Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

2. Intelligent Optimization and Control of Raw Material Proportioning Processes

verfasst von : Min Wu, Weihua Cao, Xin Chen, Jinhua She

Erschienen in: Intelligent Optimization and Control of Complex Metallurgical Processes

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The proportioning of raw materials in a complex metallurgical process contains a coal blending process and an iron ore sintering process.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Introduction
Nächstes Kapitel Intelligent Optimization and Control of Coking Process
Literatur
1.
Hashimoto S (1989) Coke in the blast furnace: property requirements and how to realize them. In: Proceeding of the symposium on coke properties required by the blast furnace for stable operation, pp 1–22
2.
Miura Y, Okuhara T, Nishi T, Yamaguchi T, Haraguchi H (1979) Study on coal blending theory. Seitetsu Kenkyu 299:13060–13074
3.
Shi MY (1989) A practical method for predicting coke quality. Fuel Chem Eng 20(3):114–117
4.
Wen ZM, Wu M, Shen DY (1994) Design and implementation of a computer control system for the coal blending process. Autom: Theory, Tech Appl 2:610–613
5.
Yang G, Fan X, Chen X, Huang X, Li Z (2016) Intelligent control of grate-kiln-cooler process of iron ore pellets using a combination of expert system approach and Takagi-Sugeno fuzzy model. J Iron Steel Res, Int 23(5):434–441CrossRef
6.
Fan X, Wang Y, Chen X (2012) Mathematical models and expert system for grate-kiln process of iron ore oxide pellet production. Part II: rotary kiln process control. J Cent South Univ 19(6):1724–1727CrossRef
7.
Åström KJ, Anton JJ, Årzen KE (1986) Expert control. Automatica 22(3):277–286CrossRef
8.
Efstathiou J (1989) Expert systems in process control. Longman, Essex
9.
Ishizuka M, Kobayashi S (1991) Expert systems. Maruzen, Tokyo
10.
Liebowitz J (1955) Expert systems: a short introduction. Eng Fract Mech 50:601–607CrossRef
11.
Garg H (2016) A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Appl Soft Comput 38(C):988–999CrossRef
12.
Narendra KS, Parthasarathy K (1990) Identification and control of dynamic system using neural networks. IEEE Trans Neural Netw 1(1):4–27CrossRef
13.
Su HT, McAvoy TJ (1997) Artificial neural networks for nonlinear process identification and control. Henson MA, Seborg DE. Nonlinear process control. Upper Saddle River: Prentice Hall PTR
14.
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. Rumelhart DE, Mecclellanl JL, Parallel data processing. MIT Press, Cambridge
15.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366CrossRef
16.
Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing, Boston
17.
Ghosh A, Chatterjee A (2008) Ironmaking and steelmaking: theory and practice. Rajkamal Electric Press, New Delhi
18.
Frolov YA, Polotskii LI (2015) Three-dimensional mathematical (dynamic) model of the sintering process. Part I. Metallurgist 58(11–12):1071–1079CrossRef
19.
Frolov YA, Polotskii LI (2015) Three-dimensional mathematical (dynamic) model of the sintering process. Part II. Metallurgist 59(1–2):9–15
20.
Wu M, Chen XX, Cao WH, She JH, Wang CS (2014) An intelligent integrated optimization system for the proportioning of iron ore in a sintering process. J Process Control 24(1):182–202CrossRef
21.
Zhao JP, Loo CE, Dukino RD (2015) Modelling fuel combustion in iron ore sintering. Combust Flame 162(4):1019–1034CrossRef
22.
Kawaguchi T, Yoshinaga M (1987) Development and application of an integrated simulation model for iron ore sintering. Iron Mak Proc 46:99–106
23.
Song Q, Wang AM (2009) Simulation and prediction of alkalinity in sintering process based on grey least squares support vector machine. Int J Iron Steel Res 16(5):1–6CrossRef
24.
Wang R, Wang AM, Song Q (2012) Research on the alkalinity of sintering process based on LS-SVM algorithms. Adv Intell Soft Comput 168:449–454CrossRef
25.
Han HG, Qiao JF (2012) Prediction of activated sludge bulking based on a self-organizing RBF neural network. J Process Control 22(6):1103–1112MathSciNetCrossRef
26.
Wang JS, Wang W (2006) A predictive model of sinter chemical composition and its application. In: Proceedings of the 6th world congress on intelligent control and automation, pp 4856–4860
27.
Wang B, Yang B, Sheng J, Chen M, He G (2009) An improved neural network algorithm and its application in sinter cost prediction. In: Second international workshop on knowledge discovery and data mining, pp 112–115
28.
Chen XX, Chen X, She JH, Wu M (2017) A hybrid just-in-time soft sensor for carbon efficiency of iron ore sintering process based on feature extraction of cross-sectional frames at discharge end. J Process Control 54:14–24CrossRef
29.
Chen XX, Chen X, She JH, Wu M (2017) A hybrid time series prediction model based on recurrent neural network and double extreme learning machine for carbon efficiency prediction in iron ore sintering process. Neurocomputing 292:128–139CrossRef
30.
Chen XX, Chen X, She JH, Wu M (2017) Hybrid multistep modeling for calculation of carbon efficiency of iron ore sintering process based on yield prediction. Neural Comput Appl 28(6):1193–1207CrossRef
31.
Zheng DL, Zhao ZL (2000) Application of gray linear programming in sintering mixing calculation. J Univ Sci Technol Beijing 7(4):273–276 (In Chinese)
32.
Zhang JH, Xie AG, Shen FM (2007) Multi-objective optimization and analysis model of sintering process based on BP neural network. Int J Iron Steel Res 14(2):1–5CrossRef
33.
Chen X, Chen XX, Wu M (2016) Modeling and optimization method featuring multiple operating modes for improving carbon efficiency of iron ore sintering process. Control Eng Pract 54:117–128CrossRef
34.
Giri BK, Roy GG (2012) Mathematical modeling of iron ore sintering process using genetic algorithm. Ironmak Steelmak 39(1):59–66CrossRef
35.
Zhang H, Zhu YL, Zou WP, Yan XH (2012) A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production. Appl Math Model 36(6):2578–2591CrossRef
36.
Liu C, Xie Z, Sun F, Chen L (2016) Optimization for sintering proportioning based on energy value. Appl Therm Eng 103:1087–1094CrossRef
37.
Yu Z, Xiao L, Li H, Zhu X, Huai R (2017) Model parameter identification for lithium batteries using the coevolutionary particle swarm optimization method. IEEE Trans Ind Electron 64(7):5690–5700CrossRef
38.
Lu XJ, Li HX, Yuan X (2010) PSO-based intelligent integration of design and control for one kind of curing process. J Process Control 20(10):1116–1125CrossRef
39.
Yu G, Chai TY, Luo XC (2011) Multiobjective production planning optimization using hybrid evolutionary algorithms for mineral processing. IEEE Trans Evol Comput 15(4):487–514CrossRef
40.
Yildiz AR, Solanki KN (2012) Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach. Int J Adv Manuf Technol 59(1–4):367–376CrossRef
Metadaten
Titel
Intelligent Optimization and Control of Raw Material Proportioning Processes
verfasst von
Min Wu
Weihua Cao
Xin Chen
Jinhua She
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Intelligent Optimization and Control of Complex Metallurgical Processes

Print ISBN: 978-981-15-1144-8

Electronic ISBN: 978-981-15-1145-5

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-1145-5_2

Neuer Inhalt

    Bildnachweise