Construction of Energy Scheduling Model for Iron and Steel Enterprises Based on Big Data

2021 ◽  
pp. 423-430
Author(s):  
Yanyan Jia ◽  
Chunxu Jiang ◽  
Jiping Yang ◽  
Huajun Cao ◽  
Li Li
Keyword(s):  
Big Data ◽  
Iron And Steel ◽  
Scheduling Model

Steelmaking-Hot Rolling Scheduling Model and Method for Integrated Management in Iron and Steel Enterprises

2013 ◽  
Vol 860-863 ◽  
pp. 3094-3099 ◽  
Author(s):  
Bao Lin Zhu ◽  
Shou Feng Ji
Keyword(s):  
Production Scheduling ◽  
Integrated Management ◽  
Steel Production ◽  
Optimal Solution ◽  
Scheduling Problems ◽  
Planning And Scheduling ◽  
Iron And Steel ◽  
Scheduling Model ◽  
General Production ◽  
Iron And Steel Production

Iron and steel production scheduling problems are different from general production scheduling in machine industry. They have to meet special demands of steel production process. The CCR production manner dramatically promotes the revolution in technology and management, especially to planning and scheduling. In this paper, a scheduling model is presented to integrate the three working procedures and the lagrangian relaxation technology is proposed to get the optimal solution of the scheduling model. Finally, numerical examples are given to demonstrate the effectiveness of the integrated model and method.

GAP-Like Scheduling Model of Byproduct Gas in Iron and Steel Process

2012 ◽  
Vol 217-219 ◽  
pp. 505-510
Author(s):  
Yong Liang Zhou
Keyword(s):  
Dynamic Scheduling ◽  
Energy Management System ◽  
External Energy ◽  
Iron And Steel ◽  
Intelligent Optimization ◽  
Scheduling Model ◽  
Byproduct Gas ◽  
Production And Consumption ◽  
Intelligent Optimization Algorithm ◽  
Gas System

Gas is a key byproduct of the iron and steel process, and the scheduling of gas is the most valuable one in Energy Management System. The production and consumption of the byproduct gas will be related to many sub-processes and tends to encounter imbalance problems. One GAP-like optimization model of gas scheduling is setup, where there are 3 key objectives, minimization of emission, external energy purchasing and instability of the byproduct gas system. The model is NP-Hard and can be find the solution by using intelligent optimization algorithm to realize the static and dynamic scheduling.

scholarly journals An Acquisition On Big Data Model For Quality Tracing Of Iron And Steel Industries

2019 ◽  
Vol 8 (10) ◽  
pp. 1780-1783
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Data Model ◽  
Product Life Cycle ◽  
Manufacturing System ◽  
Production Environment ◽  
General Process ◽  
Iron And Steel ◽  
Data Production ◽  
Steel Industries

Quality determine is essential affair for steel industries. Due to complication and variation of nature input that turn to be changed into many forms. Due to this, it is tough to explicit the report and trace over the whole product life cycle from designing, construction, etc. According to big data approach, study of the essence of steel brand and the factor of their manufacturing system and it is effective viable multi row system which consists of four structure , [1]the basis quality bill of material [BQBOM] ,[2]the general process bill of material[GPBOM],[3]the production and scheduling bill of material[PSBOM] ,[4]the final quality bill of material[FQBOM]. This mode would be useful to builders to frame a kind of scheme in big data production environment

scholarly journals Fuzzy Byproduct Gas Scheduling in the Steel Plant Considering Uncertainty and Risk Analysis

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2727
Author(s):  
Xueying Sun ◽  
Zhuo Wang ◽  
Jingtao Hu
Keyword(s):  
Confidence Level ◽  
Optimal Scheduling ◽  
Iron And Steel ◽  
Fuzzy Variables ◽  
Confidence Levels ◽  
Scheduling Model ◽  
Byproduct Gas ◽  
Gas System ◽  
Risk Cost ◽  
Operation Cost

In the iron and steel enterprises, efficient utilization of byproduct gas is of great significance for energy conservation and emission reduction. This work presents a fuzzy optimal scheduling model for byproduct gas system. Compared with previous work, uncertainties in byproduct gas systems are taken into consideration. In our model, uncertain factors in byproduct systems are described by fuzzy variables and gasholder level constraints are formulated as fuzzy chance constraints. The economy and reliability of byproduct gas system scheduling are sensitive to different confidence levels. To provide a reference for operators to determine a proper confidence level, the risk cost is defined to quantify the risk of byproduct gas shortage and emission during the scheduling process. The best confidence level is determined through the trade-off between operation cost and risk cost. The experiment results demonstrated that the proposed method can reduce the risk and give a more reasonable optimal scheduling scheme compared with deterministic optimal scheduling.

scholarly journals Optimization of Big Data Scheduling in Social Networks

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 902 ◽  
Author(s):  
Weina Fu ◽  
Shuai Liu ◽  
Gautam Srivastava
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Communication ◽  
Data Scheduling ◽  
Scheduling Model ◽  
Entropy Measures ◽  
Network Big Data ◽  
Data Collision ◽  
Target Data

In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.

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