Analysis of the Influence of Blaine Numbers and Firing Temperature on Iron Ore Pellets Properties Using RSM-I-Optimal Design: An Approach Toward Suitability

• Published in: Minerals & metallurgical processing 2020-10, Vol.37 (5), p.1703-1716
• Main Authors: Prasad, Rakesh, Venugopal, R., Kumaraswamidhas, L. A., Pandey, Chandan, Pan, S. K.
• Format: Article
• Language: English
• Subjects: Blast furnace practice; Compressive strength; Density; Design of experiments; Design optimization; Engineering; Fineness; Firing; Hematite; Influence; Iron compounds; Iron ores; Materials Engineering; Metallic Materials; Mineral Resources; Pellets; Porosity; Response surface methodology; Statistical analysis
• ISSN: 2524-3462; 2524-3470
• DOI: 10.1007/s42461-020-00282-x

Cold compressive strength (CCS) and apparent porosity (AP) of pellets are essential properties regarding the burden of blast furnaces. The optimal required values for industries are 2.5 KN CCS and 21% AP. Blaine fineness and firing temperature are the decisive parameters analyzed here. During firing, microstructure phases such as silicate, hematite, and pore are formed. The phase’s grain density during induration influences the pellet’s CCS and AP. The influence of the factors over the response is evaluated by I-optimal (response surface methodology) design. Blaine fineness, firing temperature, pore phase grain density (PPGD), silicate phase grain density (SPGD), and hematite phase grain density (HPGD) affect the CCS and AP of pellets. The RSM-I-optimal predicted responses with a coefficient of determination (R 2 ) of 0.89 for CCS and 0.91 for AP of iron ore pellets. Optimal conditions were Blaine no. = 1668 cm 2 /g, 1250 °C firing temperature, PPGD value 90 no/mm 2 , SPGD value 250 no/mm 2 , and HPGD value 490 no/mm 2 , with 4.5 KN CCS 27%. The induration processes were investigated through statistical design of experiments for process optimization. Statistical analysis indicated the direct and interactive influence over CCS and AP. In this study, RSM-I-optimal modeling is used for iron ore pellets processing. The I-optimal prediction could be applied to make the process economical because information is obtained through fewer experiments.