Furnace operation optimization with hybrid model based on mechanism and data analytics

The operation optimization problem of furnace process (OOPFP) is to obtain an optimal setting of furnace temperatures to make the reheated slabs have suitable temperature distribution with minimum energy consumption and oxide loss. Since the furnace process is complex, dynamic, and nonlinear, it is...

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Bibliographic details
Volume:	23
Main Author:	Xia, Qiong Wang, Xianpeng Tang, Lixin
Format:	Journal Article
Language:	English
Zielgruppe:	Academic
Place of publication:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2019 SPRINGER Springer Springer Nature B.V
published in:	Soft computing (Berlin, Germany) Vol. 23; no. 19; pp. 9551 - 9571
Editor:	Chen, Mu-Yen Pham, Hoang Mercaldo, Francesco Lu, Huimin Sangaiah, Arun Kumar
ORCID:	0000-0001-8132-9446 0000-0002-9950-5169
Data of publication:	20191000
ISSN:	1432-7643 1433-7479
EISSN:	1433-7479
Discipline:	Engineering Computer Science
Subjects:	Engineering Computational Intelligence Operation optimization Reheating furnace Control, Robotics, Mechatronics Least squares support vector machine Artificial Intelligence Differential evolution Hybrid modeling Mathematical Logic and Foundations Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Technology Computer Science Science & Technology Support vector machines Temperature distribution Energy consumption Data analysis Temperature Algorithms Energy distribution Evolutionary computation Model accuracy Analytics Optimization PARTICLE SWARM OPTIMIZATION Operation optimization Reheating furnace DIFFERENTIAL EVOLUTION ALGORITHM Least squares support vector machine Differential evolution Hybrid modeling SUPPORT VECTOR MACHINE PARAMETERS SELECTION
Online Access:	Fulltext
Database:	Web of Science - Science Citation Index Expanded - 2019 Web of Knowledge Science Citation Index Expanded Web of Science CrossRef Academic OneFile (A&I only) Database information Databases - DBIS