Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production

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%)

Challenges and Prospects of Steelmaking Towards the Year 2050

The world steel industry is strongly based on coal/coke in ironmaking, resulting in huge carbon dioxide emissions corresponding to approximately 7% of the total anthropogenic CO2 emissions. As the world is experiencing a period of imminent threat owing to climate change, the steel industry is also facing a tremendous challenge in next decades. This themed issue makes a survey on the current situation of steel production, energy consumption, and CO2 emissions, as well as cross-sections of the potential methods to decrease CO2 emissions in current processes via improved energy and materials efficiency, increasing recycling, utilizing alternative energy sources, and adopting CO2 capture and storage. The current state, problems and plans in the two biggest steel producing countries, China and India are introduced. Generally contemplating, incremental improvements in current processes play a key role in rapid mitigation of specific emissions, but finally they are insufficient when striving for carbon neutral production in the long run. Then hydrogen and electrification are the apparent solutions also to iron and steel production. The book gives a holistic overview of the current situation and challenges, and an inclusive compilation of the potential technologies and solutions for the global CO2 emissions problem

How does digitalization alter the paradox of supply base concentration? The effects of digitalization intensity and breadth

Purpose – The highly uncertain and turbulent environments nowadays intensify the paradoxical effects of supply base concentration (SBC) on improving cost efficiency while increasing idiosyncratic risk (IR). Digitalization is regarded as a remedy for this paradox, yet digitization’s potentially curative effect has not been empirically tested. Leveraging the lenses of paradox theory and information processing theory (IPT), this study explores how two distinct dimensions of digitalization, i.e. digitalization intensity (DI) and digitalization breadth (DB), reconcile the paradoxical effects of SBC. Design/methodology/approach – Using a panel dataset of 1,238 Chinese manufacturing firms in the period of 2012–2020, this study utilizes fixed-effects regression models to test the proposed hypotheses. Findings – The authors discover that SBC enhances a firm’s cost efficiency but induces greater IR. More importantly, there is evidence that DI restrains the amplifying effect of SBC on IR. However, DB weakens the enhancing effect of SBC on cost efficiency and aggravates the SBC’s exacerbating effect on IR. Originality/value – This study advances the understanding of the paradoxical effects of SBC on cost efficiency and IR from a paradox theory perspective. More importantly, to the best of the authors’ knowledge, the authors’ study is the first to untangle the differential roles of DI and DB in reconciling the paradox of SBC. This study also provides practitioners with nuanced insights into how the practitioners should use appropriate tactics to deploy digital technologies effectively

Multi-agent system implementation in demand response: A literature review and bibliometric evaluation

This research provides a comprehensive literature overview and bibliometric evaluation of multi-agent system (MAS) implementation in energy demand response (DR) to identify gaps. The review encompasses 39 relevant papers from searches in three academic databases, focusing on studies published from 2012 to the middle of 2023. The review includes MAS frameworks, optimization algorithms, communication protocols, market structures and evaluation methodologies. Bibliometric analysis of 587 documents from the search on the Scopus database identified prolific authors, influential articles and collaborative networks within the field. The findings reveal growing research interest in implementing an MAS for DR, focusing on integrating intelligent agents into electricity grids to enable effective load management and enhance grid stability. Additionally, the review outlines potential research directions, including exploring advanced MAS techniques, interoperability challenges, policy implications and the integration of renewable energy sources

Manufacturing of Photovoltaic Devices, Power Electronics and Batteries for Local Direct Current Power Based Nanogrid

To meet the current and future demands of electrical power for household, industrial, commercial and transport sectors, the energy infrastructure has to undergo changes in terms of generation, distribution and consumption. Due to the shortcomings of nuclear and fossil fuel based power generation, the emergence of renewable energy has provided a very lucrative option. With the advent of low-cost photovoltaics (PV) panels and our ability to generate, store and use electrical energy locally without the need for long-range transmission, the world is about to witness transformational changes in electricity infrastructures. For local nano-grids, direct current (DC) -based system has several distinct advantages that are demonstrated through theoretical and experimental results. A PV- powered and local DC power based nano-grids can be more efficient, reliable, cyber secured, and can easily adopt internet of things (IoT) platforms. With DC generation, storage and consumption, significant amount of energy can be saved that are wasted in back and forth conversion between AC and DC. In case of geomagnetic disturbances, such nano-grids will be more resilient compared to centralized distribution network. Free-fuel, i.e. sunlight, based local DC nano-grid can be the sustainable and cost effective solution for underdeveloped, developing and developed economies. To take advantage of this, the manufacturing of PV, power electronics and batteries have to follow the best practices that aid process control, quality improvement and potential cost reduction. Without proper process control, the variation will result in yield loss, inferior performance and higher cost of production. On many instances, these issues were not considered, and some technology such as perovskite solar cell, received a lot of attention as a disruptive technology. Through detailed technical and economic assessments, it was shown that the variability and lack of rigorous process control will result in a lower efficiency when perovskite thin film solar cells are connected together to form a module. Due to stability and performance reasons, it was showed the perovskite solar cell is not ideal for 2-terminal or 4-terminal multi-junction/tandem configuration with silicon cells. Power electronics also play a vital role in PV systems. The challenges and design rules for silicon carbide (SiC) and gallium nitride (GaN) based power device manufacturing were analyzed. Based on it, advanced process control (APC) based single wafer processing (SWP) tools for manufacturing SiC and GaN power devices are proposed. For energy storage, batteries play an important role in PV installation. Li-ion technology will become the preferred storage due to its capabilities. Incorporation of advanced process control, rapid thermal processing, Industrial IoT, etc. can reduce variability, improve performance and reduce quality-check failures and bring down the cost of electrochemical batteries. The combined approaches in manufacturing of PV, power electronics and batteries will have a very positive impact in the growth of PV powered DC –based nano-grids

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining