting cast iron in electric furnaces. Cast iron produced from a higher proportion of steel scrap in the charge has higher hardness and tensile strength. On the other side, these cast irons has a higher tendency to chillout, brittleness, shrinkage, pearlitic microstructure, stresses and higher purity due to the difference in hardness at different wall thicknesses of the castings. It is the high hardness that is the problem in the final machining of castings. These negative properties are recorded mainly in heavy thick-walled castings with higher tensile strength and hardness. This negative effect is mainly due to the nitrogen content in the steel scrap. Based on these knowledges, operating melts were realized in the foundry's operating conditions. The influence of steel scrap in the charge and the possibility of eliminating its negative effects on the properties of cast iron were investigated.

Associating the most influential parameters with the product distribution is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). These correlations can lead to the information-driven catalyst screening, kinetic modeling and reactor design. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, has been obtained in an FCC simulator using 6 types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and 3 industrial catalysts. Principal component analysis (PCA) has been applied over the dataset, showing that the main components are associated with feed composition (27.41% variance); operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component have been correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others.

The production of lithium-ion battery cells is characterized by a high degree of complexit due to numerous cause-effect relationships between process characteristics. Knowledge about the multi-stage production is spread among several experts, rendering tasks such as failure analysis challenging. In this paper, a method is presented, which includes expert knowledge acquisition in production ramp-up by combining Failure Mode and Effects Analysis (FMEA) with a Bayesian Network. We show the effectiveness of this holistic method by building up a large scale, cross-process Bayesian Failure Network in lithium-ion battery production. Using this model, we are able to conduct root cause analyses as well as analyses of failure propagation. The former support operators in identifying root causes once a cell possesses a specific failure by calculating most-probable explanations matched to the individual battery cell data. The latter enable us to analyze propagation of failures and deviations in the production chain and thus provide support for placement of quality gates, leading to a significant reduction in scrap rate. Moreover, it gives an insight into which process steps are key drivers for which final product characteristics.