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Volumn 8, Issue 15, 2008, Pages 2732-2738

Response surface methodology and genetic algorithm in optimization of cement clinkering process

Author keywords

Cement; Clinker; Experimental optimization; Genetic algorithm; Response surface methodology

Indexed keywords

ALUMINUM OXIDES; BURNING TEMPERATURE; CLINKER; CONTROL VARIABLE; EXPERIMENTAL OPTIMIZATION; RESPONSE SURFACE METHODOLOGY; SATURATION FACTORS; TRADITIONAL TECHNIQUES;

EID: 67649276934     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2008.2732.2738     Document Type: Article
Times cited : (19)

References (11)
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  • 2
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    • Correia, D.S.1    Goncoalves, C.V.2    da Cunha, S.S.3    Ferraresi, V.A.4
  • 3
    • 4344671271 scopus 로고    scopus 로고
    • Study on compatibility of Cement-super plasticizer assisted by multi criteria statistical optimization
    • Grabiec, A.M. and Z. Piasta, 2004. Study on compatibility of Cement-super plasticizer assisted by multi criteria statistical optimization. J. Mater. Process. Technol., 52: 197-203.
    • (2004) J. Mater. Process. Technol. , vol.52 , pp. 197-203
    • Grabiec, A.M.1    Piasta, Z.2
  • 5
    • 33645853496 scopus 로고    scopus 로고
    • Mean square error criteria to multi response process optimization by a new genetic algorithm
    • Koksoy, O. and T. Yalcinoz, 2006. Mean square error criteria to multi response process optimization by a new genetic algorithm. Applied Math. Computat, 175: 1657-1674.
    • (2006) Applied Math. Computat , vol.175 , pp. 1657-1674
    • Koksoy, O.1    Yalcinoz, T.2
  • 6
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    • Models relating mixture composition to the density and strength of Foam concrete using response surface methodology
    • Kunhanandan Nambiar, E.K. and K. Ramamurthy, 2006. Models relating mixture composition to the density and strength of Foam concrete using response surface methodology. Cem. Concr. Composites, 28: 752-760.
    • (2006) Cem. Concr. Composites , vol.28 , pp. 752-760
    • Kunhanandan Nambiar, E.K.1    Ramamurthy, K.2
  • 7
    • 33748077110 scopus 로고    scopus 로고
    • Modeling the compressive strength of molasses-cement sand system using design of experiments and back propagation neural network
    • Mandal, A. and P. Roy, 2006. Modeling the compressive strength of molasses-cement sand system using design of experiments and back propagation neural network. J. Mater. Process. Technol., 80: 167-173.
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    • Application of response surface methodology in the optimization of cutting conditions for surface roughness
    • Oktem, H., T. Erzurumlu and H. Kurtaran, 2005. Application of response surface methodology in the optimization of cutting conditions for surface roughness. J. Mater. Process. Technol., 70: 11-16.
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    • Determination of effecting dimensional parameters onwarpage of thin shell plastic parts using integrated response surface method and genetic algorithm
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.