The Steel Industry. International Comparisons of Industrial Structure and Performance.

1975 ◽  
Vol 85 (338) ◽  
pp. 428
Author(s):  
G. F. Ray ◽  
A. Cockerill ◽  
A. Silberston
Keyword(s):  
Steel Industry ◽  
Industrial Structure ◽  
International Comparisons ◽  
And Performance

Computer Vision-Based Non-Magnetic Object Detection on Moving Conveyors in Steel Industry through Differential Techniques and Performance Evaluation

2014 ◽  
pp. 247-261
Author(s):  
K. C. Manjunatha ◽  
H. S. Mohana ◽  
P. A. Vijaya
Keyword(s):  
Object Detection ◽  
Steel Industry ◽  
Material Handling ◽  
Raw Materials ◽  
Industrial Applications ◽  
Raw Material ◽  
Smart Control ◽  
Image Pixels ◽  
And Performance ◽  
Video Input

Intelligent process control technology in various manufacturing industries is important. Vision-based non-magnetic object detection on moving conveyor in the steel industry will play a vital role for intelligent processes and raw material handling. This chapter presents an approach for a vision-based system that performs the detection of non-magnetic objects on raw material moving conveyor in a secondary steel-making industry. At single camera level, a vision-based differential algorithm is applied to recognize an object. Image pixels-based differential techniques, optical flow, and motion-based segmentations are used for traffic parameters extraction; the proposed approach extends those futures into industrial applications. The authors implement a smart control system, since they can save the energy and control unnecessary breakdowns in a robust manner. The technique developed for non-magnetic object detection has a single static background. Establishing background and background subtraction from continuous video input frames forms the basis. Detection of non-magnetic materials, which are moving with raw materials, and taking immediate action at the same stage as the material handling system will avoid the breakdowns or power wastage. The authors achieve accuracy up to 95% with the computational time of not more than 1.5 seconds for complete system execution.

Computer Vision-Based Non-Magnetic Object Detection on Moving Conveyors in Steel Industry Through Differential Techniques and Performance Evaluation

2018 ◽  
pp. 1820-1837
Author(s):  
K. C. Manjunatha ◽  
H. S. Mohana ◽  
P. A. Vijaya
Keyword(s):  
Object Detection ◽  
Steel Industry ◽  
Material Handling ◽  
Raw Materials ◽  
Industrial Applications ◽  
Raw Material ◽  
Smart Control ◽  
Image Pixels ◽  
And Performance ◽  
Video Input

Intelligent process control technology in various manufacturing industries is important. Vision-based non-magnetic object detection on moving conveyor in the steel industry will play a vital role for intelligent processes and raw material handling. This chapter presents an approach for a vision-based system that performs the detection of non-magnetic objects on raw material moving conveyor in a secondary steel-making industry. At single camera level, a vision-based differential algorithm is applied to recognize an object. Image pixels-based differential techniques, optical flow, and motion-based segmentations are used for traffic parameters extraction; the proposed approach extends those futures into industrial applications. The authors implement a smart control system, since they can save the energy and control unnecessary breakdowns in a robust manner. The technique developed for non-magnetic object detection has a single static background. Establishing background and background subtraction from continuous video input frames forms the basis. Detection of non-magnetic materials, which are moving with raw materials, and taking immediate action at the same stage as the material handling system will avoid the breakdowns or power wastage. The authors achieve accuracy up to 95% with the computational time of not more than 1.5 seconds for complete system execution.

scholarly journals Design and performance of a coarse media, high hydraulic load polishing wetland for steel industry wastewater

2019 ◽  
Vol 80 (1) ◽  
pp. 59-66
Author(s):  
Viet Anh Nguyen ◽  
Minh Phuong Nguyen ◽  
Karin Tonderski ◽  
Hai Do Thi ◽  
Anh Thi Kim Bui
Keyword(s):  
Flow Rate ◽  
Steel Industry ◽  
Oxygen Demand ◽  
Head Loss ◽  
Design Flow ◽  
Total System ◽  
Pilot Test ◽  
Removal Efficiencies ◽  
And Performance ◽  
Industry Wastewater

Abstract This paper presents the design of a constructed wetland (CW) system in an area with limited land availability, resulting in high hydraulic loads. The CW was constructed to act as a buffering/ polishing step after stabilization ponds for steel industry wastewater post-treatment. A pilot test with two different filter media (50–100mm vs 40–60mm diameter) indicated that a flow rate increase from 49.5 m3/h to 122.4 m3/h would lead to a head loss increase from 2.9cm to 8.7cm, and more than double that for the finer gravel. This was substantially higher than the calculated theoretical values, though the relation with flow rate was similar. Four full scale wetland cells (CW1, CW2, CW3 and CW4) were constructed using the coarser gravel. A design value of total head loss of 1.01m over the total system length, with a design flow of 36,000 m3/day, was expected based on pilot test results. During the first operation year (September 2017 to July 2018), the pond-CW system has received wastewater already meeting required discharge standards. The effluent from the CWs had consistently lower concentrations of all measured variables, and met the predicted values for biochemical oxygen demand (BOD5), total nitrogen (TN) and NH4+-N. Highest removal efficiencies were achieved for NH4+-N (>90%), Mn (>60%) and Fe (45%) with removal efficiencies for TN (14%), BOD5 and chemical oxygen demand (COD) (around 30%). Concentrations of phenol, CN− and Cr6+ were below 10, 4 and 3μg/l, respectively, in in- and outflows. An appreciated benefit of the wetland was the ‘green element’ in the industrial landscape.

Industrial Structure and Performance

2020 ◽  
pp. 105-112
Author(s):  
Chung-Shing Lee ◽  
Michael Pecht
Keyword(s):  
Industrial Structure ◽  
And Performance

scholarly journals Analysis of Industrial Structure, Firm Conduct and Performance – A Case Study of the Textile Industry

2016 ◽  
Vol 16 (2) ◽  
pp. 35-42 ◽  
Author(s):  
Yueh-Chiang Lee ◽  
Yao-Hung Yang
Keyword(s):  
Market Structure ◽  
Textile Industry ◽  
Industrial Structure ◽  
Random Effect ◽  
Capital Intensity ◽  
Ordinary Least Squares ◽  
Performance Model ◽  
Entry Barrier ◽  
And Performance ◽  
Firm Conduct

AbstractWith the analysis of the industrial economic theory structure – conduct – performance model, the study investigates the existence of significant relationship among market structure, conduct and performance. Twelve Taiwan companies are studied during the study period from 2006 to 2012 which are analysed with fixed effect and random effect of panel data and ordinary least squares estimation. The empirical result backs the statement by “Structuralism” that market structure (market share, entry barrier and capital intensity) directly affects firm conduct (R&D intensity) and performance (ROA).

scholarly journals A structure analysis of Chinese iron and steel industry based on Jingyou evaluation of performances of listed companies

2020 ◽  
Vol 214 ◽  
pp. 02020
Author(s):  
Xinan Zhao ◽  
Jongho Kim ◽  
Gyongsu Pak
Keyword(s):  
Strategic Management ◽  
Structural Feature ◽  
Structure Analysis ◽  
Steel Industry ◽  
Industrial Structure ◽  
Management Practice ◽  
Listed Companies ◽  
Iron And Steel Industry ◽  
Iron And Steel ◽  
Corporate Strategic Management

With the value ideology of modern strategic management, a new industrial structure analysis method was proposed based on the Jingyou theory rather than the traditional competitive mechanism in this paper, and then it was carried out to apply it to the performance data of the listed companies in the first quarter of 2017 in order to explain the structural feature of Chinese iron and steel industry. Such analysis results as the distributive feature of enterprise groups in industry, the main developing mode of industry, the identification of benchmarks and partners will be helpful to the detailed explanation of structural feature of industry under the objective law of development of things and contribute to solve some important problems in the modern corporate strategic management practice.

scholarly journals Research on Carbon Productivity and It's Spatial Convergence of Steel Industry in China

2021 ◽  
Author(s):  
Xiping Wang ◽  
Rong Tang
Keyword(s):  
Steel Industry ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Conditional Convergence ◽  
Growth Trend ◽  
Average Value ◽  
Spatial Convergence ◽  
Convergence Trend ◽  
Convergence Model ◽  
Carbon Productivity

Abstract The Global-Malmquist-Luenberger (GML) index was applied to analyse the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2017, and then the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) The overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with EC, TC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

Sign in / Sign up

Export Citation Format

Share Document