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

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

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

Estudio de funcionamiento de la integración de una planta siderúrgica con una central de potencia y un sistema de captura de CO2

Como parte de las estrategias en la lucha contra el cambio climático y el reto que supone para la industria siderúrgica mejorar la eficiencia energética en la producción de acero y por tanto en la reducción de emisiones de CO2, se ha propuesto la integración de una planta siderúrgica con un sistema de captura de CO2 y una central de potencia. El objetivo de este TFM ha sido el desarrollo de una simulación de una industria siderúrgica, a nivel energético como másico. Seguidamente de un estudio de viabilidad de integración con una central de potencia para finalmente estudiar la integración con el sistema de captura de CO2. Para el estudio de la integración de los diferentes componentes se han simulado los balances de masa y de energía de la industria siderúrgica, atendiendo al comportamiento de cada componente interno según las mejores tecnologías disponibles actualmente. Todo ello seguido de un estudio del potencial inherente a la industria siderúrgica para una auto-captura de CO2. A la vista de los resultados obtenidos, el autor y director se decantan por la elección de un sistema CCS de solvente químico con aminas. De esta manera la integración energética y de captura permite la reducción de las emisiones de CO2 del orden del 90% con una producción eléctrica adecuada al régimen de funcionamiento de la planta siderúrgica. Para la producción eléctrica se ha escogido un ciclo combinado con turbina de gas en cabecera que aprovecha los gases energéticos de la siderurgia, con un ciclo de vapor en cola con una extracción de vapor para el sistema CCS. El sistema CCS es un ciclo de aminas de configuración básica (absorbedor-regenerador) con compresión de CO2. Por último se ha realizado un análisis económico así como de sensibilidad. Para ello se ha introducido el concepto de ‘emisiones evitadas’ estudiando las emisiones en cada una de las plantas por separado antes y después de la integración del sistema CCS. Los resultados preliminares son los suficientemente óptimos como para seguir investigando en este sector del campo de CCS

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

Minimising Particulate Emissions From Sintering Operations

With the drive for manufacturing and foundation industries to move towards a circular economy, the steel industry is making step changes to its processes that aim to produce greener and cleaner products. The current work is focused on sintering, which can account for almost half of all particulate matter (PM) emissions produced during integrated steelmaking. Historic sintering data has been explored to understand the formation of particulate matter and has informed experimental trials, simulating the sintering process. It has shown that it is feasible to reduce PM emissions without incurring significant capital expenditures for a new end-of-line abatement. Prioritising trials was supported by an understanding of the main key levers from the historical data analysis of the sinter plant and a pilot-scale sinter rig that had been modified to capture PM emissions was commissioned and validated. To promote a more circular economy within the steel industry, experimental work showed that the use of new micropellets made from recycled materials would enhance sintering performance and reduce PM emissions. It was determined that the amount of chloride content emitted from PM emissions increased in the waste gas stream as well as decreasing the electrostatic precipitator (ESP) abatement efficiency and this influence can be reduced by washing recycled materials to remove undesirable volatile elements before sintering. It was also established that by manipulating the ratio of nuclei, adhering, and non-adhering particles in the sinter blend by controlling the size fractions, along with partially replacing raw materials, the particle size distribution can be optimised to reduce PM emissions

Valorisation des laitiers issus de l'élaboration d'aciers inoxydables dans le béton

Electric Arc Furnace (EAF) slag and Argon Oxygen Decarburization (AOD) slag are the two principal slags resulted from stainless steel manufacture. Unlike blast furnace slag, a significant part of these steel slags is not valued and has to be treated as waste. Stainless steel slags are nowadays only used as aggregates in road construction and their future valorisation in concrete for building could be interesting. To our knowledge, that possibility has not yet been studied in the literature. The objective of this work is to study the possibility of using slags from stainless steel process as aggregates in concrete. Moreover, this is the first report on mineral composition and physical properties of stabilized AOD slags from stainless steel process.In this study, physicochemical and mechanical properties of EAF slag and stabilised AOD slag aggregates are firstly determined. Mineralogical composition of these stainless steel slag aggregates is also analysed to verify if they contain mineral phases likely to show expansion reactions. An original method using short and long UV lights was developed for studying the mineralogical composition of slag. Secondly, the natural aggregates of reference concretes are substituted, in different proportions, by stainless steel slag aggregates and several physical and mechanical properties are measured on concrete samples. The evolution of concrete mechanical properties has been followed over the time up to 365 days. Similarly, durability tests and swelling tests are made on concrete samples to assess the impact of EAF and AOD slag aggregates. SEM observations of crack network at the paste-aggregate interface and in concrete samples were carried out. The results show adequate structural properties, with a slight improvement of the mechanical properties for concretes made of stainless steel slag aggregates. The durability and expansion characteristics of these concretes are sufficient for construction use.Le laitier EAF inox et le laitier AOD sont les deux principaux laitiers issus de l'élaboration des aciers inoxydables. Aujourd'hui ces laitiers ne sont valorisés qu'en construction routière. Cette utilisation ne permet pas d'assurer une valorisation complète et pérenne de ces laitiers dont une large part reste stockée. Compte tenu des propriétés physiques des laitiers EAF inox et AOD solidifié par stabilisation, leur utilisation comme granulats dans le béton peut être intéressante. L'objectif de cette thèse est d'étudier la faisabilité de cette voie de valorisation. Ces travaux sont réalisés en deux parties.Premièrement les propriétés physico-chimiques et mécaniques des granulats de laitiers EAF inox et AOD stabilisé sont déterminées. La composition minéralogique complète de ces granulats de laitiers issus de l'élaboration d'aciers inoxydables est aussi analysée afin de rechercher la présence éventuelle de minéraux instables et incompatibles à leur utilisation dans le béton. Compte tenue de la fluorescence sous rayons ultra-violet (UV) des laitiers, une nouvelle approche qui associe les UV et les analyses minéralogiques courantes (DRX, MEB, EDS) est développée dans cette étude. Cette étape de caractérisation des granulats des laitiers étudiés fait de ce mémoire de thèse le premier rapport sur la caractérisation physique, chimique et minéralogique des granulats de laitier AOD stabilisé. La deuxième étape de cette étude consiste à réaliser des bétons en substituant, dans des proportions variées, des granulats naturels silico-calcaires d'un béton de référence par des granulats de laitiers EAF inox et AOD stabilisé. Les propriétés physiques et mécaniques de ces bétons à matrice ordinaires et à hautes performances sont évaluées. Une étude de durabilité de ces bétons est aussi effectuée à travers le suivi des propriétés mécaniques des bétons sur 365 jours, l'analyse d'indicateurs de durabilité et la réalisation d'essais de gonflement. Enfin des observations MEB sont faites sont faites les échantillons de béton âgés de 365 jours pour observer et analyser le réseau de fissures à l'interface granulat – matrice cimentaire. Les résultats montrent des propriétés adaptées à une utilisation structurelle, avec une légère amélioration des caractéristiques mécaniques et des propriétés de durabilité acceptables pour les bétons de granulats de laitiers EAF inox et AOD stabilisé

Bofy-fuzzy logic control for the basic oxygen furnace (BOF)

In this paper, fuzzy modeling for the control of basic oxygen furnace (BOF) processes is proposed. BOF is a widely preferred and effective steel making method due to its higher productivity and considerably low production cost. Therefore, today almost 65% of the total crude steel production in the world is met by using the BOF method. Higher steel output at lower cost is one of the main objectives of modem steel making methods. In order to accomplish this objective, fuzzy modeling was employed in this study in order to control some variables related to the BOF process. Fuzzy modeling and control in BOF promise a solution to the strongly non-linear problems associated with the process, which have so far proven extremely difficult to be solved by conventional control methods. Data set was selected as inputs from the real empirical BOF data in an integrated steel plant based in Turkey. Although there were negligible deviations from the target values, most of the fuzzy results obtained using MATLAB-Fuzzy Logic Toolbox version 5.0 were found to be acceptable. As a result of the application of the proposed modeling, acceptable levels of compatibility were achieved compared to the empirical BOF data and targeted steel composition. The paper indicates how fuzzy logic would be effectively used for improved process control of BOF furnace in steel making industry. (C) 2004 Elsevier B.V. All rights reserved