MOJ MOJMM

Mining and Metallurgy
Research Article
Volume 1 Issue 3

Pelletization of iron ore fines with parameter optimization through box-behnken design

Satyananda Patra, Venugopal Rayasam
Department of Fuel & Mineral Engineering, India
Received: July 17, 2018 | Published: September 27, 2018

Correspondence: Satyananda Patra, Department of Fuel & Mineral Engineering, India, Email

Citation: Patra S, Rayasam V. Pelletization of iron ore fines with parameter optimization through box-behnken design . MOJ Mining Met. 2018;1(3`):72‒77. DOI: 10.15406/mojmm.2018.01.00011

Abstract

Present study is focused on optimization of process parameters during green Pelletization to ascertain the significance of green pellet in indurations. Three levels Box-Behnken design in combination with Response Surface Methodology (RSM) was used for modelling and optimization of parameters like d50 yield of +9mm pellets, MDN and GCS of pellets. Beneficiation studies were carried out and the optimum condition obtained for good strength green pellets are at d50 of 13.8mm, % yield of +9mm pellets of 93.29%, MDN of 17.3 and GCS of 1.94kg/pellet were obtained at the moisture (M) of 14 %, rotation (R) of 44.39rpm and betonies (B) of 0.54 wt.

Keywords: Green Pelletization; Box-Behnken Design; RSM; Modeling and optimization.

Introduction

Iron and Steel industry is considered the backbone of industrial development. The mining of iron ore has a prime importance among all the minerals mined in our country.1 ,2 In recent years, Government of India (GoI) has stipulated a rule to use iron ore up to 45% Fe not to dump fines and slimes as waste, so to conserve the limited reserves of good quality and to increase the usage of low grade ore in the blast furnace feed mix. With the increase in mechanized mining activity and the soft and friable nature of the ores, the generation of fines and ultra-fines has been steadily increasing.3‒5 In addition to this, the steps involved in beneficiation process, handling and transportation operation generate fines and ultra-fines. These fines and ultra-fines can be efficiently used for the production of iron and steel, if used in the form of pellets or sinters, either directly or after beneficiation.3 The main raw material in iron-steel industry is iron ore which can be classified as high grade and low grade in terms of their Fe content.6,7

The most commonly employed agglomeration technique is pelletizing, wherein a mixture of iron ore, water and binder is rolled in a mechanical disc or drum to produce agglomerates (green balls or wet pellets).7‒9 The green pellet quality significantly affects the fired pellet quality. Bentonite is the most commonly used binder in iron ore Pelletization. It not only controls the moisture in iron concentrate but also remarkably improves physical properties of the pellets. However, there are some major drawbacks with the use of betonies. The most striking is the contamination of the product with gangue (silica). Addition of 1% betonies to an iron ore concentrate results in a lowering of acid pellet iron content by 0.6%. In case of direct reduction pellets, every percent of acid gangue addition is associated with an increased energy consumption of 30 kWh.10 Many researchers attempted to find suitable pellet production systems to achieve production of stronger pellets using either alternate binders or new methods against the conventional ones.11The iron ore pellets produced through Pelletization operations are used in the Blast Furnace (BF) for production of iron and in DRI for sponge iron production.12,13

The parameters which affect the Pelletization process are moisture content, drum or disc inclination, fineness of feed, speed of drum or disc, Pelletization time, type and viscosity of binders, etc.9,12,14,15 The most important iron ore Pelletization parameters that affect the agglomeration are wetting-nucleation, consolidation-growth and attrition-breakage.16‒18

This paper outlines the statistical analysis of the influence of process variables on the green pellet characteristics of Barsua iron ore fines.

Experimental

Material preparation

The size analysis results indicate that the d80 and d50 passing sizes of the sample are 1900µm and 150µm respectively. The Fe content is more in finer sizes with lesser silica and alumina contents. The -150μm size fraction of about 51% contains 61% Fe.

The -3mesh ROM iron ore sample, collected from Barsua iron ore mines of Odisha, was crushed in a dodge type jaw crusher. The crushed product was passed through a roll crusher, which was then ground to 100% passing 200mesh in a ball mill. This head sample was used for characterization. Beneficiation studies were carried out after grinding the sample to -150μm using Wet High Intensity Magnetic Separation (WHIMS) to obtain pellet grade concentrate. The sample prepared for the Pelletization study contains approximately 85% below 350mesh.

Experimental setup

Pelletization experiments were carried out on a disc pelletizer having a diameter of 40cm and a rim height of 15cm. The disc is driven by a 1hp engine through reduction gears and provided with a variable speed drive mechanism to regulate the disc speed. The disc is provided with a facility to change the disc inclination. Iron ore sample of 1kg each was mixed with betonies at different proportions prior to the Pelletization study. Moisture was added slowly and continuously. Pelletization time and disc inclination were fixed at 20min and 42°, after preliminary studies. The quality of pellets was tested for size analysis (d50), % yield of +9mm pellets, mean drop number (MDN) and green compressive strength (GCS).

Design of experiments (DOE)

Box-Behnken design of experiments are considered better over full factorial design in that the same output information can be obtained with less number of experiments. Box-Behnken design was used in the present study and the product pellet characteristics of d50, yield of +9mm pellets, MDN and GCS were analyzed as functions of the design and operation parameters.

A full factorial design of three variables with three levels i.e. 33, will involve a total of 27 experiments, it consume more time and material for all the experiments. Box-Behnken Design was used which includes only 15 tests to give all the prediction as given by conventional factorial design. The designs of experiments with experimental data are presented in Table 1, and their levels are shown in Table 2. It is being commonly used to determine the main and interactional effects among two or more factors of the process variables.19‒23 Design of experiments conducted for experimental data has been made by Minitab 17 (Trial).

RSM was used to generate the relationship between dependent and independent variables. A quadratic response surface design comprises of both polynomial and factorial regression equations. The general regression equation for three variables X1, X2and X3are given below;

y= β 0  +  β 1 X 1  +  β 2 X 2  +  β 3 X 3 +  β 4 X 1 2  +  β 5 X 2 2  +  β 6 X 3 2  +  β 7 X 1 X 2 + β 8 X 1 X 3 + β 9 X 2 X 3 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcLbsacaWG5b Gaeyypa0JaeqOSdiwcfa4aaSbaaSqaaKqzadGaaGimaiaaykW7aSqa baqcLbsaqaaaaaaaaaWdbiaacckacqGHRaWkcaGGGcGaeqOSdiwcfa 4aaSbaaKqaGeaajugWaiaaigdaaSqabaqcLbsacaWGybqcfa4aaSba aKqaGeaajugWaiaaigdaaSqabaqcLbsacaGGGcGaey4kaSIaaiiOai abek7aILqbaoaaBaaajeaibaqcLbmacaaIYaaaleqaaKqzGeGaamiw aKqbaoaaBaaajeaibaqcLbmacaaIYaaaleqaaKqzGeGaaiiOaiabgU caRiaacckacqaHYoGyjuaGdaWgaaqcbauaaKqzGdGaaG4maaWcbeaa jugibiaadIfajuaGdaWgaaqcbasaceaa2nqcLbmacaaIZaaaleqaaK qzGeGaey4kaSIaaiiOaiabek7aILqbaoaaBaaajeaibaqcLbmacaaI 0aaaleqaaKqzGeGaamiwaKqbaoaaDaaajeaibaqcLbmacaaIXaaaje aibaqcLbmacaaIYaaaaKqzGeGaaiiOaiabgUcaRiaacckacqaHYoGy juaGdaWgaaqcbasaaKqzadGaaGynaaWcbeaajugibiaadIfajuaGda qhaaqcbasaaKqzadGaaGOmaaqcbasaaKqzadGaaGOmaaaajugibiaa cckacqGHRaWkcaGGGcGaeqOSdiwcfa4aaSbaaKqaGeaajugWaiaaiA daaSqabaqcLbsacaWGybqcfa4aa0baaKqaGeaajugWaiaaiodaaKqa GeaajugWaiaaikdaaaqcLbsacaGGGcGaey4kaSIaaiiOaiabek7aIL qbaoaaBaaajeaibaqcLbmacaaI3aaaleqaaKqzGeGaamiwaKqbaoaa BaaajeaibaqcLbmacaaIXaaaleqaaKqzGeGaamiwaKqbaoaaBaaaje aibaqcLbmacaaIYaaaleqaaKqzGeGaey4kaSIaeqOSdiwcfa4aaSba aKqaGeaajugWaiaaiIdaaSqabaqcLbsacaWGybqcfa4aaSbaaKqaGe aajugWaiaaigdaaSqabaqcLbsacaWGybqcfa4aaSbaaKqaGeaajugW aiaaiodaaSqabaqcLbsacqGHRaWkcqaHYoGyjuaGdaWgaaqcbasaaK qzadGaaGyoaaWcbeaajugibiaadIfajuaGdaWgaaqcbasaaKqzadGa aGOmaaWcbeaajugibiaadIfajuaGdaWgaaqcbasaaKqzadGaaG4maa Wcbeaaaaa@BBE3@     1

Where, y is the predicted response, β is model constant; X1, X2 and X3 are independent variables; β1, β2 and β3 are the linear coefficients; β4, β5 and β6 are the quadratic coefficients and β7, β8 and β9 are the cross product coefficients.23

Results and discussion

Analysis with design of experiments

The results obtained from the experiments were plotted to obtain d50 values at different operating parameters. The proportion of pellets above 9mm (% yield of +9mm pellets), the strength parameters i.e. MDN and GCS were calculated. The results were used to develop correlations between the input and output parameters (Eq. 2-5).

d 50 = 63.061.361 X 1 2.5823 X 2 1.75 X 3 +0.1331 X 1 2 +0.0356 X 2 2 +14.64 X 3 2 0.01275 X 1 X 2 0.795 X 1 X 3 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaqcLbsacaWGKb qcfa4aaSbaaKqaGeaajugWaiaaiwdacaaIWaaaleqaaKqzGeGaeyyp a0deaaaaaaaaa8qacaGGGcGaaGOnaiaaiodacaGGUaGaaGimaiaaiA dacqGHsislcaaIXaGaaiOlaiaaiodacaaI2aGaaGymaiaadIfajuaG daWgaaqcbasaaKqzadGaaGymaaWcbeaajugibiabgkHiTiaaikdaca GGUaGaaGynaiaaiIdacaaIYaGaaG4maiaadIfajuaGdaWgaaqcbasa aKqzadGaaGOmaaWcbeaajugibiabgkHiTiaaigdacaGGUaGaaG4nai aaiwdacaWGybqcfa4aaSbaaKqaGeaajugWaiaaiodaaSqabaqcLbsa cqGHRaWkcaaIWaGaaiOlaiaaigdacaaIZaGaaG4maiaaigdacaWGyb qcfa4aa0baaKqaGeaajugWaiaaigdaaKqaGeaajugWaiaaikdaaaqc LbsacqGHRaWkcaaIWaGaaiOlaiaaicdacaaIZaGaaGynaiaaiAdaca WGybqcfa4aa0baaKqaGeaajugWaiaaikdaaKqaGeaajugWaiaaikda aaqcLbsacqGHRaWkcaaIXaGaaGinaiaac6cacaaI2aGaaGinaiaadI fajuaGdaqhaaqcbasaaKqzadGaaG4maaqcbasaaKqzadGaaGOmaaaa jugibiabgkHiTiaaicdacaGGUaGaaGimaiaaigdacaaIYaGaaG4nai aaiwdacaWGybqcfa4aaSbaaKqaGeaajugWaiaaigdaaSqabaqcLbsa caWGybqcfa4aaSbaaKqaGeaajugWaiaaikdaaSqabaqcLbsacqGHsi slcaaIWaGaaiOlaiaaiEdacaaI5aGaaGynaiaadIfajuaGdaWgaaqc basaaKqzadGaaGymaaWcbeaajugibiaadIfajuaGdaWgaaqcbasaaK qzadGaaG4maaWcbeaaaaa@98C1@ .         2

% Yield=122.7 + 53.74 X 1  81.0 X 3  1.409 X 1 2 ++ 0.1265 X 2 2 +76.6 X 3 2 0.3425 X 1 X 2 a MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbmqcLbsaqa aaaaaaaaWdbiaa=vcacaWFGcGaa8xwaiaa=LgacaWFLbGaa8hBaiaa =rgacqGH9aqpcqGHsislcaaIXaGaaGOmaiaaikdacaGGUaGaaG4nai aabckacqGHRaWkcaqGGcGaaGynaiaaiodacaGGUaGaaG4naiaaisda caWGybqcfa4damaaBaaajeaibaqcLbmapeGaaGymaaWcpaqabaqcLb sapeGaeyOeI0IaaeiOaiaaiIdacaaIXaGaaiOlaiaaicdacaWGybqc fa4damaaBaaajeaibaqcLbmapeGaaG4maaWcpaqabaqcLbsapeGaey OeI0IaaeiOaiaaigdacaGGUaGaaGinaiaaicdacaaI5aGaamiwaKqb a+aadaWgaaqcbasaaKqzadWdbiaaigdaaKqaG8aabeaajuaGdaahaa qcbasabeaajugWa8qacaaIYaaaaKqzGeGaey4kaSIaey4kaSIaaeiO aiaaicdacaGGUaGaaGymaiaaikdacaaI2aGaaGynaiaadIfajuaGpa WaaSbaaKqaGeaajugWa8qacaaIYaaajeaipaqabaqcfa4aaWbaaKqa GeqabaqcLbmapeGaaGOmaaaajugibiabgUcaRiaaiEdacaaI2aGaai OlaiaaiAdacaWGybqcfa4damaaBaaajeaibaqcLbmapeGaaG4maaqc baYdaeqaaKqbaoaaCaaajeaibeqaaKqzadWdbiaaikdaaaqcLbsacq GHsislcaaIWaGaaiOlaiaaiodacaaI0aGaaGOmaiaaiwdacaWGybqc fa4damaaBaaajeaibaqcLbmapeGaaGymaaWcpaqabaqcLbsapeGaam iwaKqba+aadaWgaaqcbasaaKqzadWdbiaaikdaaSWdaeqaaKqzGeGa amyyaaaa@8D46@ .      3

MDN =49.7  6.45 X 1  0.804 X 2  27.9 X 3 +0.179 X 1 X 2 +2.7 X 1 X 3 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqkY=wjY=PjY=PjY=Hhbbf9v8qqaqFr0xc9pk0xbba9q8 WqFfea0=yr0RYxir=Jbba9q8aq0=yq=He9q8qqQ8frFve9Fve9Ff0d meaabaqaciGacaGaaeqabaWaaeaaeaaakeaaieWaqaaaaaaaaaWdbi aa=1eacaWFebGaa8NtaiaacckacqGH9aqpcaaI0aGaaGyoaiaac6ca caaI3aGaaeiOaiabgkHiTiaabckacaaI2aGaaiOlaiaaisdacaaI1a Gaamiwa8aadaWgaaWcbaWdbiaaigdaa8aabeaak8qacqGHsislcaqG GcGaaGimaiaac6cacaaI4aGaaGimaiaaisdacaWGybWdamaaBaaale aapeGaaGOmaaWdaeqaaOWdbiabgkHiTiaabckacaaIYaGaaG4naiaa c6cacaaI5aGaamiwa8aadaWgaaWcbaWdbiaaiodaa8aabeaak8qacq GHRaWkcaaIWaGaaiOlaiaaigdacaaI3aGaaGyoaiaadIfapaWaaSba aSqaa8qacaaIXaaapaqabaGcpeGaamiwa8aadaWgaaWcbaWdbiaaik daa8aabeaak8qacqGHRaWkcaaIYaGaaiOlaiaaiEdacaWGybWdamaa BaaaleaapeGaaGymaaWdaeqaaOWdbiaadIfapaWaaSbaaSqaa8qaca aIZaaapaqabaaaaa@6506@ .        4

GCS=19.65 + 1.670 X 1 +0.4653 X 2 +2.415 X 3 0.08281 X 1 2 0.00655 X 2 2 2.860 X 3 2 +0.008 X 1 X 2 +0.31 X 1 X 3 0.046 X 2 X 3 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqkY=wjY=PjY=PjY=Hhbbf9v8qqaqFr0xc9pk0xbba9q8 WqFfea0=yr0RYxir=Jbba9q8aq0=yq=He9q8qqQ8frFve9Fve9Ff0d meaabaqaciGacaGaaeqabaWaaeaaeaaakeaaieWaqaaaaaaaaaWdbi aa=DeacaWFdbGaa83uaiabg2da9iabgkHiTiaaigdacaaI5aGaaiOl aiaaiAdacaaI1aGaaeiOaiabgUcaRiaabckacaaIXaGaaiOlaiaaiA dacaaI3aGaaGimaiaadIfapaWaaSbaaSqaa8qacaaIXaaapaqabaGc peGaey4kaSIaaGimaiaac6cacaaI0aGaaGOnaiaaiwdacaaIZaGaam iwa8aadaWgaaWcbaWdbiaaikdaa8aabeaak8qacqGHRaWkcaaIYaGa aiOlaiaaisdacaaIXaGaaGynaiaadIfapaWaaSbaaSqaa8qacaaIZa aapaqabaGcpeGaeyOeI0IaaGimaiaac6cacaaIWaGaaGioaiaaikda caaI4aGaaGymaiaadIfapaWaaSbaaSqaa8qacaaIXaaapaqabaGcda ahaaWcbeqaa8qacaaIYaaaaOGaeyOeI0IaaGimaiaac6cacaaIWaGa aGimaiaaiAdacaaI1aGaaGynaiaadIfapaWaaSbaaSqaa8qacaaIYa aapaqabaGcdaahaaWcbeqaa8qacaaIYaaaaOGaeyOeI0IaaGOmaiaa c6cacaaI4aGaaGOnaiaaicdacaWGybWdamaaBaaaleaapeGaaG4maa WdaeqaaOWaaWbaaSqabeaapeGaaGOmaaaakiabgUcaRiaaicdacaGG UaGaaGimaiaaicdacaaI4aGaamiwa8aadaWgaaWcbaWdbiaaigdaa8 aabeaak8qacaWGybWdamaaBaaaleaapeGaaGOmaaWdaeqaaOWdbiab gUcaRiaaicdacaGGUaGaaG4maiaaigdacaWGybWdamaaBaaaleaape GaaGymaaWdaeqaaOWdbiaadIfapaWaaSbaaSqaa8qacaaIZaaapaqa baGcpeGaeyOeI0IaaGimaiaac6cacaaIWaGaaGinaiaaiAdacaWGyb WdamaaBaaaleaapeGaaGOmaaWdaeqaaOWdbiaadIfapaWaaSbaaSqa a8qacaaIZaaapaqabaaaaa@8856@ .    5

In all the equations, the term with positive and negative sign indicate the positive and deleterious influence of the parameters. This statement is also evidenced from the Fisher test value (F-value) for the model which gives the level of significance of the different parameters. Larger the F-value more is the significance. The significance of the variables can also be determined by Probability Test (P-value/Significance Probability Values). A P-value is the probability of an observed result assuming that the null hypothesis is true. The term is considered as highly significant if the P-value is below 0.01. The term is significant and acceptable if the p-value is below 0.05.24‒26 The ANOVA was prepared for all the four response models and the results of ANOVA and the main and interactional effects for the green pellet parameters are presented in Tables 3 and Table 4. The correlation between the observed and predicted values shows that the fit is quite good with an R2 value of 0.9996 for d50, 0.9909 for yield of +9mm pellets, 0.9943 for MDN and 0.9969 for GCS.

Effects of variables

In order to gain a clear understanding of the interactional effects of all the variables on the four responses viz. d50, % yield of +9mm pellets, MDN and GCS. 3D plots for all the responses were drawn, to evaluate the relationship between dependent and independent variables. Since the model has more than two factors, one factor was held constant at central level for each plot, leading to a total of twelve response surface plots.

On d50 and % yield

Figure 1a& Figure 2a show the 3D response surface relationship between M and R on d50 and % yield of +9mm pellets at centre level of B i.e. 0.5%. An increase in d50 and % yield of +9mm pellets was noticed with increase in M and R. As moisture acts as a natural binding agent due to its surface tension, particles come closer and aggregate together. The pellet size increases with increase in M with corresponding increase in d50. It was also observed that d50 of the pellet slightly increased and %yield of +9mm pellets slightly decreased with increase in R. The increase in R makes the pellets strike with great force at the rim of pelletizer which results in breakage of pellets. The layering of the broken fragments might not have happened within the total time of pelletization. Hence, although the d50 had increased, the % yield of +9mm pellets didn’t show any increase.

Figure 1b and Figure 2b show the 3D response surface relationship between M and B on d50 and %yield of+9mm pellets at centre level of R i.e. 40rpm. The maximum d50 and %yield of +9mm pellets were obtained at higher M and B. This is due to the strong mobile liquid bridging of particles. It is known that Bentonite uses moisture equivalent to its quantity to swell and provide more sites for the bonding of particles.

The 3D response surface relationship between R and B on d50 and %yield of +9mm pellets at centre level of moisture M i.e. 12%, are given in Figure 1c & Figure 2c. It is seen that initially the d50 decreased and then increased with increase in B. The increase in d50 was marginal but the decrease in % yield of +9mm pellets was steep with increase in R. At higher R, the pellets break due to high impact with the rim of pelletizer and also due to collision among themselves. Because of continuous breaking of pellet, the layering of broken fragments on surviving pellets may not have occurred. Increase in R leads to higher centrifugal force and hence pellet growth is constrained and retarded.

On mean drop number

Figure 3a shows the 3D response surface relationship between M and R on MDN of green pellet at centre level of B i.e. 0.5%. Figure 3b shows the 3D response surface relationship between M and B on mean MDN of green pellet at centre level of R i.e. 40rpm and Fig 3c shows the 3D response surface relationship between R and B on mean MDN of green pellet at centre level of M i.e. 12%.

Maximum drop number was noted at higher M, R and B. This may be attributed to the combined effect of increased M availability, enhanced M–B interaction and regular creation of new sites due to crushing and layering enabling moisture bridging of the particles and stronger cohesive bonding.

On green compressive strength

Figure 4a shows the 3D response surface relationship between M and R on GCS of green pellet at centre level of B, i.e. 0.5%. The GCS was found to be increasing when R increase. In case of M, it increased up to a certain point i.e. 12%, after which it was decreasing gradually. This may be due to increase in M at higher level of B leads to higher plasticity and made pellets of lesser GCS.

Figure 4b and Figure 4c show the 3D response surface relationship between M and B on GCS of green pellet at centre level of R i.e. 40rpm and the 3D response surface relationship between R and B on GCS of green pellet at centre level of M i.e. 12%, respectively. Moisture gives strength to the green pellet, but more moisture may lead to bigger pellets and lesser packing/bonding, thus showing decreased compressive strength. Similar trend was found for R. High dosage of B gives better compressive strength to green ball, as at higher B leads to greater solid-liquid and solid-solid bonding (Figure 4b & Figure 4c).

The predicted values and the actual data points indicated a good fit (R2 value of 0.9996 for d50, R2 value of 0.9909 for % yield of +9mm pellets, R2 value of 0.9943 for MDN and R2 value of 0.9969 for GCS) of the response equations. This is shown graphically in Figure 5.

Figure 1 Effect of variables on d50

Figure 2 Effect of variables on yield of +9mm pellet

Figure 3 Effect of variables on MDN

Figure 4 Effect of variables on GCS

Figure 5 Relationship between actual and predicted values d50, MDN, GCS and % yield of +9mm pellets

Test no.

Variables

Responses

Actual and coded levels of variables

Observed result

X1

X2

X3

d50

%Yield

MDN

GCS

1

-1

-1

0

9.47

70.2

5.2

1.07

2

1

-1

0

13.5

97.6

8.62

1.51

3

-1

1

0

10.6

80.3

6.68

1.08

4

1

1

0

14.12

94

17.29

1.84

5

-1

0

-1

9.4

72.8

5.01

0.85

6

1

0

-1

13.83

95.1

9.66

1.2

7

-1

0

1

10.86

81.5

7.33

1.21

8

1

0

1

13.7

99.2

17.38

2.18

9

0

-1

-1

11.41

95.7

5.45

1.03

10

0

-1

-1

12.43

93

8.75

1.26

11

0

1

1

12.3

96.3

10.18

1.91

12

0

1

1

13.08

98.8

13.98

1.91

13

0

0

0

10.5

88

9.8

1.87

14

0

0

0

10.5

88

9.8

1.87

15

0

0

0

10.5

88

9.8

1.87

Table 1 Experimental results along with the operating variables

Variables

Symbols

Coded Variable Levels

Low

Medium

High

-1

0

1

Moisture (%)

X1

10

12

14

Disc speed (rpm)

X2

35

40

45

Bentonite (Wt. %)

X3

0.25

0.5

0.75

Table 2 Level of the variables considered

Variables

d50

%Yield

SS

MSS

F-Value

P-Value

SS

MSS

F-Value

P-Value

Moisture

27.454

27.454

8785.3

0

822.15

822.15

377.74

0

Rotation

1.575

1.575

504.1

0

4.96

4.96

2.28

0.191

Bentonite

1.03

1.03

329.48

0

46.08

46.08

21.17

0.006

Moisture*Moisture

0.608

1.047

335.03

0

143.76

117.35

53.92

0.001

Rotation*Rotation

2.495

2.925

935.9

0

29

36.93

16.97

0.009

Bentonite*Bentonite

3.091

3.091

989.21

0

84.63

84.63

38.88

0.002

Moisture*Rotation

0.065

0.065

20.81

0.006

46.92

46.92

21.56

0.006

Moisture*Bentonite

0.632

0.632

202.25

0

5.29

5.29

2.43

0.18

Rotation*Bentonite

0.014

0.014

4.61

0.085

6.76

6.76

3.11

0.138

Variables

MDN

GCS

SS

MSS

F-Value

P-Value

SS

MSS

F-Value

P-Value

Moisture

103.177

103.177

422.95

0

0.794

0.794

507.22

0

Rotation

37.195

37.195

152.47

0

0.041

0.041

25.95

0.004

Bentonite

50

50

204.96

0

1.03

1.03

657.9

0

Moisture*Moisture

0.004

0.009

0.04

0.857

0.351

0.405

258.88

0

Rotation*Rotation

0.359

0.341

1.4

0.29

0.084

0.099

63.26

0.001

Bentonite*Bentonite

0.032

0.032

0.13

0.73

0.118

0.118

75.38

0

Moisture*Rotation

12.924

12.924

52.98

0.001

0.026

0.026

16.36

0.01

Moisture*Bentonite

7.29

7.29

29.88

0.003

0.096

0.096

61.41

0.001

Rotation*Bentonite

0.063

0.063

0.26

0.634

0.013

0.013

8.45

0.034

Table 3 ANOVA table

Response

Main effect

Interactional effect

d50

M > R > B

M × B > M × R > R × B

% Yield of +9 mm pellets

M > B > R

M × R > R × B >M × B

MDN

M > B > R

M × R >M × B > R × B

GCS

M > B > R

M × B > M × R > R × B

Where, M = Moisture content, R = Disc Speed and B = Binder content

Table 4 Represents the main and interactional effect of the green pellet parameters

Optimization

In iron ore green pelletization d50, % yield of +9mm pellets, MDN, and GCS are the important parameters. The model equations were optimized using MINITAB Trial for evaluation of pelletization parameters on the targeted quality and quantity of the final product. The level considered for all these variables are mentioned in Table 2. However, optimization of each response is done individually Table 5. Further, the variables were optimized to achieve the product of targeted quality. A yield of +9mm pellets was targeted at a minimum of 85% (Minimum of 85% of pellets required in the size range of +9mm to -18mm for blast furnace) among all the parameters and the optimized variables were derived. The parameters at which the best results were obtained are presented in Table 6.

Pellet Characteristics

Moisture, %

Disc speed, RPM

Bentonite, %

Maximum values of responses

d50

14

45

0.25

15.1mm

% Yield

13.83

43.37

0.75

99.41%

MDN

14

45

0.75

21.06

GCS

13.47

41.06

0.75

2.22kg/pellet

Table 5 Individual optimization of response

Target M = 14%, R = 44.39 rpm, B = 0.54%

d50

13.8mm

% Yield of +9mm pellets

93.29%

MDN

17.3

GCS

1.94kg/pellet

Table 6 Optimization of responses with targeted variables

Conclusion

In this study, a three-level Box–Behnken factorial design combined with a RSM was used for modelling and optimizing three operations parameters of the iron ore green pelletization. The mathematical model equations were derived for all the responses separately by using sets of experimental data and a mathematical software package (MINITAB 17 Trial).

It was found that best result of d50 of 13.8mm, % yield of +9mm pellets of 93.29%, MDN of 17.3 and GCS of 1.94kg/pellet were obtained at the M of 14 %, R of 44.39 rpm and B of 0.54 wt. %.

Acknowledgements

The authors gratefully acknowledge the technical support extended by Department of Applied Geology, ISM, Dhanbad and CSIR-IMMT, Bhubaneswar. Authors would like to thank all technical staff of Fuel and Mineral Engineering Department, for their support during the experimental work.

Conflict of interest

There is no conflict of interest in this work.

References

  1. Sahoo PK, De BK, Kar S, et al. Iron ore grindability improvement by microwave pre-treatment. Journal of industrial and engineering chemistry. 2010;16(5):805‒812.
  2. Kingman S, Jackson WK, Bradshaw SM, et al. An investigation into the influence of microwave treatment on mineral ore comminution. Powder technology. 2004;146(3):176‒184.
  3. Mahiuddin S, Bondyopadhway S, Baruah JN. A study on the beneficiation of Indian iron-ore fines and slime using chemical additives. International Journal of Mineral Processing. 1989;26(4):285‒296.
  4. Sen P, Mishra DD. The problem of the iron ore fines in India. NML Tech Journal. 1972;14:47.
  5. Chaurasia RC, Nikkam S. Application of artificial neural network to study the performance of multi-gravity separator (MGS) treating iron ore fines. Particulate Science and Technology. 2015;1‒10.
  6. Sivrikaya O, Ali Ihsan A. Use of Boron Compounds as Binders in Iron Ore Pelletization. The Open Mineral Processing Journal.2010;25(3):25‒35.
  7. Sivrikaya O, Ali Ihsan A. Evaluation of low grade iron ore deposit in Erzincan-Turkey for iron ore pellet concentrate production. Physicochem Probl Miner Process. 2012;48(2):475‒484.
  8. Meyer M. Pelletizing of Iron ore, Springer-Verlag Berlin, Dusseldorf, Germany. SCIRB: 1980.
  9. Iveson SM, Litster JD, Hapgood K, et al. Nucleation, growth and breakage phenomena in agitated wet granulation processes: a review. Powder technology. 2001;117(1):3‒39.
  10. Qiu G, Tao J, Hongxu L, et al. Functions and molecular structure of organic binders for iron ore pelletization, Colloids and Surfaces A: Physicochem. Eng Aspects. 2003;224:11‒22.
  11. Sivrikaya O, Ali Ihsan A. An investigation of the relationship between compressive strength and dust generation potential of magnetite pellets. International Journal of Mineral Processing. 2013;123:158‒164.
  12. Abouzeid AZM, Kotb IM, Negm AA. Iron ore fluxed pellets and their physical properties. Powder technology. 1985;42(3): 225‒230.
  13. Leitner J. Application of mercury porosimetry in evaluating the quality of iron ore pellets. Powder Technology. 1981;29(1):199‒203.
  14. Ennis bj, Jianlan P, feffer R. The influence of viscosity on the strength of an axially strained pendular liquid bridge. Chemical Engineering Science. 1990;45(10):3071‒3088.
  15. Ennis BJ, Tardos G, Pfeffer R. A microlevel-based characterization of granulation phenomena. Powder Technology. 1991;65(1-3): 257‒273.
  16. Mort P, Gabriel T. Scale-up of agglomeration processes using transformations. KONA Powder and Particle Journal. 1997;17: 64‒75.
  17. Butensky M, Hyman D. Rotary drum granulation. An experimental study of the factors affecting granule size. Industrial & Engineering Chemistry Fundamentals. 1971;10(2):212‒219.
  18. Tardos GI, Khan MI, Paul RM. Critical parameters and limiting conditions in binder granulation of fine powders. Powder Technology. 1997;94(3):245‒258.
  19. Martı́nez LA, Ortiz JC. Study of celestite flotation efficiency using sodium dodecyl sulfonate collector: factorial experiment and statistical analysis of data. International Journal of Mineral Processing. 2003;70(1):83‒97.
  20. Aslan N. Modelling and optimization of Multi-Gravity Separator to produce celestite concentrate. Powder Technology. 2007;174(3):127‒133.
  21. Aslan N Cebeci Y. Application of Box–Behnken design and response surface methodology for Modelling of some Turkish coals. Fuel. 2007;86(1):90‒97.
  22. Chaurasia RC Nikkam S. Beneficiation of low-grade iron ore fines by multi-gravity separator (MGS) using optimization studies, Particulate Science and Technology. 2015:45‒53.
  23. Chaurasia RC Nikkam S. Optimization Studies on Multi Gravity Separator Treating Ultra-Fine Coal. International Journal of Coal Preparation and Utilization. 2016:195‒212.
  24. Thella JS, Venugopal R. Modelling of iron ore pelletization using 3** (k–p) factorial design of experiments and polynomial surface regression methodology. Powder Technology. 2011;211(1):54‒59.
  25. Fidan N, Aslan R. Optimization of Pb flotation using statistical technique and quadratic programming. Separation and Purification Technology. 2008;62(1):160‒165.
  26. Britto FFD, Alexandre DS, Guiro MJ. When is statistical significance not significant?. Brazilian Political Science Review. 2013;7(1):31‒55.
©2018 Patra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and build upon your work non-commercially.
© 2014-2018 MedCrave Group, All rights reserved. No part of this content may be reproduced or transmitted in any form or by any means as per the standard guidelines of fair use.
Creative Commons License Open Access by MedCrave Group is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at https://medcraveonline.com
Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version | Opera |Privacy Policy