Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network

Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke

The main production route for cast iron and steel is through the blast furnace. The silicon content in cast iron is an important indicator of the thermal condition of a blast furnace. High silicon contents indicate an increase in the furnace\u2019s thermal input and, in some cases, may indicate an excess of coke in the reactor. As coke costs predominate in the production of cast iron, tighter control of the silicon content therefore has economic advantages. The main objective of this article was to design an artificial neural network to predict the silicon content in hot metal, varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, 100, 125, 150, 170 and 200 neurons. In general, all neural networks showed excellent results, with the network with 30 neurons showing the best results among the 12 modeled networks. The validation of the models was confirmed using the Mean Square Error (MSE) and Pearson\u2019s correlation coefficient. The cross-validation technique was used to re-evaluate the performance of neural networks. In short, neural networks can be used in practical operations due to the excellent correlations between the real values and those calculated by the neural network

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Development of a prediction model for ironmaking blast furnace operational conditions based on artificial neural networks

Orientadores: Ana Maria Frattini Fileti, Andre Pitasse da CunhaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuimicaResumo: Este trabalho descreve o desenvolvimento de um modelo capaz de prever as condições operacionais do processo de redução de minério de ferro em alto-forno a partir de características das matérias-primas e da composição da carga e do sopro, fornecendo aos operadores e engenheiros de processo, uma ferramenta de planejamento e de análise de desempenho da operação. A motivação para este trabalho deveu-se à constatação de que existe uma deficiência de modelos capazes de identificar cotidianamente os fatores críticos na operação dos altos-fornos siderúrgicos. Os modelos encontrados são excessivamente simples para preverem condições futuras de processo ou são demasiadamente complexos para o uso operacional diário ou no pJanejamento estratégico da produção. Os altos-fornos e os conversores de aço são as unidades centrais de uma usina siderúrgica integrada. O primeiro obtém o ferro primário a partir dos óxidos contidos nos minérios e o segundo refina e ajusta a composição química do metal produzindo o aço. O modelo desenvolvido e aplicado tem natureza híbrida, combinando algoritmos simuladores baseados em balanços de massa e energia com variável grau de desvio estequiométrico e térmico. O valor de cada indicador de desvio é previsto por uma rede neural cujas variáveis de entrada quantificam características das matérias-primas e condições de sopro e carga do alto-forno. A aplicação industrial do modelo comprovou sua capacidade de prever as condições do processo e sua aplicação resultou em aumento da produtividade média do processo e menor consumo específico de redutores, decorrentes da melhor efetividade das ações operacionais. Além disso, o modelo, associado a um módulo de balanço do setor primário da usina, vem sendo aplicado na simulação de alternativas de padrões operacionais, atividade fundamental para o planejamento estratégico do negócioAbstract: This work describes the development of a model capable of evaluating and predicting iron ore reduction process in blast-furnaces based on raw materiais characteristics as well as burden and blast composition. It provides a planning and analysing toll to operators and process engineers. The motivation for this development resides on the lack of this kind of model in the ironmaking industry. The many models found are either toa simple and not capable of predicting raw materiais parameters effects on the furnace performance or toa complexo The latter are useful in technology potential identifications but not in the daily work or ordinary operation planning. Blast-furnaces and oxygen converters are the core units in a integrated steel works. The former one produces primary iron from oxides bared by iron ores and the latter one refines molten iron into steel, adjusting its chemical compositiono The developed and applied model is hybrid in nature, combining simulating algorithms based on mass and energy balances with variable lack of fitnesso The value of each lack-of-fitness index is estimated by a neural model in which the input quantify burden materiais characteristics as well as blow and charging conditions. The model has shown its predicting capacity during its industrial application, which lead to higher average productivity and lower specific fuel consumption are expected following better operation action and process planning efficiency. In addition, the model is now being associated to a plant mass balance module to asses alternative operation pattern simulation for the hole company which is a fundamental activity in strategic business planningDoutoradoSistemas de Processos Quimicos e InformaticaDoutor em Engenharia Químic

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in an energy-efficient state. In the proposed scheme, production indicators prediction models are first developed using a kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multiobjective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of the approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which an improved association rule mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility

New Methods for ferrous raw materials characterization in electric steelmaking

425 p.In the siderurgical sector, the steel scrap is the most important raw material in electric steelmaking,contributing between 70% of the total production costs. It is well-known how the degree of which thescrap mix can be optimized, and also the degree of which the melting operation can be controlled andautomated, is limited by the knowledge of the properties of the scrap and other raw-materials in thecharge mix.Therefore, it is of strategic importance having accurate information about the scrap composition of thedifferent steel scrap types. In other words, knowing scrap characteristics is a key point in order to managethe steel-shop resources, optimize the scrap charge mix/composition at the electric arc furnace (EAF),increase the plant productivity, minimize the environmental footprint of steelmaking activities and tohave the lowest total cost of ownership of the plant.As a main objective of present doctoral thesis, the doctorate will provide new tools and methods of scrapcharacterization to increase the current recycling ration, through better knowledge of the quality of thescrap, and thus go in the direction of a 100% recycling ratio. In order to achieve it, two main workinglines were developed in present research. Firstly, it was analysed not only the different existingmethodologies for scrap characterization and EAF process optimization, but also to develop new methodsor combination of existing, Secondly, it was defined a general recommendations guide for implementingthese methods based on the specifics of each plant

Quality control and improvement of the aluminum alloy castings for the next generation of engine block cast components.

This research focuses on the quality control and improvement of the W319 aluminum alloy engine blocks produced at the NEMAK Windsor Aluminum Plant (WAP). The present WAP Quality Control (QC) system was critically evaluated using the cause and effect diagram and therefore, a novel Plant Wide Quality Control (PWQC) system is proposed. This new QC system presents novel tools for off line as well as on line quality control. The off line tool uses heating curve analysis for the grading of the ingot suppliers. The on line tool utilizes Tukey control charts of the Thermal Analysis (TA) parameters for statistical process control. An Artificial Neural Network (ANN) model has also been developed for the on-line prediction and control of the Silicon Modification Level (SiML). The student t-statistical analysis has shown that even small scale variations in the Fe and Mn levels significantly affect the shrink porosity level of the 3.0L V6 engine block bulkhead. When the Fe and Mn levels are closer to their upper specification limits (0.4 wt.% and 0.3wt.%, respectively), the probability of low bulkhead shrink porosity is as high as 0.73. Elevated levels of Sn (∼0.04 wt.%) and Pb (∼0.03 wt.%) were found to lower the Brinell Hardness (HB) of the V6 bulkhead after the Thermal Sand Removal (TSR) and Artificial Aging (AA) processes. Therefore, Sn and Pb levels must be kept below 0.0050 wt.% and 0.02 wt.%, respectively, to satisfy the bulkhead HB requirements. The Cosworth electromagnetic pump reliability studies have indicated that the life of the pump has increased from 19,505 castings to 43,904 castings (225% increase) after the implementation of preventive maintenance. The optimum preventive maintenance period of the pump was calculated to be 43,000 castings. The solution treatment parameters (temperature and time) of the Novel Solution Treatment during the Solidification (NSTS) Process were optimized using ANN and the Simulated Annealing (SA) algorithm. The optimal NSTS process (516°C and 66 minutes) would significantly reduce the present Thermal Sand Removal (TSR) time (4 hours) and would avoid the problem of incipient melting without sacrificing the mechanical properties. In order to improve the cast component characteristics and to lower the alloy price, a new alloy, Al 332, (Si=10.5 wt.% & Cu=2 wt.%) was developed by optimizing the Si and Cu levels of 3XX Al alloys as a replacement for the W319 alloy. The predicted as cast characteristics of the new alloy were found to satisfy the requirements of Ford engineering specification WSE-M2A-151-A2/A4.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .F735. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6201. Thesis (Ph.D.)--University of Windsor (Canada), 2005

Advances in Design by Metallic Materials: Synthesis, Characterization, Simulation and Applications

Very recently, a great deal of attention has been paid by researchers and technologists to trying to eliminate metal materials in the design of products and processes in favor of plastics and composites. After a few years, it is possible to state that metal materials are even more present in our lives and this is especially thanks to their ability to evolve. This Special Issue is focused on the recent evolution of metals and alloys with the scope of presenting the state of the art of solutions where metallic materials have become established, without a doubt, as a successful design solution thanks to their unique properties

Stress-Crack Separation Relationship for Macrosynthetic, Steel and Hybrid Fiber Reinforced Concrete

An experimental evaluation of the crack propaga tion and post-cracking response of macro fiber reinforced concrete in flexure is c onducted. Two types of structur al fibers, hooked end steel fibers and continuousl y embossed macro-synthetic fibers are used in this study. A fiber blend of the two fibers is evaluated for spec ific improvements in the post peak residual load carrying response. At 0.5% volume fraction, both steel and macrosynthetic fiber reinforced concrete exhibits load recovery at large crack opening. The blend of 0.2% macrosynthetic fibers and 0.3% steel fibers shows a significa nt improvement in the immediate post peak load response with a significantly smaller load drop and a constant residual load carrying capacity equal to 80% of the peak load. An analytical formulation to predict fle xure load-displacement behaviour considering a multi-linear stress- crack separation (σ -w) relationship is developed. An inverse analysis is developed for obtaining the multi- linear σ -w relation, from the experimental response. The � -w curves of the steel and macrosynthetic fiber reinforced concrete exhibit a stress recovery after a significant drop with increa sing crack opening. Significant residual load carrying capacity is attained only at large crack separation. The fiber blend exhibits a constant residual stress with increasing crack sepa ration following an initial decrease. The constant residual stress is attained at a small crack separation