A Causal Model-Based Scheduling Approach for Coke Oven Gas System in Steel Industry

被引：3

作者：

Jin, Feng ^{[1
]}

Lv, Zheng ^{[1
]}

Li, Maohua ^{[2
]}

Mou, Lei ^{[2
]}

Zhao, Jun ^{[1
]}

Wang, Wei ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian, Peoples R China

[2] Shanghai Baoneng Informat Technol Co Ltd, Shanghai, Peoples R China

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 21期

关键词：

Coke oven gas system; Causal model; Scheduling; OPTIMIZATION;

D O I：

10.1016/j.ifacol.2018.09.384

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Reasonable scheduling of coke oven gas (COG) in steel industry will save fossil energy resources and increase gas consumption efficiency. In this study, a causal model-based scheduling approach is proposed to provide guidance for the coke oven gas scheduling. The causal relationship of the variables related to the gas tank level is discovered and the causal diagram is established, according to which the training sample is constructed and the predicting model of gas tank level is trained. Then, an objective function that considers the scheduling solution and its result is designed. To calculate the most reasonable solution, a modified particle swarm optimization algorithm is employed. The validation experiments are carried out by using practical data coming from a steel plant in China, where the gas tank level approaching the higher and the lower level zones are both considered. The human experience -based method and a data based scheduling one are conducted as comparative studies. The results indicate that the proposed method is efficient for COG scheduling. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：7 / 12

页数：6

共 19 条

[1] Combining a causal effect criterion for evaluation of facial attractiveness models [J].

Chen, Fangmei ;

Zhang, David .

NEUROCOMPUTING, 2016, 177 :98-109

[2] Proportional-integral-derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm [J].

Civelek, Zafer ;

Cam, Ertugrul ;

Luy, Murat ;

Mamur, Hayati .

IET RENEWABLE POWER GENERATION, 2016, 10 (08) :1220-1228

[3] Reliable Knowledge Discovery with A Minimal Causal Model Inducer [J].

Dai, Honghua ;

Keble-Johnston, Sarah ;

Gan, Min .

12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012), 2012, :629-634

[4] Using causality modeling and Fuzzy Lattice Reasoning algorithm for predicting blood glucose [J].

Fong, Simon ;

Mohammed, Sabah ;

Fiaidhi, Jinan ;

Kwoh, Chee Keong .

EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (18) :7354-7366

[5] The Compact Genetic Algorithm is Efficient Under Extreme Gaussian Noise [J].

Friedrich, Tobias ;

Koetzing, Timo ;

Krejca, Martin S. ;

Sutton, Andrew M. .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2017, 21 (03) :477-490

[6]

Hai R, 2013, 2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), P1246, DOI 10.1109/ICInfA.2013.6720485

[7] A two-stage method for predicting and scheduling energy in an oxygen/nitrogen system of the steel industry [J].

Han, Zhongyang ;

Zhao, Jun ;

Wang, Wei ;

Liu, Ying .

CONTROL ENGINEERING PRACTICE, 2016, 52 :35-45

[8]

Kohler M, 2016, IEEE C EVOL COMPUTAT, P2518, DOI 10.1109/CEC.2016.7744102

[9]

Mohan K., 2014, ADV NEURAL INFORM PR, V2, P1520

[10]

Pearl J., 2000, MODELS REASONING INF, V19

← 1 2 →