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“2sDR” An innovative mini mill concept for EAF-dust recycling
Published in Cândida Vilarinho, Fernando Castro, Margarida Gonçalves, Ana Luísa Fernando, Wastes: Solutions, Treatments and Opportunities III, 2019
M. Auer, J. Antrekowitsch, G. Hanke
Steel mill dust out of the electric arc furnace (EAF) route nowadays represents a well-known secondary resource in zinc metallurgy. Although a lot of research and development has been carried out within the last decades, no relevant breakthrough regarding an innovative recycling technology could be established. Roughly 90% of the produced dust is treated via the waelz kiln technology. However, globally less than 50% of this high zinc containing dust is recycled. The reasons for this still unsatisfying rate are disadvantages like the recovery of only one metal, the contamination of the produced zinc oxide with halides and the huge amount of newly generated residues (Schneeberger et al. 2012).
Urbanization, industrialization, and noxious facilities
Published in Michael R. Greenberg, Siting Noxious Facilities, 2018
Input–output (I–O) models can help us understand forces of agglomeration and deglomeration and the multiplier impacts of businesses and policies. Created by Wassily Leontief50–52 who won the Nobel Prize in economics for his development of I–O, the real issue for those interested in industrial location theory is whether this tool would be useful for analysis at the local, county, state and regional scales. An input–output model is a table that shows the flow of transactions within the economy. If I want to build a mini-steel mill, I need to buy scrap iron ore and steel and have a cheap source of electric power. I will need a group to build and set up air pollution control systems to capture particles that otherwise will go into the atmosphere (see Chapter 3 for two examples). I will need access to rail, water and highways to move products.
Results of Investigative Studies on Various Industrial Refractory Systems
Published in A. Schacht Charles, Refractory Linings, 2017
An analysis was conducted on the spherical refractory dome shown in Figure 17.8. The refractory spherical dome described is part of a complex refractory lining system used in a blast furnace stove. The blast furnace is part of the equipment used in the production of pig iron in a steel mill. The stove is used to conserve heat from spent gas as well as to heat the fresh supply gas used in the blast furnace operation, which reduces iron ore to pig iron [5]. This portion of the steel mill is referred to as the ironmaking, or primary, portion of the steel mill. The steelmaking portion of the steel mill converts the pig iron to steel by adding alloys and removing impurities and other undesirable chemical components.
Cadmium and lead in rice grains and wheat breads in Isfahan (Iran) and human health risk assessment
Published in Human and Ecological Risk Assessment: An International Journal, 2019
Fahimeh Ghoreishy, Mahsa Salehi, Jaber Fallahzade
In central Iran, Isfahan is an important industrial region with high population (5.12 million). In recent years, industrial activities have increased extremely in Isfahan province, consequently this province has been heavily industrialized. Rapid and unorganized industrial developments and also use of wastewater irrigation and sewage sludge in agricultural lands have led to an increment in the level of pollution caused by heavy metals. The presence of several steel mill industries in this region has added large amounts of heavy metal contaminated wastewater and sewage sludge to the environment (Moradi et al. 2016a). In this region, increased concentrations of toxic heavy metals in cultivated soils and their uptake in food crops (wheat and rice) have become a severe human health risk (Moradi et al. 2016b). Recently, food contamination by toxic heavy metals has been considered as a serious problem due to their potential accumulation in biosystems through contaminated soil and water sources (Lokeshwari and Chandrappa 2006).
Dynamic multimode process monitoring using recursive GMM and KPCA in a hot rolling mill process
Published in Systems Science & Control Engineering, 2021
Gongzhuang Peng, Keke Huang, Hongwei Wang
After decades of application of advanced sensing, communication technology and distributed control system (DCS), most large steel mills have formed a five-level automation and information architecture that includes basic automation system, process control system, manufacturing execution system, manufacturing management system, and business decision-making system. The newly rising industrial internet of things (IIoT) and cyber physical system (CPS) technologies have further broken the barriers between different information systems and promoted the collection, fusion and storage of multi-source heterogeneous data (Cao et al., 2020; Han et al., 2020; Zhang et al., 2016). The IIoT platform and development of data science enable the widespread application of data-driven process monitoring methods (Nkonyana et al., 2019), in which normal operation conditions are modelled with historical process data and the state of the monitored process is then examined by evaluating the deviation of indicators (Jiang et al., 2019; Quiñones-Grueiro et al., 2019; Zhang et al., 2016). The most typical data modelling methods are based on feature extraction, such as principal component analysis (PCA) and independent component analysis (ICA) (Guo et al., 2019; Jiang et al., 2016; Jiang & Yan, 2018; Zhou et al., 2016). They extract the main features from the sample that reflect the normality or abnormality by dimensionality reduction. Another category is correlation-related methods, such as canonical correlation analysis (CCA) and partial least squares (PLS) (Jiang & Yan, 2019; Liu et al., 2017; Liu et al., 2018). Some machine learning and deep learning methods have also been applied in the process monitoring due to their powerful feature extraction capability in dealing with high-dimensional problems, such as autoencoder (AE) and its variations, and Bayesian networks (Huang et al., 2020; Lee et al., 2019; Song et al., 2020; Yu & Zhao, 2019).