Big Data is related to data sets which exceed the capacity of traditional data processing systems and enforces significant changes in the data analysis approach. Changes in the analysis of Big Data relate to: the ability to analyze large amounts of data, readiness to deal with unstructured data, and rising importance of correlations. Big Data Analytics (BDA) may show new relations between data, reveal unseen earlier trends and contribute to the creation of new knowledge, which can then be used to create value. The aim of this paper is to identify the areas of metal enterprise areas where bid data analytics can be effectively used and to formulate recommendations for the effective implementation of big data analytics projects. The aim was achieved using the desk research methodology and interviews with managers of large metal enterprises. The results of the study justified the efficiency of the use of big data analytics (BDA) in management of metal enterprises in the areas of customer relationship management, creation of value, creation of innovations and production management.
One of the aspects defining the paradigm sometimes called Industry 4.0 is the availability of large amounts of data generated by modern processes. Nowadays, most process parameters can be collected automatically and therefore, the large volume of process data generated becomes close to impossible to analyse manually, or at least, to be able to extract from it all the useful information that it contains.
Advanced mathematical and computational tools to analyse data and create models automatically have existed for some time, being labeled at one time or another as Expert Systems, Artificial Intelligence, Machine Learning, Data Mining, Statistical Pattern Recognition etc. However, only recently the combination of automated data collection and fast computers have allowed to extend the benefits of such techniques beyond some niche applications and industries.
This work introduces a new project to develop a comprehensive set of such tools aimed to fit industrial needs. As starter, several advanced model fitting techniques are applied to a metallurgical example. As dataset, a publicly available database on creep rupture is used. Supervised learning algorithms (using gradient descent training, normal equation regression, artificial neural networks), when correctly applied, allow to fit models to any set of complex data and to make reliable predictions with them. Unsupervised learning methods on the other hand, may also be used to find structure in the data without any a priori knowledge of such underlying structure (anomaly detection, clustering).
Management of non-renewable resources, including metals, is a key issue in the circular economy and one of the priorities of the EU's environmental policy. Life Cycle Assessment (LCA) is a technique for assessing the environmental impact in a product or technology life cycle which allows the estimation of metal, minerals and fossil fuels consumption, including both direct demand for them in the technological processes as well as indirect demand for them in the production of raw materials, materials and energy used in these processes. The analysis results of cumulative metal consumption in wastewater treatment system based on life cycle analysis are presented in this paper. In addition to economic issues, the use of methods to assess environmental performance at the stage of construction, operation and decommissioning of water and wastewater infrastructure is becoming more important in the wastewater economy. In this context, a new approach to the assessment of environmental aspects is the use of a life cycle analysis concept that allows the assessment of cumulative resource demands throughout the whole life cycle of a wastewater treatment system. The Life Cycle Impact Assessment (LCIA) of wastewater treatment system proposed in this paper allows the identification and assessment of its potential environmental impacts from the perspective of metal consumption. It has been shown that the LCA technique is an appropriate tool for assessing the cumulative metal consumption.
The paper deals with the evaluation of experimental data on samples of steel wire. The main attention is aimed at the R&R study, which was a part of the experiment for studying effects of three factors on various mechanical properties of drawn wires. The experiment was performed in the wire drawing laboratory at VŠB-Technical University of Ostrava. Samples of wire were tested for tensile, torsion and bending fatigue. Several operators took part in the experiment. Due to the destructive character of tests, a nested design was considered. The R&R study revealed that the mechanical properties along the wire length were not uniform and that the key assumption of the destructive-test analysis was not met. Since the employed scheme of assigning drawn wires to operators led to confounding of effects and made the further analysis dubious, another arrangement for this type of experiment is suggested in the paper. Apart from the R&R analysis, statistical methods of evaluation that take the real experimental pattern into account are discussed. The method based on means is applied and significant effects are identified.
Industrial processes use large quantities of fuel and electricity that ultimately produce heat, much of which is wasted to the atmosphere. Re-using of this waste energy within manufacturing process is the optimal solution. For example preheating of the combustion air and gas reduce energy intensity of the production process is optimal solution. This contribution describes efficient heat recovery of fumes from electric arc furnace (EAF) in a steel mill in Brescia (Italy). A large scale pilot plant has been built and commissioned for production of electricity (technology ORC) and heat for central heating system of the town. The economic evaluation of return on investment for technology ORC is involved into this contribution.
This article is supposed to take into account the usage of business intelligence tools for metallurgical production control systems. Artificial intelligence tools, not only the popular method of data mining, are currently necessary for the control systems, and they are supposed to be used also for any heavy industry, including metallurgy. It is expected that the ratio of usage of fuzzy sets, expert systems and other available means of artificial intelligence, which include also artificial neural networks, will be significantly increased also for metallurgical processes wherever it is feasible and economically acceptable. This article is focused to Neural Networks and their usage for Control of Heating Furnace.
Homogenization yard is used for holding dust ore and ore concentrates. The raw materials for agglomeration are transport to the industry premises usually by railway and with help of spreader machine are stored on so called homogenization yards. The goal of this article is not the description of homogenization yard function, but describe proposed method to estimate its actual proportions and capacity. Proposed system usage several cameras, which convert real image of homogenization yard to its digital form. Used algorithm is then capable to estimate its proportions, shape and capacity. These informations are accessible ONLINE, so there are only a few seconds difference concerning real state. Up to date informations can increase effectiveness of logistic processes concerning blast furnace raw material base control and also homogenization yards loading.
In recent years many factors have led to the need to change the approach to management of the modern metallurgical enterprise. The most important of these are the increasing differences between the dynamics of the development of metallurgical companies in the European Union countries and countries known as developing countries (e.g. China, India, etc.). In addition, the recent economic crisis has shown that corporate governance philosophy based primarily on financial ratios is insufficient and does not ensure its long-term existence in the market and future development. So new concepts are sought, which aim at dynamic growth of the competitiveness of European companies. One of the new propositions is the concept of the industry 4.0, which primarily uses web development, technical progress, and is based in a sense on the cooperation of various enterprise which build production networks.
The paper outlines the main assumptions of the Industry 4.0 concept for metallurgical companies and identifies new challenges in the management of a metallurgical enterprise in line with the industry 4.0. The main goal of the article is to present a new solution called a management map of the Industry 4.0, which makes changes in the company much easier and more effective conducted and prepared them for the practical application of the Industry 4.0 concept.
The role of innovation and innovativeness is crucial for every kind of organization. Changes in the implementation and absorption of innovation could be a way to for organizational and financial success of a company. Nowadays, it is important to realize that both processes have to be implemented for a company to remain competitive. This paper the author describes innovation management in metallurgical companies and its importance. The impact of innovation on the global economy is the main goal of presented investigation. Research is based on the literature review and data analysis for the years 2007-2016. This allows for predictions and to propose the theoretical model that can be verified in real existing organizations.
Managers of metal companies recognize the importance of continuous product development and engineering. Value chain integration, product platforms, digital business models and marketing are still less noticeable in metal companies but that may change fast as customers adopt new concepts. Managers have to respond to those changes with strategic initiatives, which are difficult to measure and control. I analyze changes in research and development expenditures focusing on industrial metals and mining companies. The main result of the paper is the recognition of recent changes in R&D spending indicating the decreasing importance of in-house corporate R&D initiatives.
In this paper the finite element method approach was presented in order to determine the effective stress redistribution and displacements in a simple beam to beam end plate connection with high tensile bolts at different values of tightening torque. Mentioned beam to beam connection was represented via HEA profile with both end plates, bolted to the I-shape short cantilevers welded to the supporting columns. Assuming that the building is foreseen to be a warehouse and the beam is located in ceiling, live load with value of 5.5 kN/m2 was adopted. Moreover, it was assumed that analysed elements were made of S235JR steel class. Three different bolt classes were taken into considerations i.e. 8.8, 10.9 and 12.9. The thickness of end plates for each bolt class were determined individually on the basis of bolt tensile strength and the bolt pin diameter according to the polish national standard. Additionally, four different values of tightening torque for each bolt class were investigated i.e. 0.25, 0.50, 0.75 and 1.00 of maximum acceptable torque. Through the analysis it was shown that the higher tightening torque decreases overall displacements and plates connection split while values of effective stress in the connection can be significantly increased. Stated boundary problem was solved with the utilization of SolidWorks software.
Optimal control of heating furnaces requires temperature-thermal mode correction with respect to actual heating conditions. Conditions are by far the most influenced by irregularities during furnace heating material withdrawal. Time flow corrections settings of required temperature values in single furnace zones is possible to optimize based on heating models prediction. Furnace temperature values play the most important role during heating simulations. However, the influence of an instant heating gas flow in nozzles within singe parts of a furnace cannot be underestimated. The article shows the way to define time trajectories of actuating variables and identification process itself, based on the identification and consequent simulation. Achieved results should be exploited for various heating ways generating, followed by optimal alternative choice.
The article deals with the results of the partial cost analysis realized by using the tools of the regression analysis and artificial intelligence tools - particularly genetic algorithms – in the area of metallurgy. The aim of these activities is to create a relationship (a formula) that would describe the dependencies of individual cost items. In this particular case was searched multiple correlation of gas consumption per cycle on the weight of the charge, the temperature of the delays and the length of the cycle.
On a statistical sample was performed regression analysis. On the basis of the coefficients of the regression function of three variables (weight of charge, lag temperature and cycle length) were calculated the corresponding values of estimated gas consumption and the value of the residue.
On the same data were subsequently applied artificial intelligence tools – in particular, genetic algorithms - and the results obtained from conventional procedure (regression analysis) and obtained by using the tools of artificial intelligence (genetic algorithms) were compared.
Up-to-date labour market is characteristic by the lack of qualified candidates for all industries. There are ever less students of technical branches in all levels of education system and this fact negatively influences mainly manufacturing sectors. Czech foundries have had problems to find suitable employees already in times of higher unemployment rate when the labour market was offering more candidates so today is the lack of candidates for them really critical. Although foundries often offer higher than average salaries, mainly young people prefer physically less demanding work for lower salaries and their fluctuation has been significantly increasing. Authors of the paper have therefore focused on main problems of human resource management in the selected foundry with the aim to find, discuss and propose potential solutions of more effective employee recruitment and motivation.
Sustainability is widely accepted as one of the most important approaches, which allows to reach a long-term success in the metallurgical industry. Sustainability considers three fundamental dimensions: economic, environmental and social. Each dimension contains a relatively high number of specific topics. The aim of the paper is to identify key topics used by world-leading metallurgical companies and propose a methodology for determining the significance of individual topics and dimensions. The research work is based on the analysis of the metallurgical companies’ sustainability reports, categorisation of the sustainability specific topics according to Global Reporting Initiative standards and prioritizing those using Analytic Hierarchy Process multi-criteria decision making method.
The concept of supply chain resilience, due to its practical implications, is gaining an increasing attention of wide range of experts across various disciplines. So far, the main attention has been paid to the definition of resilience itself, its measurements, and methodological frameworks of building the resilience using simulation models. However, no analytical models for assessing different scenarios and costs of building the resilience of specific supply chains has been presented yet. This article aims to introduce, analyze and evaluate the possible use of modified analytical models of production lines to optimize investment directed to the supply chain to improve its overall resilience. For purposes of the article, example of the real structures of metallurgical supply chain was used. Discussed are the possibilities and limitations of the specific models and their practical implementation in the metallurgical supply chain management.
Not only do business conditions change with the continuous development of the market environment, but also the requirements for the evaluation of a company as well as the significance of individual evaluated parametres. The article presents a model determining the value of a metallurgical company based on the DCF method using the principles of structural analysis to objectively evaluate the impact of factors affecting the changes in cost potential in the nearest surroundings of the company.