Material and energy flows of the iron and steel industry: status quo, challenges and perspectives

Integrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies

Fuzzy control systems for thermal processes: synthesis, design and implementation

A completed case study on fuzzy logic control of thermal processes has been carried out using a professional laboratory oven for industrial purpose as an experimental test rig. It involved system engineering design analysis, control synthesis, and implementation as well as application software and signal interface design and development. The resulting expertise and lessons learned are reported in this contribution. The structure of PD type of fuzzy logic controllers is closely discussed along with synthesis issues of membership functions and knowledge rule base. Special software was developed using Microsoft Visual Studio, C++ and Visual basic for GUI for a standard PC platform. The application software designed and implemented has four modules: FIS editor, Rule Editor, Membership Function Editor and Fuzzy Controller with Rule Viewer. Quality and performance of the overall fuzzy process control system have been investigated and validated to fulfill the required quality specification

Contribution to the study and design of advanced controllers : application to smelting furnaces

In this doctoral thesis, contributions to the study and design of advanced controllers and their application to metallurgical smelting furnaces are discussed. For this purpose, this kind of plants has been described in detail. The case of study is an Isasmelt plant in south Peru, which yearly processes 1.200.000 tons of copper concentrate. The current control system is implemented on a distributed control system. The main structure includes a cascade strategy to regulate the molten bath temperature. The manipulated variables are the oxygen enriched air and the oil feed rates. The enrichment rate is periodically adjusted by the operator in order to maintain the oxidizing temperature. This control design leads to large temperature deviations in the range between 15ºC and 30ºC from the set point, which causes refractory brick wear and lance damage, and subsequently high production costs. The proposed control structure is addressed to reduce the temperature deviations. The changes emphasize on better regulate the state variables of the thermodynamic equilibrium: the bath temperature within the furnace, the matte grade of molten sulfides (%Cu) and the silica (%SiO2) slag contents. The design is composed of a fuzzy module for adjusting the ratio oxygen/nitrogen and a metallurgical predictor for forecasting the molten composition. The fuzzy controller emulates the best furnace operator by manipulating the oxygen enrichment rate and the oil feed in order to control the bath temperature. The human model is selected taking into account the operator' practical experience in dealing with the furnace temperature (and taking into account good practices from the Australian Institute of Mining and Metallurgy). This structure is complemented by a neural network based predictor, which estimates measured variables of the molten material as copper (%Cu) and silica (%SiO2) contents. In the current method, those variables are calculated after carrying out slag chemistry assays at hourly intervals, therefore long time delays are introduced to the operation. For testing the proposed control structure, the furnace operation has been modeled based on mass and energy balances. This model has been simulated on a Matlab-Simulink platform (previously validated by comparing real and simulated output variables: bath temperature and tip pressure) as a reference to make technical comparisons between the current and the proposed control structure. To systematically evaluate the results of operations, it has been defined some original proposals on behavior indexes that are related to productivity and cost variables. These indexes, complemented with traditional indexes, allow assessing qualitatively the results of the control comparison. Such productivity based indexes complement traditional performance measures and provide fair information about the efficiency of the control system. The main results is that the use of the proposed control structure presents a better performance in regulating the molten bath temperature than using the current system (forecasting of furnace tapping composition is helpful to reach this improvement). The mean square relative error of temperature error is reduced from 0.72% to 0.21% (72%) and the temperature standard deviation from 27.8ºC to 11.1ºC (approx. 60%). The productivity indexes establish a lower consumption of raw materials (13%) and energy (29%).En esta tesis doctoral, se discuten contribuciones al estudio y diseño de controladores avanzados y su aplicación en hornos metalúrgicos de fundición. Para ello, se ha analizado este tipo de plantas en detalle. El caso de estudio es una planta Isasmelt en el sur de Perú, que procesa anualmente 1.200.000 toneladas de concentrado de cobre. El sistema de control actual opera sobre un sistema de control distribuido. La estructura principal incluye una estrategia de cascada para regular la temperatura del baño. Las variables manipuladas son el aire enriquecido con oxígeno y los flujos de alimentación de petróleo. La tasa de enriquecimiento se ajusta perióodicamente por el operador con el fin de mantener la temperatura de oxidación. Este diseño de control produce desviaciones de temperatura en el rango entre 15º C y 30º C con relación al valor de consigna, que causa desgastes del ladrillo refractario y daños a la lanza, lo cual encarece los costos de producción. La estructura de control propuesta esta orientada a reducir las desviaciones de temperatura. Los cambios consisten en mejorar el control de las variables de estado de equilibrio termodinámico: la temperatura del baño en el horno, el grado de mata (%Cu) y el contenido de escoria en la sílice (%SiO2). El diseño incluye un módulo difuso para ajustar la proporción oxígeno/nitrógeno y un predictor metalúrgico para estimar la composición del material fundido. El controlador difuso emula al mejor operador de horno mediante la manipulación de la tasa de enriquecimiento de oxígeno y alimentación con el fin de controlar la temperatura del baño del aceite. El modelo humano es seleccionado teniendo en cuenta la experiencia del operador en el control de la temperatura del horno (y considerando el principio de buenas prácticas del Instituto Australiano de Minería y Metalurgia). Esta estructura se complementa con un predictor basado en redes neuronales, que estima las variables medidas de material fundido como cobre (%Cu) y el contenido de sílice (%SiO2). En el método actual, esas variables se calculan después de ensayos de química de escoria a intervalos por hora, por lo tanto se introducen tiempos de retardo en la operación. Para probar la estructura de control propuesto, la operación del horno ha sido modelada en base a balances de masa y energía. Este modelo se ha simulado en una plataforma de Matlab-Simulink (previamente validada mediante la comparación de variables de salida real y lo simulado: temperatura de baño y presión en la punta de la lanza) como referencia para hacer comparaciones técnicas entre la actual y la estructura de control propuesta. Para evaluar sistemáticamente los resultados de estas operaciones, se han definido algunas propuestas originales sobre indicadores que se relacionan con las variables de productividad y costos. Estos indicadores, complementados con indicadores tradicionales, permite evaluar cualitativamente los resultados de las comparativas de control. Estos indicadores de productividad complementan las medidas de desempeño tradicionales y mejoran la información sobre la eficiencia de control. El resultado principal muestra que la estructura de control propuesta presenta un mejor rendimiento en el control de temperatura de baño fundido que el actual sistema de control. (La estimación de la composición del material fundido es de gran ayuda para alcanzar esta mejora). El error relativo cuadrático medio de la temperatura se reduce de 0,72% al 0,21% (72%) y la desviación estandar de temperatura de 27,8 C a 11,1 C (aprox. 60%). Los indicadores de productividad establecen asimismo un menor consumo de materias primas (13%) y de consumo de energía (29%)

Approach for Improved Signal-Based Fault Diagnosis of Hot Rolling Mills

Der hier vorgestellte Ansatz ist in der Lage, zwei spezifische schwere Fehler zu erkennen, sie zu identifizieren, zwischen vier verschiedenen Systemzuständen zu unterscheiden und eine Prognose bezüglich des Systemverhaltens zu geben. Die vorliegende Arbeit untersucht die Zustandsüberwachung des komplexen Herstellungsprozesses eines Warmbandwalzwerks. Eine signalbasierte Fehlerdiagnose und ein Fehlerprognoseansatz für den Bandlauf werden entwickelt. Eine Literaturübersicht gibt einen Überblick über die bisherige Forschung zu verwandten Themen. Es wird gezeigt, dass die große Anzahl vorheriger Arbeiten diese Thematik nicht gelöst hat und dass weitere Untersuchungen erforderlich sind, um eine zufriedenstellende Lösung der behandelten Probleme zu erhalten. Die Entwicklung einer neuen Signalverarbeitungskette und die Signalverarbeitungsschritte sind detailliert dargestellt. Die Klassifikationsaufgabe wird in Fehlerdiagnose, Fehleridentifikation und Fehlerprognose differenziert. Der vorgeschlagene Ansatz kombiniert fünf verschiedene Methoden zur Merkmalsextraktion, nämlich Short-Time Fourier Transformation, kontinuierliche Wavelet Transformation, diskrete Wavelet Transformation, Wigner-Ville Distribution und Empirical Mode Decomposition, mit zwei verschiedenen Klassifikationsalgorithmen, nämlich Support-Vektor Maschine und eine Variation der Kreuzkorrelation, wobei letztere in dieser Arbeit entwickelt wurde. Kombinationen dieser Merkmalsextraktion und Klassifikationsverfahren werden an Walzkraft-Daten aus einer Warmbreitbandstraße angewendet.The approach introduced here is able to detect two specific severe faults, to identify them, to distinguish between four different system states, and to give a prognosis on the system behavior. The presented work investigates the condition monitoring of the complex production process of a hot strip rolling mill. A signal-based fault diagnosis and fault prognosis approach for strip travel is developed. A literature review gives an overview about previous research on related topics. It is shown that the great amount of previous work does not cope with the problems treated in this work and that further investigation is necessary to provide a satisfactory solution. The design of a new signal processing chain is presented and the signal processing steps are detailed. The classification task is differentiated into fault detection, fault identification and fault prognosis. The proposed approach combines five different methods for feature extraction, namely short time Fourier transform, continuous wavelet transform, discrete wavelet transform, Wigner-Ville distribution, and empirical mode decomposition, with two different classification algorithms, namely support vector machine and a variation of cross-correlation, the latter developed in this work. Combinations of these feature extraction and classification methods are applied to rolling force data originating from a hot strip mill

Coal-fired furnace modeling oriented to operational decision support

Esta dissertação enfoca o desenvolvimento de um modelo de suporte a decisão de opera ção de uma fornalha de carvão pulverizado, explorando as bases teóricas de dois métodos de solução. O modelo de combustão do carvão utiliza uma descrição da combustão que não considera características dimensionais da fornalha, baseando-se no balanço de átomos, e estimando assim importantes parâmetros de processo como o poder calorí co do carvão, ar necessário à combustão, vazão e temperatura do gás de combustão assim com a concentração dos principais poluentes. O modelo de combustão trata a zona gasosa da fornalha como um reator perfeitamente misturado, sendo sensível à composição química do carvão, assim como à parâmetros de processo como a vazão e temperatura de entrada do ar e combustível. Resultados considerando a fornalha como um único reator, quando comparados à dados reais de operação, apresentaram desvios relativos de 18.46% para a temperatura do gás de combustão e -1.32% para o HHV e 1.82% para o LHV do carvão. Quanto à emissão de poluentes o modelo apresentou desvios relativos de 4.82% para o SO2, 14.72% para CO2, -89.61% para o NO e 53.85% para o O2. A segunda abordagem foi realizada dividindo-se o domínio da fornalha em múltiplas zonas de gás. A radiação foi abordada pelo Método Zonal de Hottel, o qual subdivide o domínio da fornalha em um conjunto de zonas isotérmicas (de superfície e gasosas) e utiliza-se de áreas de troca diretas, determinadas através das correlações polinomiais de Tucker. As áreas de troca totais foram calculadas para contabilizar as múltiplas re ecções dentro da fornalha, enquanto balanços de energia em cada zona foram resolvidos iterativamente. A validação do modelo foi obtida simulando a caldeira de referência estudada por Ström, 1980, onde apesar de ter sido adotado um coe ciente de absorção médio constante (K = 0.5), desvios relativos máximos de 7.6% foram encontrados em relação ao trabalho original. O desvio relativo médio dos resultados em comparação aos dados apresentados por Ström foi de apenas 1.7%. A avaliação de um caso real foi proposta, combinando-se as duas abordagens apresentadas, formando um modelo aplicado a fornalha da caldeira de PECÉM, instalada no estado do Ceará-BR. Um esquema de duas zonas foi proposto, incluindo o modelo de combustão desenvolvido. O resultado do modelo para a temperatura dos gases de combustão apresentou um desvio relativo de apenas 13.12% em relação aos dados obtidos de PECÉM. Em relação a capacidade de predição de poluentes do modelo, diferenças maiores foram observadas. A predição da concentração de dióxido de enxofre apresentou um desvio relativo aos dados reais de 4.04%, enquanto para o CO2 e O2 as diferenças foram de 19.46% e 23.53%, respectivamente. Predições de NO aparecem como um interessante resultado, uma vez que apesar da discretização limitada proposta no modelo, relativa concordância foi observada (desvios relativos de -75.75%). O presente modelo provou ser uma abordagem adequada para a descrição da operação de uma fornalha a carvão pulverizado, combinando processamento rápido com uma implementação simpli- cada. O modelo apresentou bons resultados para a predição da temperatura do gás de combustão e poder calorí co do carvão. A emissão de poluentes, por outro lado, exige maior detalhamento em sua descrição através de equações de taxa de reação, buscando melhorar a precisão do modelo. Não obstante, o modelo foi capaz de sugerir cenários de operação da fornalha em função de diferentes composições de carvão e dos parâmetros de processo, atingindo os requisitos de um modelo básico de suporte à decisão operacional.This master thesis focuses on the development of a coal- red furnace operational decision support model, exploring the theoretical basis of two solution methods. The so-called combustion model is a zero-dimensional approach for coal combustion, based in atomic balance, which estimates important process parameters such as coal Higher Heating Value (HHV), Lower Heating Value (LHV), air ow rate and ue gas temperatures, followed by the concentration of main chemical species in furnace outlet. Combustion model approaches the gas zones as perfect stirred reactors, sensitive to the coal chemical composition and the input process parameters such as inlet ow rates and temperatures. Results were generate considering the entire furnace domain as one reactor, where 18.46% relative deviation was found to the measured ue gas temperature, while HHV and LHV deviates only -1.32% and 1.82%, respectively. Model results for pollutant emission displayed relative deviations of 4.82% for SO2, 14.72% for CO2, -89.61% for NO and 53.85% for O2. The second solution approach consisted of subdividing furnace domain into multiple gas zones. Radiation was approached by means of Hottel's Zonal Method (ZM), which considered isotherm zones (surfaces or gas volumes) to calculate direct exchange areas with the help of Tucker's polynomial correlations. Total exchange areas were calculated to account for radiation multiple re ections inside the furnace, while the energy balance equation system was solved iteratively. Model validation was performed by simulating the benchmark furnace studied by Ström, 1980, with a maximum 7.6% relative deviation to real data, despite the assumption of a constant media absorption coe cient (K = 0.5). Assessment of a real case was performed by combining both approaches, to model the boiler furnace of PECÉM power plant, installed in Ceará-BR. A two gas-zone scheme was proposed, embedding the developed combustion model to describe PECÉM furnace operation. Model ue gas temperature result was 13.12% distant from the measured value. Prediction on sulfur dioxide concentration displayed 4.04% relative deviation to measured data, while CO2 and O2 were 19.46% and 23.53% distant from PECÉM records, respectively. Prediction of NO emission appears as an interesting result since even with a coarse discretization of the domain, relative concordance with real data was observed (-75.75% deviation). The presented model proved to be an interesting approach to describe the behavior of a coal- red furnace, combining fast processing with a simpli ed implementation. Flue gas temperature and coal high heating value were close to measured data. Pollutant emission, however, requires a more detailed treatment, with reaction rate equations, to improve result accuracy. Notwithstanding, the model was able to suggest operation scenarios as a function of di erent coal compositions and process parameters, meeting the requirements of a basic operation decision support model