scholarly journals Radar Detection-Based Modeling in a Blast Furnace: A Prediction Model of Burden Surface Descent Speed

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 609 ◽  
Author(s):  
Jiuzhou Tian ◽  
Akira Tanaka ◽  
Qingwen Hou ◽  
Xianzhong Chen
Keyword(s):  
Blast Furnace ◽  
Radial Profile ◽  
Surface Shape ◽  
Data Driven ◽  
Speed Distribution ◽  
Proposed Model ◽  
Operating Blast Furnace ◽  
Equivalent Field ◽  
Operating Blast ◽  
Vital Factor

The distribution of burden layers is a vital factor that affects the production of a blast furnace. Radars are advanced instruments that can provide the detection results of the burden surface shape inside a blast furnace in real time. To better estimate the burden layer thicknesses through improving the prediction accuracy of the burden descent during charging periods, an innovative data-driven model for predicting the distribution of the burden surface descent speed is proposed. The data adopted were from the detection results of an operating blast furnace, collected using a mechanical swing radar system. Under a kinematic continuum modeling mechanism, the proposed model adopts a linear combination of Gaussian radial basis functions to approximate the equivalent field of burden descent speed along the burden surface radius. A proof of the existence and uniqueness of the prediction solution is given to guarantee that the predicted radial profile of the burden surface can always be calculated numerically. Compared with the plain data-driven descriptive model, the proposed model has the ability to better characterize the variability in the radial distribution of burden descent speed. In addition, the proposed model provides prediction results of higher accuracy for both the future surface shape and descent speed distribution.

Automated Monitoring of Physical Processes of Formation of Burden Material Surface and Gas Flow in Blast Furnace

2018 ◽  
Vol 277 ◽  
pp. 54-65 ◽  
Author(s):  
Anatoliy Golovchenko ◽  
Yuliya Pazynich ◽  
Michał Potempa
Keyword(s):  
Blast Furnace ◽  
Surface Texture ◽  
Gas Flow ◽  
Current Control ◽  
Material Surface ◽  
Burden Distribution ◽  
Flow Stabilization ◽  
Operating Blast Furnace ◽  
First Time ◽  
Operating Blast

The paper is devoted to the issues of energy saving automatic control of radial burden distribution in the blast furnace throat. The main idea consists in control with prediction of the control resulting on the basis of automatic monitoring of burden surface texture. The paper develops the mathematic description of burden surface texture on the blast furnace throat by means of substantiation of minimum quantity of general indicators of the mixture being closely related to the main parameters of blast furnace processes. It is the first time that the optimum value of hoper depth in burden surface at 0.14 – 0.2 of throat diameter determined, the methods of its stabilization at the rate are substantiated, the new regularity of burden surface formation on the operating blast furnace throat is shown as consisting in the fact that the hoper depth on the surface is mainly changed responding the process of material charge rather than bulk material descent after the charge. It was also substantiated for the first time that radioisotopic methods for current control of burden distribution on the blast furnace throat provide timely formation of control actions for gas flow stabilization. The principle of self-tuning was theoretically substantiated for monitoring system of gamma profilometer responding to the monitoring conditions with respect to high penetration and random character of gamma rays. The principle enables significant improvement of accuracy, quick-response and radiological safety of gamma profilometer operation. The possibility of determination of burden surface texture on the throat of operating blast furnace and distribution of burden components according to infrared radiation of the surface without application of radiation hazardous monitoring means was proved for the first time.

Extending the Campaign Life of an Intensively Operating Blast Furnace

Metallurgist ◽  
2017 ◽  
Vol 60 (9-10) ◽  
pp. 905-911 ◽  
Author(s):  
S. V. Filatov ◽  
I. F. Kurunov ◽  
Ya. M. Gordon ◽  
D. N. Tikhonov ◽  
S. N. Grachev
Keyword(s):  
Blast Furnace ◽  
Campaign Life ◽  
Operating Blast Furnace ◽  
Operating Blast

scholarly journals Some Aspects of the Control for the Radial Distribution of Burden Material and Gas Flow in the Blast Furnace

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 923 ◽  
Author(s):  
Anatoliy Golovchenko ◽  
Roman Dychkovskyi ◽  
Yuliya Pazynich ◽  
Cáceres Cabana Edgar ◽  
Natalia Howaniec ◽  
...  
Keyword(s):  
Experimental Study ◽  
Blast Furnace ◽  
Radial Distribution ◽  
Surface Texture ◽  
Technology Implementation ◽  
Gas Flow ◽  
Material Surface ◽  
Automated Control ◽  
Operating Blast Furnace ◽  
Operating Blast

The paper presents an experimental study on the formation process of burden surface texture on the blast furnace throat and its influence on the radial distribution of gas flow. The study was performed with the application of blast furnaces equipped with a bell-type charging device using radio-isotope means for the control of burden surface texture (profile) and burden surface level, i.e., gamma locators for burden surface texture. The study was carried out under the conditions of an operating blast furnace in an iron and steel plant using a unique GEOTAPS system for automated control of geometric and temperature parameters of burden material surface on the blast furnace throat. The influence of the surface texture on the gas flow distribution was also investigated. The possibility of a self-stabilization effect for burden surface texture and gas flow in an operating blast furnace under suitable conditions was experimentally proven. As a result of the experimental study performed, four ways of energy-saving technology implementation were determined for the control of blast furnace melting based on the data on the burden surface texture and previously unknown regularities of surface layer formation of burden material on the throat of an operating blast furnace with a bell-type charging device. The main idea of the paper is the development of automated control for the radial distribution of burden material and gas flow using actual or predicted surface texture parameters as important intermediate factors that both describe the process and have a significant simultaneous influence on it.

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

scholarly journals A Proposed Model for Detecting Learning Styles Based on Agent Learning

2016 ◽  
Vol 11 (10) ◽  
pp. 65 ◽  
Author(s):  
M S Hasibuan ◽  
L E Nugroho ◽  
P I Santosa ◽  
S S Kusumawardani
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Evaluation Model ◽  
Learning System ◽  
Learning Activities ◽  
Instructional Materials ◽  
Data Driven ◽  
Automatic Learning ◽  
Proposed Model ◽  
Agent Learning

A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.

scholarly journals Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data

2021 ◽  
pp. 147592172110448
Author(s):  
Xuyan Tan ◽  
Yuhang Wang ◽  
Bowen Du ◽  
Junchen Ye ◽  
Weizhong Chen ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Mechanical Analysis ◽  
Data Driven ◽  
Monitoring Data ◽  
Tunnel Structure ◽  
Mechanical Behaviors ◽  
Proposed Model ◽  
Full Face ◽  
Cross Test ◽  
Mechanical Constraint

Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this line, this study aims to develop a spatial deduction model to obtain the full-faced mechanical behaviors through integrating mechanical properties into pure data-driven model. The spatial tunnel structure is divided into many parts and reconstructed in a form of matrix. Then, the external load applied on structure in the field was considered to study the mechanical behaviors of tunnel. Based on the limited observed monitoring data in matrix and mechanical analysis results, a double-driven model was developed to obtain the full-faced information, in which the data-driven model was the dominant one and the mechanical constraint was the secondary one. To verify the presented spatial deduction model, cross-test was conducted through assuming partial monitoring data are unknown and regarding them as testing points. The well agreement between deduction results with actual monitoring results means the proposed model is reasonable. Therefore, it was employed to deduct both the current and historical performance of tunnel full face, which is crucial to prevent structural disasters.

scholarly journals Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Chenfei Shao ◽  
Chongshi Gu ◽  
Zhenzhu Meng ◽  
Yating Hu
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Elastic Modulus ◽  
Random Coefficient ◽  
Arch Dam ◽  
Data Driven ◽  
The Finite Element Method ◽  
Random Coefficient Model ◽  
Proposed Model ◽  
Element Method

Both numerical simulations and data-driven methods have been applied in dam’s displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and temporal correlations among all monitoring points into account, we took the first step toward integrating the finite element method into a data-driven model. As the data-driven method, we selected the random coefficient model, which can make each explanatory variable coefficient of all monitoring points following one or several normal distributions. In this way, explanatory variables are constrained. Another contribution of the proposed model is that the actual elastic modulus at each monitoring point can be back-calculated. Moreover, with a Lagrange polynomial interpolation, we can obtain the distribution field of elastic modulus, rather than gaining one value for the whole dam in previous studies. The proposed model was validated by a case study of the concrete arch dam in Jinping-I hydropower station. It has a better prediction precision than the random coefficient model without the finite element method.

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