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Influence of coke structure on coke quality using image analysis method 被引量:3
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作者 B.Ghosh b.k.sahoo +4 位作者 B.Chakrabortv K.K.Manjhi S.K.Das J.N.Sahu Atul K.Varma 《International Journal of Coal Science & Technology》 EI 2018年第4期473-485,共13页
The quality of coke affects the performance of the blast furnace, factors affecting coke quality include coal properties, coal charge granulometry and carbonization conditions. The coke properties in elude the size an... The quality of coke affects the performance of the blast furnace, factors affecting coke quality include coal properties, coal charge granulometry and carbonization conditions. The coke properties in elude the size analysis, cold strength (Micum Indices-M4(). MI0) and hot strength (Coke Reactivity Index-CRI, Coke Strength after Reaction-CSR) properties and structural properties such as coke structure and texture. Structural properties comprise the porosity, pore-cell wall thickness and pore sizes, while textures consist of the carbon forms in the coke. In present work, advanced method such as image analysis method was used to interpret coke microstructure. Conventional methods such as determination of coke porosity by measurement of real and apparent density and mercury porosimetry have a number of limitations. Coke size, magnification, number of image frames captured, process of pellet preparations and coke properties such as M4(), M|0, CRI and CSR (low, medium and high values) were taken as variables for experimental purposes. The coke structure parameters such as porosity, length, perimeter, breadth, roundness, pore-wall thickness and pore size distribution of the pores were determined by image analysis method. This method provided average porosity in addition to pore-wall thickness and pore-size distribution. The pore wall thickness measuremenl by image analysis method provided significant correlations with M40, CRI and CSR values. This explained the usability of image analysis for coke structure measurement. 展开更多
关键词 Image analysis COKE STRUCTURE POROSITY Micro STRUCTURE ROUNDNESS PORE WALL thickness
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Influence of tool rotational speed on mechanical and corrosion behaviour of friction stir processed AZ31/Al_(2)O_(3)nanocomposite 被引量:3
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作者 Ashish Kumar V.P.Singh +5 位作者 Akhileshwar Nirala R.C.Singh Rajiv Chaudhary Abdel-Hamid I.Mourad b.k.sahoo Deepak Kumar 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2023年第7期2585-2599,共15页
Nano-sized reinforcements improved the mechanical characteristics efficiently by promoting more implicit particle hardening mechanisms compared to micron-sized reinforcements.Nano-sized particles lessen the critical p... Nano-sized reinforcements improved the mechanical characteristics efficiently by promoting more implicit particle hardening mechanisms compared to micron-sized reinforcements.Nano-sized particles lessen the critical particle solidification velocity for swamp and thus offers better dispersal.In the present investigation,the friction stir processing(FSP)is utilized to produce AZ31/Al_(2)O_(3)nanocomposites at various tool rotation speeds(i.e.,900,1200,and 1500 rpm)with an optimized 1.5%volume alumina(Al_(2)O_(3))reinforcement ratio.The mechanical and corrosion behavior of AZ31/Al_(2)O_(3)-developed nanocomposites was investigated and compared with that of the AZ31 base alloy.The AZ31 alloy experienced a comprehensive dynamic recrystallization during FSP,causing substantial grain refinement.Grain-size strengthening is the primary factor contributed to the enhancement in the strength of the fabricated nanocomposite.Tensile strength and yield strength values were lower than those for the base metal matrix,although an upward trend in both values has been observed with an increase in tool rotation speed.An 19.72%increase in hardness along with superior corrosion resistance was achieved compared to the base alloy at a tool rotational speed of 1500 rpm.The corrosion currents(Jcorr)of all samples dropped with increase in the rotational speed,in contrast to the corrosion potentials(Ecorr),which increased.The values of Jcorr of AZ31/Al_(2)O_(3)were 42.3%,56.8%,and 65.5%lower than those of AZ31 alloy at the chosen rotating speeds of 900,1200,and 1500 rpm,respectively.The corrosion behavior of friction stir processed nanocomposites have been addressed in this manuscript which has not been given sufficient attention in the existing literature.Further,this work offers an effective choice for the quality assurance of the FSP process of AZ31/Al_(2)O_(3)nanocomposites.The obtained results are relevant to the development of lightweight automobile and aerospace structures and components. 展开更多
关键词 Friction stir processing AZ31 alloy Al_(2)O_(3) NANOCOMPOSITE Mechanical properties Corrosion resistance
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Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment 被引量:5
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作者 b.k.sahoo S.De B.C.Meikap 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2017年第2期379-386,共8页
Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheol... Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model. 展开更多
关键词 Microwave pre-treatment Coal-water slurry Apparent viscosity Artificial neural network Back propagation algorithm
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