2,183 research outputs found

    Metallurgical Process Simulation and Optimization

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    Metallurgy involves the art and science of extracting metals from their ores and modifying the metals for use. With thousands of years of development, many interdisciplinary technologies have been introduced into this traditional and large-scale industry. In modern metallurgical practices, modelling and simulation are widely used to provide solutions in the areas of design, control, optimization, and visualization, and are becoming increasingly significant in the progress of digital transformation and intelligent metallurgy. This Special Issue (SI), entitled ā€œMetallurgical Process Simulation and Optimizationā€, has been organized as a platform to present the recent advances in the field of modelling and optimization of metallurgical processes, which covers the processes of electric/oxygen steel-making, secondary metallurgy, (continuous) casting, and processing. Eighteen articles have been included that concern various aspects of the topic

    A History of Materials and Technologies Development

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    The purpose of the book is to provide the students with the text that presents an introductory knowledge about the development of materials and technologies and includes the most commonly available information on human development. The idea of the publication has been generated referring to the materials taken from the organic and non-organic evolution of nature. The suggested texts might be found a purposeful tool for the University students proceeding with studying engineering due to the fact that all subjects in this particular field more or less have to cover the history and development of the studied object. It is expected that studying different materials and technologies will help the students with a better understanding of driving forces, positive and negative consequences of technological development, etc

    Contributions to predicting contaminant leaching from secondary materials used in roads

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    Slags, coal ashes, and other secondary materials can be used in road construction. Both traditional and secondary materials used in roads may contain contaminants that may leach and pollute the groundwater. The goal of this research was to further the understanding of leaching and transport of contaminants from pavement materials. Towards this goal, a new probabilistic framework was introduced which provided a structured guidance for selecting the appropriate model, incorporating uncertainty, variability, and expert opinion, and interpreting results for decision making. In addition to the framework, specific contributions were made in pavement and embankment hydrology and reactive transport, Bayesian statistics, and aqueous geochemistry of leaching. Contributions on water movement and reactive transport in highways included probabilistic prediction of leaching in an embankment, and scenario analyses of leaching and transport in pavements using HYDRUS2D, a contaminant fate and transport model. Water flow in a Minnesota highway embankment was replicated by Bayesian calibration of hydrological parameters against water content data. Extent of leaching of Cd from a coal fly ash was estimated. Two dimensional simulations of various scenarios showed that salts in the base layer of pavements are depleted within the first year whereas the metals may never reach the groundwater if the pavement is built on adsorbing soils. Aqueous concentrations immediately above the groundwater estimated for intact and damaged pavements can be used for regulators to determine the acceptability of various recycled materials. Contributions in the aqueous geochemistry of leaching included a new modeling approach for leaching of anions and cations from complex matrices such as weathered steel slag. The novelty of the method was its simultaneous inclusion of sorption and solubility controls for multiple analytes. The developed model showed that leaching of SO4, Cr, As, Si, Ca, Mg, and V were controlled by corresponding soluble solids. Leaching of Pb was controlled by Pb(VO4)3 solubility at low pHs and by surface precipitation reactions at high pHs. Leaching of Cd and Zn were controlled by surface complexation and surface precipitation, respectively

    Newsletter new technologies and innovation policy. Special Issue. September 1985. 46A.

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    Title changed to Innovation and technology transfer Newsletter in 198

    A Literature Review of Fault Diagnosis Based on Ensemble Learning

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    The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

    Interated Intelligent Industrial Process Sensing and Control: Applied to and Demonstrated on Cupola Furnaces

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    Data-driven Soft Sensors in the Process Industry

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    In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

    Annual Report 2018-2019

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    It contains the statement of R&D works undertaken, achievement made and the expenditure by the laboratory during the financial year 2018-2019
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