Forecast of Carbon Consumption of a Blast Furnace Using Extreme Learning Machine and Probabilistic Reasoning

Blast furnaces are chemical metallurgical reactors for the production of pig iron and slag. The raw materials used (metallic feedstock) are sinter, granulated ore and pellets. The main fuel is metallurgical coke. Considering the existing difficulties in the field of simulation of complex processes, the application of solutions based on neural networks has gained space due to its diversity of application and increase in the reliability of responses. The Extreme Learning Machine is a way to train an artificial neural network (ANN) with only one hidden layer. The database used for numerical simulation corresponds to 3.5 years of reactor operation. Big Data contains 94875 pieces of information divided into 75 variables. The input of the ELM neural network is composed of 72 variables and the output of 3 variables. The selected output variables were coke rate, PCI rate and fuel rate. Artificial neural networks using extreme learning machines and using Big Data are able to predict fuel consumption based on the parameters of the reduction process in blast furnaces, and this can be verified by the accuracy of the model

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

Laves phase precipitation and sigma phase transformation in a duplex stainless steel microalloyed with niobium.

ABSTRACT Duplex stainless steels are used to replace austenitic steels in industrial applications, especially in the chemical industry where corrosion resistance in chloride-containing media is an important requirement. According to literature, niobium has a great influence on the transformation of the phases of these steels (ferrite, austenite and sigma). Given the interest in evaluating this property in terms of wear resistance and corrosion of the material, the aim of this study was to evaluate the effect of niobium on the formation in austenitic-ferritic stainless steel SEW 410 Nr 1.4517 after heat treatment at 850ºC for 15, 30 and 60 minutes. The niobium contents studied were 0.2; 0.5 and 1.5 wt%. Measurements of microhardness, electrochemical tests in saline solution containing 3.5% sodium chloride and wear resistance were carried out. The results show that an increase in niobium content and sigma phase leads to an increase in hardness and wear resistance, associated with a decrease in corrosion potential. Therefore, the use of niobium in austeno-ferritic stainless steel is recommended when wear resistance is an important factor to be considered

Evaluation of the use of blast furnace slag as an additive in mortars

Abstract Clinker is the raw material used in the manufacture of cement. However, this material is very harmful to the environment, since it is estimated that for every ton of clinker produced, about 1.0 ton of CO2 is released into the atmosphere. For this reason, alternatives were sought for the use of other materials that are less harmful to the environment. This has led to the use of industrial by-products with the aim of increasing their use and thus reducing the amount of carbon released into the atmosphere. Blast furnace slag is a by-product used in the manufacture of some cementitious products. The aim of this research is to conduct a study on the use of slag as an additive for cement or concrete. The mortar samples were tested according to Brazilian, American and European technical standards. Physical, chemical and compressive strength tests were carried out which confirmed the possibility of using the slag without chemical or thermal activation

AVALIAÇÃO DA INCORPORAÇÃO DE RESÍDUO DE CORTE DE MÁRMORE E GRANITO EM CONCRETO PARA PRODUÇÃO DE PISOS INTERTRAVADOS PARA PAVIMENTAÇÃO

A indústria da construção civil é uma atividade que consome elevado volume de recursos naturais e no cenário atual é imprescindível se preocupar com o desenvolvimento sustentável e encontrar alternativas de reaproveitamento de resíduos sólidos. Nesse contexto, a reinserção do resíduo de corte de mármore e granito (RCMG) na cadeia produtiva é uma alternativa para amenizar um sério problema ambiental, pois o consumo de rochas ornamentais vem crescendo e elevando a quantidade de resíduo produzido. Devido ao grande volume de resíduos de corte de granito produzido e não reutilizado, este artigo técnico avalia a viabilidade técnica da sua utilização como adição em concretos e produção de blocos intertravados com resistência à compressão mínima de 35 MPa e a absorção d'água de 6% no máximo. As matérias primas foram caracterizadas quanto à massa específica, massa unitária, distribuição do tamanho de partículas, materiais pulverulentos e composição química (FRX). Em seguida, os corpos de prova foram moldados de acordo com as prescrições da norma técnica NBR 9781:2013 e foram realizadas análises de desempenho mecânico (resistência à compressão) e absorção d'água. Em suma, a partir dos resultados obtidos no programa experimental, o uso do resíduo de corte de mármore e granito como adição em concretos para produção de pisos de pavimentação é viável tecnicamente para tráfego de pedestres, veículos leves e veículos comerciais de linha

UTILIZAÇÃO DA AREIA MARINHA PARA PRODUÇÃO DE CONCRETO: ESTUDO DE VIABILIDADE

A indústria da construção civil tem destacado papel na sociedade brasileira devido a sua importância socioeconômica. Estudos conduzidos no Brasil e no exterior apontam que a areia marinha se apresenta como uma alternativa viável na construção civil. Nesta direção, esta pesquisa analisa uma amostra de agregado miúdo do litoral da Serra/ES, Brasil. Foram realizados ensaios de composição granulométrica, massa específica, teor de argilas em torrões, teor de materiais friáveis, teor de materiais pulverulentos, dimensão máxima característica, avaliação de impurezas orgânicas solúveis, teor de cloretos solúveis, teor de sais solúveis, teor de sulfatos solúveis, teor de materiais carbonosos e pH e resistência a compressão uniaxial. Os ensaios colorimétricos de avaliação de penetração de cloretos no concreto, imerso em água do mar durante 7 dias, não destacaram a presença de cloretos livres em nenhuma das amostras. Ensaios de carbonatação não revelaram processos de carbonatação nos três corpos de prova analisados. Os ensaios de resistência à compressão mostraram que o concreto moldado com areia marinha possui resultados próximos ao moldado com areia de rio

Machine Learning Applications in the Steel Production Industry

The blast furnace is a countercurrent chemical metallurgical reactor, and monitoring the blast furnace is of paramount importance in producing a quality product. In this work, the behavior of the main chemical elements that make up the metallic charge was studied in detail. The elements affecting the final quality of the steel can be limited to carbon (C), silicon (Si), manganese (Mn), phosphorus (P), and sulfur (S). Manganese receives less attention because it often plays only a minor role in the production process. Carbon is the main raw material used as a reducing agent for iron oxides in blast furnaces. Metallurgical coke is the main supplier of carbon and accounts for a significant percentage of the cost of cast iron. Therefore, keeping consumption low has always been an important goal. In recent decades, pulverized coal injection (PCI) has become increasingly popular in the global steel industry to reduce specific coke consumption per ton produced. During production, it is important to control the thermal condition of the reactor, and the amount of silicon dissolved in the hot metal is used as an indicator. If the amount of dissolved silicon is high, it indicates an excess of energy, probably an excess of metallurgical coke in the reactor. However, if the silicon level is low, the reactor may need to be warmed up and countermeasures taken to correct the problem. Phosphorus is an element that causes little change in metallurgical processes (sintering, coke ovens, and blast furnaces) but is extremely detrimental to quality, productivity, and cost, especially in steel production, which requires a low phosphorus content in its chemical composition. The amount of phosphorus dissolved in the metal depends on the chemical composition of the metallic charge (sinter, pellets and lump ore). It is possible to reduce the phosphorus content in hot metal by controlling the reactor temperature and the residence time of the molten metal in the reactor. Sulfur in steel is an undesirable residue that negatively affects properties such as ductility, toughness, weldability and corrosion resistance. The production of low-sulfur steel is extremely important for shipbuilding and for pipes for the oil industry. The application of neural network solutions is very popular due to its versatility and reliability. The application of neural network technology in steel production is new and there are few works on this topic. The database of this work consists of Big Data corresponding to 3450 operating days and divided into seven groups: Air Injection, Top gas, Thermal Control, Fuels, Minerals, Hot metal and Slag. The Big Data were divided into three parts, comprising a total of 3 different samples, each with 1150 variables, with 9 variables of interest divided into 3 groups. The variables studied were silicon, phosphorus, sulfur, carbon, temperature, daily production, coke rate, PCI and fuel rate. The artificial neural networks were trained using the Levenberg-Marquardt algorithm with sigmoidal activation functions in the hidden layers. The committee machine has 12 classifiers for each element. Each classifier has 3 neural networks with the same architecture and the same number of neurons, but the input variables used to train the neural network are different. The artificial neural networks have 10, 20, 25, 30, 40, 50, 75, 100, 125, 150, 175, and 200 neurons in each layer. In this study, the quality of the neural network model was evaluated using Pearson's root mean square error (RMSE) and correlation coefficient (R). The result obtained with the committee machine is better than the results of the individual models, but still the models alone are able to provide good and convergent results. Looking at the RMSE values between training, validation and testing, no differences were found that could indicate overfitting, nor do the results obtained indicate underfitting. Neural networks operating in a committee system have been shown to have better predictive power than models from the literature. In conclusion, neural networks working in a committee system can be used in practice because the model is both a predictive tool and an action guide due to the excellent correlations between the real values and the values calculated by the neural network

Rem: Revista Escola de Minas Wandercleiton da Silva Cardoso Austenitic-ferritic stainless steel containing niobium

Resumo Os aços inoxidáveis austeno-ferríticos apresentam melhor combinação de propriedades mecânicas e resistência à corrosão que os inoxidáveis austeníticos e ferríticos. A microestrutura desses aços depende da composição química e tratamentos térmicos. Nos aços inoxidáveis austeno-ferríticos, a solidificação inicia a 1450ºC com a formação de ferrita, austenita a 1300ºC e fase sigma na faixa de 600 a 950ºC. Esta última compromete a resistência à corrosão e a tenacidade desses aços. Conforme a literatura, o nióbio tem grande influência na transformação de fases dos aços inoxidáveis austeno-ferríticos. Essa pesquisa avaliou o efeito do nióbio na microestrutura, dureza e resistência de transferência de carga de um aço inoxidável austeno-ferrítico. As amostras foram solubilizadas a 1050ºC e envelhecidas a 850ºC, para promover a formação da fase sigma. Os ensaios de corrosão foram realizados em meio de saliva artificial. Os resultados mostram que a adição de 0.5% Nb no aço provoca a formação da fase de Laves. Essa fase, associada à fase sigma, aumenta a dureza do aço, reduzindo, porém, os valores da resistência de transferência de carga. Palavras-chave: Aço inoxidável austeno-ferrítico, fase sigma, fase de Laves, resistência de transferência de carga. Abstract The austenitic-ferritic stainless steels present a better combination of mechanical properties and stress corrosion resistance than the ferritic or austenitic ones