The development of a process charge expert system for a Basic Oxygen Steelmaking plant.

In an integrated steelworks the Basic Oxygen Steelmaking (BOS) process is required for the refining of molten iron from a blast furnace to produce steel. The development of an expert diagnostic system is considered in the context of the initial phase of BOS operation, the loading and operation of the BOS vessel. In this part of the steelmaking process the BOS vessel is charged with the molten iron, scrap metal and fluxes which are there to facilitate the capture of impurities by forming slag. The nature of the elements added requires knowledge of the steelmaking process, the actual state of the contents of the vessel and the available process management options. The expert system produced to oversee this process exhibits the capability of dealing with both continuous and batch data, combining the two together to aid effective decision making and management. Fuzzy inference is used in the main diagnostic system due to the large rule base required to diagnose faults and infer a process state. The operation of the system and its use by the process operators and the application of this approach into other areas of the steelworks is considered in this paper

Scour detection with monitoring methods and machine learning algorithms - a critical review

Foundation scour is a widespread reason for the collapse of bridges worldwide. However, assessing bridges is a complex task, which requires a comprehensive understanding of the phenomenon. This literature review first presents recent scour detection techniques and approaches. Direct and indirect monitoring and machine learning algorithm-based studies are investigated in detail in the following sections. The approaches, models, characteristics of data, and other input properties are outlined. The outcomes are given with their advantages and limitations. Finally, assessments are provided at the synthesis of the research.This research was funded by FCT (Portuguese national funding agency for science, research, and technology)/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020 and trough the doctoral Grant 2021.06162.BD. This work has also been partly financed within the European Horizon 2020 Joint Technology Initiative Shift2Rail through contract no. 101012456 (IN2TRACK3)

Çelikhane (BOF) tesisinde yapay sinir ağı (ANN) uygulamaları ile karbon (C) ve fosfor (P) tahmini

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Günümüz teknolojinde ürün kalitesinin sürekliliği ve verimli çalışma için süreçlerin nihai durumunu tahmin eden modeller geliştirilmekte ve kullanılmaktadır. Üzerinde çalışılan sistemin detayı ve tüm çalışma prensipleri bilindiğinde modeller daha güçlü yanlışsız ve kesin olmaktadır. Çelik Üretim gibi sürecin bilinmesine rağmen modellemenin zorlayıcı olduğu süreçlerde bulunmaktadır. Yüksek Fırın ve Basic Oxygen Furnace (BOF) gibi tesislerdeki sıvı, metal ve gaz tepkimelerinin yer aldığı ve nihai ürünün durumunu etkileyen birçok girdinin olduğu zorlu süreçlerde modelleme yapmak güçleşmektedir. Bu çalışmamızda BOF konvertöründe üfleme sonu TSO ve üfleme içi TSC probu ile ölçüm yapma anlarındaki fosfor ve karbon değerlerinin tahmini için Matlab programı ile öğrenme algoritması Scaled Conjugate Gradient olan geri yayılımlı çok katmanlı sinir ağı önerilmiştir. Giriş verisinin rastgeleliğinin ve doğruluğunun sağlanması için tek bir konvertörden tesis bazlı metalürjik etkileşimler, konvertörün alttan karıştırma durumu göz önüne alınarak veri seçimi ve homojen bir öğreneme ortamı için10 Fold cross Validation tekniği kullanımı sağlanmıştır. Yapay sinir ağı modeli sonuçlarımızda TSC anı tahminlerde ±0,02 hata aralığında %83 tutarlılıkla fosfor, ±0,15 hata aralığında %93 tutarlılıkla karbon değeri gözlemlenmiştir. TSO anı tahminlerinde ise ±0,025 hata aralığında %89.4 tutarlılıkla fosfor, ±0,01 hata aralığında %92 tutarlılıkla karbon değeri gözlemlenmiştir.In today's technology, models are commonly developed and applied to predict and control the end point of any processes, due to obtaining sustainable product quality. The power of model and it's usage will be more precise and accurate in case when the system is explained in detail and defined completely. Nevertheless, during the steelmaking process there are several plants that modelling becomes challenging. Blast Furnace (BF) and Converter process are the most difficult processes that can be modelled due to liquid, metal and gas reactions and a large number of input variables that can influence reaching the end point. In this study, Feed Forward Back Propagation Multi-Layer Neural Network in Matlab with training function Trainscg is proposed for prediction of the phosphorus and carbon at blowing end (TSO phase) and inblow (TSC phase ,%80 of blowing time) in BOF Converter. In order to ensuring and validating the randomness of input data, a single BOF plants data is collected. The data set is filtered with a strict limitation method according to the plant specific metallurgical interactions, bottom stirring effect and 10 Fold Cross Validation used for clustering in order to have homogenous learning process. The simulated results hit rate %92 within the error range ±0,01 for end-point carbon and %89.4 within the error range ±0,025 for end-point phosphorus are observed. For inblow the simulated results hit rate %83 within the error range ±0,02 for phosphorus and %93 within the error range ±0,15 are observed. The results showed that the output could be used in software to calculate P and C during the end of blowing and inblow without interrupting the blowing process like TSO or TSC measurement

Quantifying the transient interfacial area during slag-metal reactions

The steel industry is facing significant competition on a global scale due to the drive for light-weighting and cheaper more sustainable construction. Not aided by oversupply in geographic sectors of the industry, there is significant competition within the slowly shrinking sector. The recent growth in developing countries through installation of modern plant technology has led to the reduction in unique selling points for mature steelmaking locations. As such, to compete with the equalling product capability and innate cheaper production costs within developing areas the industries in Europe and North America require significant improvements in productivity and agile resource management. To date the basic oxygen furnace has been somewhat treated as a black box within industry, where only control parameters are monitored, not the fundamental mechanisms within the converter. Studies over the past 30 years have shown the basic oxygen furnace is unable to attain the thermodynamic minimum phosphorus content within the output liquid steel. Coupled with the need to drive down resource cost, with a potential for high content phosphorus ores the internal dynamic system of the basic oxygen furnace requires more rigorous understanding. With the aid of in-situ sampling of a pilot scale basic oxygen furnace, and laboratory studies of individual metal droplets suspended in a slag medium (known to be a key driving environment for impurity removal) the present project aims to provide insight into the transient interfacial area between slag and liquid metal through basic oxygen steelmaking processing. Initially the macroscopic dynamics including the amount of metal suspended in the gas/slag/metal emulsion, the period of time it is suspended for, and the speed at which it moves, is investigated. It was found that these parameters vary greatly through the blow, with a normal peak in residence times near the beginning of the blow and a dramatic increase in metal circulation rates at the end of the blow, when foaming is reduced or collapsed. Further to this, a method of interrogating the size of metal droplets within the slag layer using X-ray computed tomography is introduced. The study then progresses into the microscopic environments that individual droplets are subjected to during steel processing. Initially the cause of spontaneous emulsification in basic oxygen furnace type slags is investigated through high temperature-confocal scanning laser microscopy/X-ray computed tomography led experimentation, with the addition of null experiments conducted to rationalize the experimental technique. It was found that the flux of oxygen across the interface was the cause and thus the confirmation of material transfer across the interface being the driving force. Furthermore the physical pathway of emulsification is interrogated and quantified, with in-situ observation of spontaneous emulsification in the high temperature-confocal scanning laser microscope enabled through use of optically transparent slags. The life cycle of perturbation growth, necking and budding is observed and quantified through high-resolution X-ray computed tomography. In addition a phase-field model is developed to interrogate slag/metal systems in 2D and 3D variations, giving rise to the ability to track the cause of emulsification and to predict its occurrence. Finally the project progresses with the in-situ investigation of spontaneous emulsification as a function of initial metal composition. The behaviour of droplet spontaneous emulsification is seen to reduce in severity and subsequently to decline into a non-emulsifying regime below a critical level. Free energy calculations coupled with a measure of the global interfacial tension increase give quantifiable reasoning as to the behaviour seen

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

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)