A hybrid modelling approach based on deep learning for the prediction of the silicon content in the blast furnace

The blast furnace is an important part of the steelmaking process, and its main function is to melt and reduce oxygen from the iron ore for subsequent processing into the steel-ironmaking process. Due to its complexity, Blast Furnaces need to operate near their practical limits because of economic and environmental constraints. The capacity to monitor and regulate the process's thermal condition is, however, constrained by the harsh operating conditions inside the furnace. The amount of silicon present in pig iron, which is the metallic iron generated by the blast furnace process, serves as a crucial indicator of the furnace's thermal condition. Therefore, the creation of a predictive model is essential to assist in proactive control of the furnace's thermal condition because measurements of this crucial variable can only be sampled at sporadic and irregular intervals and analysis of the sample introduces a substantial delay. In this paper, an improved hybrid modelling methodology is introduced for blast furnace operation, which integrates physical and data-driven models. Deep Learning based Autoencoders are used for the prediction of the changes in silicon concentration with respect to time and that helps users to avoid running frequent and costly feature pre-processing procedures and correlation studies. Integrating the physical model improved the prediction accuracy compared to a purely data-driven model

The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications

The XDEM multi-physics and multi-scale simulation platform roots in the Ex- tended Discrete Element Method (XDEM) and is being developed at the In- stitute of Computational Engineering at the University of Luxembourg. The platform is an advanced multi- physics simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and multi-scale simulation tools. For this purpose the simulation framework relies on coupling various predictive tools based on both an Eulerian and Lagrangian approach. Eulerian approaches represent the wide field of continuum models while the Lagrange approach is perfectly suited to characterise discrete phases. Thus, continuum models include classical simulation tools such as Computa- tional Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended configuration of the classical Discrete Element Method (DEM) addresses the discrete e.g. particulate phase. Apart from predicting the trajectories of individual particles, XDEM extends the application to estimating the thermo- dynamic state of each particle by advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either CFD or FEA opens up a wide range of applications as diverse as pharmaceutical industry e.g. drug production, agriculture food and processing industry, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology

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

A neural network approach to the modeling of blast furnace

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 65-69).by Angela X. Ge.M.Eng

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

Domain Knowledge integrated for Blast Furnace Classifier Design

Blast furnace modeling and control is one of the important problems in the industrial field, and the black-box model is an effective mean to describe the complex blast furnace system. In practice, there are often different learning targets, such as safety and energy saving in industrial applications, depending on the application. For this reason, this paper proposes a framework to design a domain knowledge integrated classification model that yields a classifier for industrial application. Our knowledge incorporated learning scheme allows the users to create a classifier that identifies "important samples" (whose misclassifications can lead to severe consequences) more correctly, while keeping the proper precision of classifying the remaining samples. The effectiveness of the proposed method has been verified by two real blast furnace datasets, which guides the operators to utilize their prior experience for controlling the blast furnace systems better.Comment: 9 pages, 4 figure

Optimization of Blast Furnace Parameters using Artificial Neural Network

Inside the blast furnace (BF) the process is very complicated and very tough to model mathematically. Blast furnace is the heart of the steel industry as it produces molten pig iron which is the raw material for steel making. It is very important to minimise the operational cost, reduce fuel consumption, and optimise the overall efficiency of the blast furnace and also improve the productivity of the blast furnace. Therefore a multi input multi output (MIMO) artificial neural network (ANN) model has been developed to predict the parameters namely raceway adiabatic flame temperature (RAFT), shaft temperature and uptake temperature. The input parameters in the ANN model are oxygen enrichment, blast volume, blast pressure, top gas pressure, hot blast temperature (HBT), steam injection rate, stove cooler inlet temperature, & stove cooler outlet temperature. For the optimisation of the predictive output back propagation ANN model has been introduced. In this present work, Artificial Neural Network (ANN) has been used to predict and optimise the output parameters. All the input data were collected from Rourkela steel plant (RSP) of blast number IV during the one month of operation

A prediction method of silicon content in hot metal of blast furnace

In blast furnace smelting, the silicon content in hot metal can indirectly reflect the blast furnace temperature and measure the quality of hot metal. For more accurate prediction, according to the reduction reaction, the input parameters affecting the silicon content are selected to form a data set. The Weighted Random Forest (WRF) prediction model and the Scaling Coefficient Particle Swarm Optimization (SCPSO) algorithm are proposed. The prediction method based on SCPSO-WRF has higher prediction hit rate and lower mean error than those traditional methods. The results show that the prediction hit rate and mean error of SCPSO-WRF are 89,1 % and 0,0291 respectively. The prediction method has theoretical research and practical application value

Mechanismen der Siliziumaufnahme von Roheisenschmelzen während stationärer und instationärer Hochofenbetriebszustände

Der Hochofenprozess ist bis heute das führende Verfahren in der Welt auf der Stahlerzeugungsroute. Demzufolge sind kontinuierliche Prozessverbesserungen notwendig, um die metallurgischen und wirtschaftlichen Ergebnisse der Hochofentechnologie zu optimieren. Im Hinblick auf den Siliziumgehalt im Roheisen ist die heutige Herausforderung im stationären Betriebszustand diesen konstant auf einem niedrigen Niveau zu halten. Nach instationären Betriebszuständen ist der Siliziumgehalt nicht kontrollierbar und wird mit deutlich höheren Gehalten abgestochen, als vom Stahlwerk vorgegeben. In den 60iger Jahren wurden von Rein und Chipman die Gleichgewichte zwischen siliziumreichen Schlacken und kohlenstoffgesättigtem Eisen hinreichend erforscht. Dabei sind aber die Aufnahmemechanismen zwischen der Schlacke und dem Roheisen im Hochofenbetrieb bis heute nicht eindeutig geklärt. Die theoretischen Ansätze aus der Literatur werden dazu diskutiert. Um den Vorgang der Siliziumaufnahme in Roheisenschmelzen zu verdeutlichen, wurden Tiegelexperimente am Institut für Metallurgie in Clausthal durchgeführt, die die unterschiedliche Austauschformen zwischen Silizium und Roheisen in einem Hochofen darstellen. Dabei wurden Experimente durchgeführt, bei denen der Fokus auf der Grenzflächenreaktion lag. Zusätzlich wurden Versuche erstellt, die die Gasphase berücksichtigt. Die Erkenntnisse, die in den Tiegelexperimenten erhalten wurden, konnten mit Koks und Eisenmöllerproben aus dem Versuchshochofen in Luleå (Schweden) bestätigt werden. Des Weiteren konnten diese Ergebnisse mit der Auswertung von Abstichdaten bei industriellen Hochöfen nachgewiesen werden. Anhand dieser Untersuchungsergebnisse konnten die Aufnahmemechanismen von Silizium in Roheisenschmelzen dargestellt und die im Ofen verantwortlichen Parameter selektiert werden. Durch diese Bewertung kann der Prozess vor geplanten instationären Prozessen angepasst werden, so dass der Siliziumgehalt nach dem Wiederanblasen im Roheisen nicht überschlägt. Mit diesen Untersuchungsergebnissen können weiter die statistischen Vorhersagemodelle für Silizium im Roheisen kritisch betrachtet werden. Für diese Modelle wurden Messdaten verwendet, die außerhalb des Ofens aufgenommen werden, jedoch ist die Siliziumreaktion eine Reaktion die von der Thermodynamik, Kinetik und Strömungsmechanik abhängig ist. Diese Faktoren sind bisher bei den bisherigen Modellen nicht beachtet worden.The blast furnace process is the most important route of steel production. Therefore continuous process improvements are necessary to optimize the metallurgical and economic results of the blast furnace technologies. Regarding the pig iron silicon content the difficulty is not a low value in principle, but a constant one. After e.g. shut downs the silicon content is not controllable and pig iron will be taped with higher silicon content as required by the steel mill. During the 60th’s Rein and Chipman investigated sufficient equilibria between silicon rich slag and carbon saturated iron.Allthogh, the absorption mechanism between slag and iron in the blast furnace are not clear. The theoretical approaches will be discussed in this work. To illustrate the process of absorbing silicon in pig iron, experiments were executed at the institute of metallurgy in Clausthal. Two different experiments were performed. On the one hand experiments were made to investigate the reaction at the interface, on the other hand the focus was on the gas phase. The knowledge of those experiments was also confirmed at coke and burden samples of the experiment blast furnace in Luleå (Sweden). Furthermore it was also found in tapping data of industrial blast furnaces. With these results the absorption mechanism of silicon in pig iron melts can be described. The most important parameter of the blast furnace process could be selected. By this analysis the process can be adjusted before shut downs, so the silicon content increase not too much. With this investigation the statistic prediction models for silicon in pig iron can be examined. For these models measured data were used, which were be taken outside the furnace or out of process gas. The silicon reaction depends on thermodynamic, kinetic and fluid mechanism. These factors are not used in the previous models