Machine learning-based prediction of a BOS reactor performance from operating parameters

A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors. View Full-Tex

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking

In recent years, improving the sustainability of the steel industry and reducing its CO2 emissions has become a global focus. To achieve this goal, further process optimization in terms of energy and resource efficiency and the development of new processes and process routes are necessary. Modeling and simulation have established themselves as invaluable sources of information for otherwise unknown process parameters and as an alternative to plant trials that involves lower costs, risks, and time. Models also open up new possibilities for model-based control of metallurgical processes. This Special Issue focuses on recent advances in the modeling and simulation of unit processes in iron and steelmaking. It includes reviews on the fundamentals of modeling and simulation of metallurgical processes, as well as contributions from the areas of iron reduction/ironmaking, steelmaking via the primary and secondary route, and continuous casting

Process Modeling in Pyrometallurgical Engineering

The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions

Enhancing Future Skills and Entrepreneurship

This open access book presents the proceedings of the 3rd Indo-German Conference on Sustainability in Engineering held at Birla Institute of Technology and Science, Pilani, India, on September 16–17, 2019. Intended to foster the synergies between research and education, the conference is one of the joint activities of the BITS Pilani and TU Braunschweig conducted under the auspices of Indo-German Center for Sustainable Manufacturing, established in 2009. The book is divided into three sections: engineering, education and entrepreneurship, covering a range of topics, such as renewable energy forecasting, design & simulation, Industry 4.0, and soft & intelligent sensors for energy efficiency. It also includes case studies on lean and green manufacturing, and life cycle analysis of ceramic products, as well as papers on teaching/learning methods based on the use of learning factories to improve students’problem-solving and personal skills. Moreover, the book discusses high-tech ideas to help the large number of unemployed engineering graduates looking for jobs become tech entrepreneurs. Given its broad scope, it will appeal to academics and industry professionals alike

Sustainable limestone and EAF aggregate concretes through particle packing models (PPMs) and life cycle assessment (LCA)

V.I. 226p. V.II 131 p.In view of the current concern about environmental problems, the use of slags from the Electric Arc Furnace (EAF) as aggregates in the concrete has been proved to be successful for multiple applications avoiding the use of natural aggregates. Hence, the range of aggregates available for designing concretes is continuously growing.The main objective of this thesis is to design economic and environmentally sustainable concrete mixes made with natural limestone (NL) aggregates and electric arc furnace (EAF) aggregate through a particle packing density perspective without compromising their compressive strength and workability.In order to verify the potential of particle packing theories to design more economical and environmentally sustainable NL aggregate and EAF aggregate concrete mixes, two traditional optimal curves and two current discrete packing models were validated with experimental packing results to demonstrate its feasibility in the prediction of the most compacted structure. Several (17) NL and EAF aggregate concrete mixes were then designed by varying the aggregate proportion and the content of cement paste to analyse the effect of aggregate packing density on the fresh and hardened concrete properties. Finally, the economic and environmental impact of the different concrete mixes were assessed to evaluate the potential of the particle packing methods in the development of more sustainable concrete.It was concluded that the concrete mixtures designed by maximizing the coarse aggregates content in the range of the maximum packing density present the highest compressive strength and workability and the low environmental and economic impact. In addition, due to the higher compressive strength and the low contribution of aggregate in the concrete environmental impact, the EAF aggregate concrete contributes to a greater reduction of the environmental and economic impact.Tecnali

The Journal of Microelectronic Research

https://scholarworks.rit.edu/meec_archive/1015/thumbnail.jp

Annual Report 2019 of the Institute for Nuclear and Energy Technologies (KIT Scientific Reports ; 7759)

The annual report of the Institute for Nuclear and Energy Technologies of KIT summarizes its research activities and provides some highlights of each working group, like thermal-hydraulic analyses for fusion reactors, accident analyses for light water reactors, and research on innovative energy technologies: liquid metal technologies for energy conversion, hydrogen technologies and geothermal power plants. The institute has been engaged in education and training in energy technologies

Doctor of Philosophy

dissertationDigital image processing has wide ranging applications in combustion research. The analysis of digital images is used in practically every scale of studying combustion phenomena from the scale of individual atoms to diagnosing and controlling large-scale combustors. Digital image processing is one of the fastest-growing scientific areas in the world today. From being able to reconstruct low-resolution grayscale images from transmitted signals, the capabilities have grown to enabling machines carrying out tasks that would normally require human vision, perception, and reasoning. Certain applications in combustion science benefit greatly from recent advances in image processing. Unfortunately, since the two fields - combustion and image processing research - stand relatively far from each other, the most recent results are often not known well enough in the areas where they may be applied with great benefits. This work aims to improve the accuracy and reliability of certain measurements in combustion science by selecting, adapting, and implementing the appropriate techniques originally developed in the image processing area. A number of specific applications were chosen that cover a wide range of physical scales of combustion phenomena, and specific image processing methodologies were proposed to improve or enable measurements in studying such phenomena. The selected applications include the description and quantification of combustion-derived carbon nanostructure, the three-dimensional optical diagnostics of combusting pulverized-coal particles and the optical flow velocimetry and quantitative radiation imaging of a pilot-scale oxy-coal flame. In the field of the structural analysis of soot, new structural parameters were derived and the extraction and fidelity of existing ones were improved. In the field of pulverized-coal combustion, the developed methodologies allow for studying the detailed mechanisms of particle combustion in three dimensions. At larger scales, the simultaneous measurement of flame velocity, spectral radiation, and pyrometric properties were realized