Challenges and Prospects of Steelmaking Towards the Year 2050

The world steel industry is strongly based on coal/coke in ironmaking, resulting in huge carbon dioxide emissions corresponding to approximately 7% of the total anthropogenic CO2 emissions. As the world is experiencing a period of imminent threat owing to climate change, the steel industry is also facing a tremendous challenge in next decades. This themed issue makes a survey on the current situation of steel production, energy consumption, and CO2 emissions, as well as cross-sections of the potential methods to decrease CO2 emissions in current processes via improved energy and materials efficiency, increasing recycling, utilizing alternative energy sources, and adopting CO2 capture and storage. The current state, problems and plans in the two biggest steel producing countries, China and India are introduced. Generally contemplating, incremental improvements in current processes play a key role in rapid mitigation of specific emissions, but finally they are insufficient when striving for carbon neutral production in the long run. Then hydrogen and electrification are the apparent solutions also to iron and steel production. The book gives a holistic overview of the current situation and challenges, and an inclusive compilation of the potential technologies and solutions for the global CO2 emissions problem

Soft sensor development using artificial intelligence and statistical multivariate methods

The lack of real-time measurement of certain critical product and process characteristics is a major problem in the manufacturing industry, and it can lead to an out of specification production. A soft sensor is a predictive model that uses readily available process measurements to infer variables that are impossible or difficult to obtain in real-time. In this work, historical process data related to the black liquor recovery circuit from a Canadian kraft pulp and paper mill is used to develop soft sensor models for the black liquor solid content at the concentrator feed. Prior to modeling, irrelevant variables and observations not representative of a normal operating regime are eliminated from the dataset. For practical reasons related to modeling restrictions and soft sensor industrial implementation, is proposed that a limited number of variables be used as model inputs. Two Partial Least Squares-based selection criteria are used to select the most relevant predictors. Two different sets of ten variables are obtained and used to develop Sugeno-type fuzzy logic, neural network and Partial Least Regression models. Their predictive performance is compared in order to determine the best model configuration and input selection method. iii Currently, the black liquor solid content at the concentrator feed is measured once every eight hours, by performing a laboratory analysis. The proposed soft sensor model can be used to provide a real-time value of the solid content, allowing operators to monitor the process and act timely if corrective actions are required

HR Selection Distortions: A theoretical framework for the Fiji Public Service

Despite being frequently perceived as a pertinent issue necessary to critically examine how incumbents are selected on merit, HR selection distortions is typically illdefined and poorly explained in much debate, hence, more precision in terms of contextualization of practice is needed. Through explaining and synthesizing the work of a number of scholars from different disciplines, the paper develops a theoretical framework for a meta- analysis, which begins with an exploration of the relationship between HR selection, networking and relational ties, employee’s justice perceptions, group heterogeneity and worker performance in Fiji’s public service institutions. The theoretical framework provides the leeway for the research questions to be answerable and the postulated hypotheses testable. However, more needs to be done to explain not only the nature and emergence of HR selection distortions but also the very real problems it faces in sustaining itself, let alone transforming the hiring processes in Fiji’s public service. The value of the paper lies in its theoretical innovation, drawing on a range of disciplines, and its attempt to situate HR selection distortions precisely, conceptually, theoretically, and practically

A Study on Utilization of LD Slag in Erosion Resistant Coatings and Polymer Composites

Although a variety of metal and ceramic powders are used as coating material and as reinforcing fillers in polymeric resins, the use of industrial wastes for this purpose has not been adequately explored. In view of this, the present investigation reports on the development and performance of new class es of plasma sprayed coatings and polymer composites using Linz-Donawitz slag (LDS) as the primary material. This LDS is a major solid waste generated in huge quantities during steel making and its chemical analysis suggests the presence of oxides of silicon, calcium and iron in it. The research reported in this thesis broadly consists of two parts: The first part has provided the description of the materials used, the experimental details and the methods adopted for analysis of experimental results. This part has also presented various physical and mechanical characteristics of the plasma sprayed LDS based coatings and LDS filled epoxy and polypropylene composites. An assessment of LDS as a potential coating material and a particulate filler in polymers has been made by evaluating the physical and mechanical properties of these coatings and composites under controlled laboratory conditions. Effects of premixing of Al 2O3 and TiO 2 powders on the physical and mechanical properties of LD slag coatings have also been reported. The second part of the thesis reports on the erosion wear characteristics of these coatings and composites. The wear response of LDS, ‘LDS + Al 2O3 ’ and ‘LDS + TiO 2 ’ coatings have been discussed separately. Comparisons between the erosion characteristics of LD slag filled epoxy and polypropylene composites with and without glass fiber reinforcement have also been presented. Parametric analysis and wear response prediction has been made for all the coatings and composites under this study using statistical techniques namely Taguchi experimental design and artificial neural networks (ANN). Correlations have been developed to predict the wear rate for these coatings and composites under different test conditions. x This work suggests that LDS is eminently coatable and deposition of such coatings using plasma spraying route is possible. These coatings possess desirable characteristics such as good adhesion strength, hardness etc. This work further establishes that LD slag can also be used as a functional filler in both thermoset and thermoplastic polymers. These LD slag filled composites possess very low amount of porosity and improved micro-hardness. They also exhibit improved impact strength as compared to that of the neat polymers. The tensile and flexural strength of the composites are affected by the weight fraction of LDS in the composites. With improved hardness, these composites have the potential to be used in wear related applications. Erosion wear characteristics of LDS coatings and LDS filled polymer composites have been successfully analyzed using Taguchi technique. Significant factors affecting the erosion rate of these coatings and composites are identified through successful implementation of this technique. Two predictive models; one based on artificial neural networks (ANN) approach and the other on Taguchi approach are proposed in this work. It is demonstrated that these models well reflect the effects of various factors on the wear loss and their predictive results are consistent with the experimental observations. Neural computation is successfully applied in this investigation to predict and simulate the wear response of these coatings and composites under various test conditions within and beyond the experimental domain. The predicted and the experimental values of erosion wear rate exhibit good agreement and validate the remarkable capability of a well-trained neural network for these kinds of processes

Dynamic simulation of red mud washers used in aluminum industries

Clarifier-Thickener equipment is used in a wide range of continuous sedimentation and sludge thickening processes where solid particles from continuous inflow mixtures are separated from the liquid. In this operation, the concentration of solids increases due to settling, so that the formation of a thicker bed is inevitable with time. Under optimal operating conditions, it is always possible to obtain two discharges from these vessels: a highly concentrated suspension at the bottom (underflow), and a clarified liquid stream at the top of the equipment (overflow or effluent). In the Bayer Process an insoluble sub-product is formed as a result of the digestion of the bauxite ore with caustic soda. This product is called “red mud” and it has to be continuously removed by settlers or thickeners/clarifiers. This project proposes the simulation of a continuous thickener/clarifier in order to predict the concentration profile and the height of the mud level (process controlled variable) as alternative to current measurement system, that contains long delay discrete sampling time(15 minutes each measurement). The simulation also, can provide an option of creating a knowledge base for off-line control. The project essentially involves two methods of simulation, namely mathematical modelling and neural networks. The model based applies a kinematic model to approximate the process behaviour using both, equipment and suspension characteristics. On the other hand, due to the large amount of historical data, neural network is proposed for system identification. The first method is based on the solution of a highly nonlinear model, based on kinematic modelling of sedimentation extended to flocculated suspensions. This approach uses a conservative finite difference scheme of the upwind type for solving an initial boundary value problem (IBVP). The successful simulation of the equipment and further validation of the mathematical model are then achieved once the properties of the suspensions have been determined. These properties are related to flux batch settling and solid stress functions, whose parameters can be obtained experimentally.On-site testing of the characteristics of red mud was conducted at the Rio Tinto Yarwum Alumina Refinery in Queensland. The settling properties of the suspension were determined via batch settling. For measurement of the rheology properties, the vane technique was used employing a Haake VT 550 rheometer. The results of the simulation showed that the concentration profile and height of the heavy mud level can be determined via a steady state model for a given underflow concentration. These results, however, were not in good agreement with measured data. The second method of simulation involved the use of Rio Tinto Yarwun historical data to develop a neural model in order to obtain a relationship between process variables. This approach has the advantage that no mathematical model is needed. With this method, historical data (continuous data) are obtained and analysed, and daily averages of the variables involved in the process are calculated. Different network architectures are tested according to the washer process. Ultimately, two networks were developed to describe washer dynamics.

Development and integration of environmental evaluation tools for the ecodesign of sustainable processes and products

Industry is recognized as one of the main sources of environmental pollution and resource depletion, both causing environmental degradation; nonetheless, its contribution to development and wealth creation is also acknowledged. Therefore, the identification of sustainable options in this area is a key factor. Nowadays, the attitude towards pollution prevention and control and cleaner production is not just a response to emerging environmental laws and regulations (Registration, Evaluation, Authorization and Restriction of Chemicals -REACH-, Integrated Pollution Prevention and Control –IPPC- Law, Integrated Product Policy –IPP-), but also a matter of corporate responsibility. Further, it has proved to be a way to increase profits. The sustainability definition has received certain criticism for its vagueness, ambiguity and difficulty to translate this concept at different levels. To overcome the difficulties of its implementation, a wide variety of indicators have been developed and applied over the years, providing metrics essential at the action level. This thesis poses a contribution to the development of environmental evaluation tools adapted to particular production sectors, aiming at providing metrics to guide decision making for the ecodesign of sustainable processes and products. Integrative frameworks that combine methodologies of different nature were proposed as the most suitable way to achieve comprehensive evaluations. At the same time, the simplicity of tools was pursued to make its application easier and more attractive for enterprises, avoiding the need of in depth training

Advances in raw material industries for sustainable development goals

"""Advances in Raw Material Industries for Sustainable Development Goals"" presents the results of joint scientific research conducted in the context of the Russian-German Raw Materials Forum. Today Russia and Germany are exploring various forms of cooperation in the field of mining, geology, mineralogy, mechanical engineering and energy. Russia and Germany are equally interested in expanding cooperation and modernizing the economy in terms of sustainable development. The main theme of this article collection is connected with existing business ventures and ideas from both Russia and Germany. In this book the authors regard complex processes in mining industry from various points of view, including: - modern technologies in prospecting, exploration and development of mineral resources - progressive methods of natural and industrial mineral raw materials processing - energy technologies and digital technologies for sustainable development - cutting-edge technologies and innovations in the oil and gas industry. Working with young researchers, supporting their individual professional development and creating conditions for their mobility and scientific cooperation are essential parts of Russian-German Raw Materials Forum founded in Dresden 13 years ago. This collection represents both willingness of young researchers to be involved in large-scale international projects like Russian-German Raw Material Forum and the results of their long and thorough work in the promising areas of cooperation between Russia and Germany.

Improved cement quality and grinding efficiency by means of closed mill circuit modeling

Grinding of clinker is the last and most energy-consuming stage of the cement manufacturing process, drawing on average 40% of the total energy required to produce one ton of cement. During this stage, the clinker particles are substantially reduced in size to generate a certain level of fineness as it has a direct influence on such performance characteristics of the final product as rate of hydration, water demand, strength development, and other. The grinding objectives tying together the energy and fineness requirements were formulated based on a review of the state of the art of clinker grinding and numerical simulation employing the Markov chain theory. The literature survey revealed that not only the specific surface of the final product, but also the shape of its particle size distribution (PSD) is responsible for the cement performance characteristics. While it is feasible to engineer the desired PSD in the laboratory, the process-specific recommendations on how to generate the desired PSD in the industrial mill are not available. Based on a population balance principle and stochastic representation of the particle movement within the grinding system, the Markov chain model for the circuit consisting of a tube ball mill and a high efficiency separator was introduced through the matrices of grinding and classification. The grinding matrix was calculated using the selection and breakage functions, whereas the classification matrix was defined from the Tromp curve of the separator. The results of field experiments carried out at a pilot cement plant were used to identify the model's parameters. The retrospective process data pertaining to the operation of the pilot grinding circuit was employed to validate the model and define the process constraints. Through numerical simulation, the relationships between the controlled (fresh feed rate; separator cut size) and observed (fineness characteristics of cement; production rate; specific energy consumption) parameters of the circuit were defined. The analysis of the simulation results allowed formulation of the process control procedures with the objectives of decreasing the specific energy consumption of the mill, maintaining the targeted specific surface area of the final product, and governing the shape of its PSD