An attempt has been made to quantify the effects of the variables on strength in sintering of iron ore/blue dust received from Bailadila, NMDC. For this, the method of design 2n factorial  experiment  is used to obtain a response surface equation.

   

   Results indicate if the coke  breeze increases in the range of variation of the  variable  in  sintering,  the strength increases but strength decreases when  moisture increases from lower level to  higher  level. Blue  dust 

does no effect the strength.



The optimum levi (-4: variables for the maximon strength of the sinter produ(A is determined  by  the  method  of hexagonal design of experjnent. The results indicate that

for a basicity of 1.9 an acceptable quality of sinter can be produced. Blue dust amounting to 40.0% of all the iron ore  fines taken together  can be incorporated in the raw mixture without  imparting  the strength  of the finished sinter

Chakravorty, N

Maulik, S C

Patnaik, H

Sil, S K

English

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OPTIMISATION OF PROCESS VARIABLES IN SINTERING THROUGH STATISTICAL DESIGN OF EYPEflIMENTS. SIL, s.C. MAULU, H. PATNAIK & N.CHAKRAVORTY. MINERAL PROCESSING DIVISION NATIONAL METALLURGICAL LABORATORY JAMSHEDPUR - 831 007> AB 	 TRACT An attempt has been made to quantify the effects of the variables on strength in sintering of iron ore/blue dust received from Bailadila, NMDC. For this, the methto of design 2n factorial exper.ment is used to obtain a res-ponse surface equation. Results indicate if the coke breeze increase` the range of variation of the variable in sintering, the strength increases but strength decreases when moisture in-creases from lower level to higher level. Blue dust does not effect the strength. The optimum levi (-4: variables for the maximon strength of the sinter produ(A is determined by the method of hexagonal design of experjnent. The results indicate that for a basicity of 1.9 an acceptable quality of sinter can be produced. Blue dust amounting to 40.0% of all the iron ore fines taken together can be incorporated in the raw mixture without imparting the strength of the finished sinter. INTRODUCTION In spite of knowing effect of all the additive but there is lack of information regarding the quantitative effect of the variables on strength properties. The number of processing variables which effect the properties are many and consequently complO]e interactions results due Lo these variables interacting with each other. In the conven tional method single factor experiments are planned when each is varied keeping other at constant level of variables, To reach an optimum level of variables, for maximum proper-ties one has to perform a large number of tests even when the optimum combination is not always guaranted. Under this circumstance, use of statistical design of experiments is extremely helpful. By performing fewer experiments in a planned manner, one can reach the optimum combination of variables in shorter time, thus saving considerable labour and cost. The most important factors of the raw materials ...02... 02 	 : quality which effect the sini—r strength are a) coke content in the raw mix b) Quality of 5'co ore fines content in the raw mix c) Quality of llmestone fines content in the raw mix d) Amount of dolomite fines, mill scale, flue dust etc. With high,  addition of coke breeze strength gets increased along with Feu content. Addition of higher amount of blue dust keeps A1203 within 7.0%. Limestone added for fluxed sinter. It also increases permeability and yone of slag. DolomIte prevents formation of faylite. increases permeability and strength. Based on this information and the single factor experiments, the base level for coke breeze, blue dust and moisture was decided. 4ince these variables interact with each other in complex manner, it was thought to develop a regression equation connecting the input variables like coke breeze, blue dust and moisture with the strength va-lues for the base level sinter product. For working out the optimum treatment combination from their regression equatic experiments were performed b, hexagonal design of experiment. The optimum treatment combination thus obtained was employed for sintering. 03 	 : W ork Plan  The work illustrates the statistical design and analysis of a sintering process through a systematic approach in the following ineS a) 	 Factorial design b) Sintering tests according to the matrix of the design. c) Development of a mathematical model - re- gression equation. d) Test of significant co-efficients and ade- quacy of the equation. e) plots dram to see the effects of the va- riables and their interactions. f) Orthogonal design - hexagonal type g) Optimisation a) 	 Factorial Design Many experimenial situation require the exa-mination of effects of varying two or more variables. IL is shown that in a complete exploration of such situation it is not sufficient to vary one factor at a time, but that all combination of the different factor levels must be examined in order to elucidate the effect of each fac-tor and the possible ways in which each factor may be mo-dified by the variation of the others. In the analysis of ...04,.. Table I: 11.••••,-.70. 23 Factorial Design, 04 the experimental results, the effect of the each factor. can be determined with the same accuracy as if only one factor had been varied at a time and the interaction effect,,1 between the factors can also be evaluated. Here experiment in which each factor is tested at two levels. A 23 full flrtorial experiment was design as shown in Table I and 2 with low and high levels of the variables as indicated. 105. A = Coke breeze 5.5 7.5 B = Blue dust 20.0 40.0 C = Moisture 6.5 7.5 ...05... 05 Table 2: 	 Matrix of the experiments Test No. % Coke breeze (A) % Blue dust (B) % Moisture (C) 1 5.0 (-1) 20.0 (-1) 6.5 (-1) 2 7.0 (+1) 20.0 (-1) 6.5 (-1) 3 5.0 (-1) 40.0 (+1) 6.5 (-i) 4 7.0 (+1) 40.0 (+1) 6.5 (-1) 5 5.0 (-1) 20.0 (-1) 7.5 (+1) 6 7.0 (+1) 20.0 (-1) 7.5 (+1) 7 5.0 (-1) 40.0 (+1) 7.5 (+1) 8 7.0 (i-1) 40.0 (+1) 7.5 (+1) 9 6.0 (0) 30.0 (0) 7.0 (0) 10 6.0 (0) 30.0 (0) 7.0 (o) ii 6.0 (0) 30.0 	 (o) 7.0 (0) Sintering experiments : The raw mix for sintering was made of the ir-" ore fines, blue dust, limestone, dolomite, lime, Mn ore, 1111.11- scale flue dust, coke breeze, water and return fines. The amoun% of variables were used according to the matrix of the experiment design. A computer programming was developed to calculate the ...06... 06 weight percentage of each ingvpdient in the raw mix. The same  programming was utilised for calculation of raw mixt:tire composition under different conditions of operating variables. Ignition and process of sintering The wet mix was then poured into the sin-ter box and filled upto the tvp level. Before pouringke. of -25.1.15mm size sinter was poured into the box and used bedding layer. The gas burner was ignited over the raw ni surface and the suction fan switched on. The igniting f1.:,10- was held for two minutes and thenput out. The suction deOn,  the ignition was maintained at 500-800mm of WG and then in-creased to 1000mm of WG as soon as the burner was put out. The exhaust temperature and suction were noted every half a minute. The temperature of tit• wind box gradually noted and ricked a maximum and started decreases indicating the co,Ve-tion of the sintering. The sinter was then allowed to cool under sucticn till the temperature of exhaust gas dropped to 1000 C. The sinter block was then taken out and broken to -50mm size. The quality of -50+10mm size material was weighed and taken as the finished sinter. The quantity of -10mm size material was also weighed mul taken as return fines produced to be used for the next tests. .•.07..• 07 Tumbling test 11.3 kg. of -50+10mm sits sinter was tumb-led for 200 revolutions in a 91.4 al' - diameter X 45.7 cm width drub with two Nos. 5 cm high lifters, fitted diametri-cally opposite to each other and rotating at 25 rpm. The ma-terial was then screened on a 1,/4th in screen and the under-size was passed again through a 28 mesh screen,f)ercentage incle)4 of -1-1/4 mm. size fraction was taken as the tumbling and percentage -28 mesh size fraction as the abrasion index. The tumbling index was used as the main cri-terion for comparing the qualities of sinter produced under various conditions. The values of the tumbling index, effects and co-efficients of variables are given in Table 3. 08 Table 3: 	 Calculation of effects and coefficients Res2211% Tumblin Index SI. No. Tumbling Index Response % T.D.- 60 Variable Co-effi- cient Effect on% I. 64.5 4.5 Mean 63.625 63.625 2. 66.5 6.5 A 1450 2.900 3. 62.2 2.2 B - 0.025 - 0.050 4. 64.4 4.4 AB - 0.300 - 0.600 5. 59.3 - 0.7 C - 0.775 - 1.550 6. 64.3 4.3 AC 0.400 0.800 7. 62.7 2.7 BC 1.075 2.150 8. 65.1 5.1 ABC - 0.350 - 0.7n" $o the regression equation can be developed in this form. = 63.625+1.45(A) -0.025(B) -0.30(AB) Ystrength -0.775(C)+0.4(AC)+1.075(BC)-0.35(ABC). 09 	 • Test for the,...9.12saIceoftlisLsg-efficieras since the design matrix is orthogonal, all re-gression co-efficients can be estimated IndopendenUy and the estimate of the co-efficient variance are the same 6b se2 g4  Or sh = J , the experimental error is calculated in the following manner MV Sat = 	 ( Yio - I = no - 3' Where Y.o = Response at base levM. I/ = Average of responses at base level no = No. of observations at base level N = Total no. observations At design poinL Significance of the co-efficients can be tested using the student t- test i bo/ t 	 = 	 ; 	 OTT.. •••ww,c,..,.. I bi  re7P0 1Sbo 	Ibb 1 For a significance level of p = 0.05 and f = n0-1 degrees of freedom, if the t value is less than the tabulated value of the Student t distribution t(f), the corresponding co-efficient is insignMxant And mst be removed fr the equatlqn. • • : Test for adequacy of equation (fisher  F - test).  Fisher F-test 9,1as adopted for the adequacy of the equation, For this 6r2 was dqtqralned by tha following (Ye 2  	xp 	 kcal' N -t Where 	Yevt y...:ength of each experiment. )(cal 	 = Calculat&ii strength. = Number of total experiment in the matrix. 	ifj 	 Cioefk . Nubm er of Fisher F = 	 1°'2 Sr2  cr) 9 Cwt SLY CU cs) CV C1) 0 ors te) —1 —S 0 (Y) —1 —4 co 0 0 0 8 -at C 	 .ri 7-1 < a N 0 0 U) 0 / •g 49 (1.) C,) rj 	 •r's > .0 o u) ,--.. . 4 (31 	 . 	 • 4'd 12 Coefficient values were divided by b1 value. Any value is less than the 4.3 (t, 0.5, 2) from statistical table value i.e. probability points of the 	 distribution can be elimi. nated from the equation (i.e. the co-efficients in the equa-tion as they are significant at the 5% point). Hence the sig-nificant co-efficients are as follows Bo, A and BC. FishpRI F-test % Strength, Y 	 = 63.625 1- 1.45A + 1,075 BC - cal  calCulative strength can be determined by 	 equation. Sr2 = 	 7.79  8-3 1.558 Fisher, F = 32 	A,,P56 Sy2 	 04-3737 = 4.17 Where 4.17 The value 5.79 (t, 0.5, 2, 5) is the probability pcint of the variance ratio (F- distribution) as they are significant at 5.0% point. Therefore model can be accepted in its given form. ...13... 13 plots were drawn to the effects and interactions of the factors. From the plots, it is clear that blue dust is not a significant factor in the ranges of study, Hence, it 3-5  possible to eliminate this factor so as to confine ourselves to a more compact and better design that would enable us to fit the model. Hexagonal design and development of a mathematical model 	  Hexagonal desi,gn was the adopted to opti-mise the process variables. A general model represented in the following form Y = Bo+B1X1i-B2X2+BIIX12 41322X22  -1-1312XIX2 where XI and —4  x_ are the two variables. Be, Di and B2 the first order least square co-effelee1;s. St, and B22 are the second order coefficients while B12 is the cross product co-efficient representing interaction between factors. An orthogonal ex-perimental design by definition is one in which all of the main effects and interactions can be estimated without en-tanglement. The design points along with a geometrical repre-sentation are shown in Table 4. The range of the variables is also given and they are coded to coven t40 TrInge of 1 0 14 to -1. The experimental points derivco after coding were tested at randoms at a fixed blue dust of 40%. table 4: 	 Hexu2nalAgsilso.  X = %Coke breeze, range 4.5-7.5 X2 = % Moisture, 	 range 5.0-9.0 Test No. Design level Factor level Bsppnso Strength Xl x2 Coke Moisture 12.  1.000 0.0 7.50 7.0 66.8 13.  0.500 0.866 6.75 8.2 65.5 14.  - 0.500 0.866 5.25 8.2 63.0 15.  - 1.000 0.000 4.50 7.0 56.0 16.  - 0.500 - 0.866 5.25 5.8 63.5 17.  0.500 - 0.366 6.75 5.8 65.8 18.  0.000 0.000 6.00 7.0 64.1 19.  0.000 0.000 6.00 7.0 63.3 20.  0.000 0.000 6.00 7.0 63.0 21.  0.000 0.000 6.00 7.0 63.0 15 Code Xi = Amount of coke - 6.0 1.5 Code X2 = 	Amount of moisture - 7.0 2.0 Regression equation was developed from hexagonal ex-periments % Ys treng th = 63.55+4.4 X1 7 0.23 X2 - 2.28 X12 + 1.94 X22 	 0.08 Differentiating the fitted models with respect to each of the variables and equating th.,A derivatives to zero. The points so obtain are given below X1  = 7.20 X2 = 7.24 The above points 1 i E: 	 thin the eNperimental regions of the design. Sintering at 	 level. Sintering tests were then conducted with the level of coke breeze and water as predicted by optimisation. Blue dust was kept at 40% in the tests. Test results are given in Table 5. 16 Table 5; 	 Results of sinter 	 at 	 level 111,•n•••-  Test No. 	 Factor level 	Response Coke 	 Moisture 	 % Strength 22.  7.20 7.24 66.4 23.  7,20 7.24 66.5 Conclusion: I) 	 Statistical design of experiments is satj,sE/e” tory method for quantifying the effect of va-rious parameters in sintering. 2) Of the three variables, coke breeze contribute the most to the strength of sinter. Blue dui; does not add to the strength properties in the range of composition studied. 3) Regression eqqattons for strength have been developed for sinter. These equations can be utilised to 6-sign experiments to get desired properties. 4) Hexagonal design of experiments is a satisfac- tory method for quantifying the effect of vari-ous parameters. 5) The equation obtained by suitable choice of Lb , various parameters, corrected by T-test are found to be adequate by the F-test. EEERENCES  Davis, 	 The design and analysis of industrial experiments. 2. 	 Natarajan, K.A Statistical design and analysis of a  flotation pocess. Transactions of the Indian Institute of Metals, V01.31, June 1978. 

Optimisation of Process Variables in Sintering through Statistical design of  Experiments

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Optimisation of Process Variables in Sintering through Statistical design of Experiments

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