OPTIMIZATION AND MODELING OF FINE COAL BENEFICIATION BY KNELSON CONCENTRATOR USING CENTRAL COMPOSITE DESIGN (CCD)

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1 OPTIMIZATION AND MODELING OF FINE COAL BENEFICIATION BY KNELSON CONCENTRATOR USING CENTRAL COMPOSITE DESIGN (CCD) Özcan Öney 1, Mehmet Tanrıverdi 2 1 Turkish Hard Coal Enterprises, Zonguldak, Turkey 2 Dokuz Eylul University, Mining Engineering Department ABSTRACT: Recently enhanced gravity separators are used widely for the enrichment of fine coal instead of froth flotation. A Knelson concentrator is a high rotational speed separator which is used for fine gold recovery from grinding circuits.. It is also used for the enrichment of fine coal. In this study, application of central composite design (CCD) for modeling and optimization of some operating variables on the Knelson separator for fine coal cleaning is discussed. Four operating variables, namely rotational speed, fluidization water, solid ratio and collecting time were changed during the experiments based on CCD. A total of 30 experiments were done using knelson concentrator on Armutçuk fine coal obtained from Zonguldak coal basin Turkey. Mathematical model equations were obtained by computer simulation program using SPSS 16. R2 values are obtained as and for ash content of clean coal and organic recovery respectively. This study has shown that CCD could be used for modeling of Knelson concentrator effectively. Keywords: Response surface methodology, Central composite design, Knelson concentrator, Fine coal 1. INTRODUCTION Coal reserve of Turkey is very low (1.3 billion tons), and meets a large part of imports. Fine coal consisting 20% of the production is dewatered and used as a power plant fuel with high ash content. For this reason, the evaluation of fine coal is very significant. Knelson concentrator, which is one of the most enhanced gravity separators, is used widely for cleaning coal fines recently. The evaluation of the experimental results is dependent on acknowledging the separation mechanism in a Knelson concentrator. Particle separation mechanism is very complicated due to the collision of particles with each other box in the high centrifugal force high centrifuge forces in the vessel (1, 2, 3, 4, 5). It depends on the implementation of effective operating conditions for the concentrator to obtain desired ash content of clean coal and organic recovery (5). Therefore, to determine the optimal operating conditions is very important. Optimal operating values are determined by using multiple regression analysis. One of those methods is the response surface method. Regression analysis model is created by the help of response surface methods. It is determined with the help of regression coefficients to determine the main effect of a factor or interaction of other factors has a significant effect on the dependent variable (6,7,8}. Appropriate software package programs are used for the design of experimental studies and optimization. There are many response surface method programs in these programs. One of them is the central composite design (CCD) method (9). The purpose of this experiment was to use CCD to establish the functional relationships between four factors (independent variables) of Knelson concentrator namely rotational speed, fluidization water pressure, the solid content and the clean coal concentration-time and dependent variables,(the effect of the clean coal ash and the organic recovery) separately. 2. KNELSON CONCENTRATOR Knelson concentrator is divided into discrete and continuous types in terms of automatic and manual unloading on the basis of the concentrate. It is classified according to the shape of discharge such as manually pouring, the central discharge (CD) and variable discharging models (2). Knelson concentrator has a V type separating vessel and there are five riffles at equal spacing (Fig.2). Feed is given in the form of slurry with gravity through a central tube. Firstly, space is full of solids. The introduction of further feed starts sorting stages where heavy minerals are settled in the concentrator while lighter minerals are carried by water to the top of the unit. Knelson concentrator indicates excellent separation efficiency for 1x0.15 mm coals (10). Fluidized water is introduced through the multiple fluidization holes in the inner shell to keep the bed with heavy minerals. Fluidization water plays an important role during the separation. Centrifugal force is another variable by rotating the separation vessel at different speeds (3, 11, 12).

2 12 Volume 14 - Issue 27 As it can be seen from the separation mechanism, separation cannot be realized after a certain time in parallel with the accumulation of refuse and feed is mixed to the upper stream. Therefore, to determine operational parameters interact with each other is very important. Figure 1. A schematic view of a Knelson concentrator 3. CENTRAL COMPOSITE DESIGN Response surface methodology is defined as a method used for the development and optimization of the processes with the use of statistical and mathematical techniques. Regression analysis model is created with the help of response surface methods. It is decided with the help of regression coefficients. To what extent the main effect of a factor or interaction with other factors (dependent) response variable values (4, 5, 6, 7, 8, 13). Central composite design is a method of experimental design and providing graphics rendering and including the expanded center points. The distance from the center point for each factor of is ± 1 unit for factorial point and star points factorial points for the unit beyond is ± α unit. Central composite design is a combination of full factorial or fractional factorial and star design (7, 8,9,13 ). Using with the central composite design method : 1 - The effect of the factors can be examined, 2 - The interaction between the factors can be examined, 3 Experimental error can be determined with repeated experiments, 4 The parabolic effect of the each factor can be investigated and the optimum conditions can be found. Six experiments at center points were carried out to determine experimental errors in central composite design. Number of experiments was raised from 15 to 20 and from 25 to 30 for three factors and four factors respectively. Coded values were used while creating the experimental design table. Results obtained from the experiments in the light of the experimental design are defined as a function of factors. Several polynomial models are used for the creation of the model equation. These polynomials shows how the response of the system parameter values obtained have effected at the same time. If all variables are assumed as measurable, the response surface can be expressed as follows (7, 8, 9, 14): y= f (x 1, x 2,..x n).(1) where y is the answer of the system, and xi the variables of action called factors. The goal is to optimize the response variable (y). An important assumption is that the independent variables are continuous and controllable by experiments with negligible errors (2, 6, 15, 16, 17). The task then is to find a suitable approximation for the true functional relationship between independent variables and the response surface. Second-degree polynomial model commonly used in the response surface is as follows: 4. MATERIALS AND METHODS. (2) Armutçuk fine coal samples from Zonguldak Coal Basin, fed to filtration circuit taken were used in the experiments. Particle size distribution of the given coal is shown in Table 1. Table 1. Characteristics of Armutçuk raw coal Size fraction Weight Ash Cum. Undersize Ash Weight (mm) (%) (%) (%) (%) ,71 7,17 23,26 100, ,23 16,44 24,99 90, ,06 43,97 43,97 28,06 Total 100,00 23,26 Ash content of the coal fed to filtration circuit is 23.26%. Between the size intervals of 1 to 0.15 mm, ash content of the coal is 16.44% with 62.23% of the total weight. Ash content increases rapidly with decreasing particle size. Float-sink analysis of coal used in the experiments was also carried out (Table 2 & Fig.2). The ash content in the density of 1.75 is found to be 7.75% with 87.91% of total weight.

3 Özcan Öney, Mehmet Tanrıverdi / The Journal of ORE DRESSING Table 2. Float-sink analysis of Armutçuk mm raw coal Specific gravity range Weight Ash Cumulative weight Cumulative ash (gr/cm 3 ) (%) (%) (%) (%) Float - 1,30 56,38 3,37 56,38 3,37 1,30-1,40 16,29 6,72 72,67 4,12 1,40-1,50 6,01 15,52 78,67 4,99 1,50-1,60 5,81 21,26 84,49 6,11 1,60-1,70 1,79 40,30 86,28 6,82 1,70-1,80 1,63 57,01 87,91 7,75 Sink - 1,8 12,09 79,69 100,00 16,45 Total 100,00 Figure 2. Float-sink analysis of given coal Table 3- Independent variables and their levels for CCD Coded variable levels Variables Symbol Lowest Low Center High Highest Rotational speed, rpm x , ,5 835 Fluidization water pressure, (lt/min) x2 1,5 2 2,5 3 3,5 Solid ratio, (%) x , ,5 25 Collecting time (sec) x The variables of the experimental study can be listed as follows; rotational speed ( rpm), water pressure ( l/min), feed rate (15-25%), clean coal, collection time (20-60 sec). A stirrer was fitted into sump to keep the solids in suspension. Slurry was fed to the Knelson concentrator through a peristaltic pump at the rate of 2 l /min. A total of 30 enrichment tests were conducted using Knelson concentrator according to the central composite design with coded values. Clean coal and residual products obtained were dried in an oven at 105 o C. The ash content of clean coal and organic recovery values are given in Table 4. Table 4 - Experimental results according to Central Composite Design Test Number Coded level of variables Observed depended variables Actual ındependent variables Ash Organic yield Speed Water Solid ratio Time x1 x2 x3 x4 pressure (%) (%) (rpm) (lt/min) (%) (sc) ,77 83,99 527,5 2 17, ,52 54,78 527,5 2 22, ,32 60,39 527,5 3 17, ,05 85,10 527,5 3 22, ,30 53,82 732,5 2 17, ,14 82,25 732,5 2 22, ,03 80,44 732,5 3 17, ,63 59,79 732,5 3 22, ,24 75, , ,17 72, , ,03 45,67 527,5 2 17, ,24 83,61 527,5 2 22, ,85 87,07 527,5 3 17, ,03 56,06 527,5 3 22, ,79 77,61 732,5 2 17, ,68 58,97 732,5 2 22, ,63 55,91 732,5 3 17, ,28 88,11 732,5 3 22, ,21 73, , ,29 70, , ,85 68, , ,73 73, , ,54 70, , ,51 66, , ,00 72, , ,41 67, , ,14 73, , ,59 32, , ,74 95, , ,25 70, ,

4 14 Volume 14 - Issue MODEL DEVELOPMENT AND RESULTS The results obtained from the experiments were subjected to multiple regression analysis by using SPSS 16 (Statistical Packages for the Social Sciences) software to get information as to the accuracy of the work performed. Here, rates of organic productivity and ash content were taken as dependent variables, independent variables (factors) and their values and second-order polynomial regression models were estimated. Second-order polynomial used in the experiments was as follows: Y= 0 + 1X 1 + 2X 2 + 3X 3 + 4X X X X X X 1X X 1X X 1X X 2X X 2X X 3X 4...(3) The results of the regression analysis done with SPSS 16 software are given in Table 5 Table 5. The change in R square R Change Statistics R Adjusted R Std. Error of Durbin- R Square F Square Square the Estimate df1 df2 Sig. F Watson Change Change Change Ash (%),881a 0,776 0,566 0, ,776 3, ,008 2,461 Organic Recovery(%),990a 0,979 0,96 2, ,979 50, ,84 R square change of second order regression equation for ash content of clean coal was calculated as This means four independent explanatory variables for the right side of this equation present 77.6% of change in ash content. R square change for organic recovery was calculated as which is a fairly large coefficient. From table 4, sig. was found to be 0. When error probability (α) is taken as 0.05, it can be trust the results with 95% confidence, It means that this is an important model and the at least one of model coefficients is important. Coefficients for ash content of clean coal and organic yield are given in Table 6. From the table, coefficients of solid ratio (x3), the clean coal concentration-time (x4) and x4x4 are seemed to be more important for ash content of clean coal. Regarding the organic recovery, coefficients of fluidization water pressure water (x2), solid ratio (x3), clean coal collecting time (x4) and x1x4 are said to be more important. Table 6. Coefficients for ash content of clean coal and organic recovery. For ash content of clean coal For organic recovery Unstandardized Coefficients Standardized Coefficients Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. B Std. Error Beta t Sig. (Constant) 5,283 0,176 30, ,98 1,12 64,29 0 x1 0,143 0,088 0,199 1,628 0,12 0,415 0,56 0,028 0,742 0,47 x2 0,139 0,088 0,193 1,58 0,14 1,8 0,56 0,119 3,214 0,01 x3 0,221 0,088 0,308 2,52 0,02 1,486 0,56 0,099 2,655 0,02 x4 0,471 0,088 0,656 5, ,48 0,56 0,961 25,86 0 x1x1 0,12 0,082 0,183 1,46 0,17-0,087 0,524-0,006-0,166 0,87 x1x2-0,06 0,108-0,073-0,599 0,56-0,561 0,686-0,03-0,818 0,43 x1x3-0,12 0,108-0,134-1,099 0,29 1,182 0,686 0,064 1,724 0,11 x1x4-0,06 0,108-0,072-0,587 0,57-1,434 0,686-0,078-2,092 0,05 x2x2 0,111 0,082 0,17 1,353 0,2-0,454 0,524-0,033-0,868 0,4 x2x3 0,029 0,108 0,033 0,273 0,79-0,829 0,686-0,045-1,21 0,25 x2x4 0,012 0,108 0,014 0,11 0,91-0,353 0,686-0,019-0,515 0,61 x3x3 0,116 0,082 0,177 1,414 0,18-0,269 0,524-0,02-0,515 0,61 x3x4 0,043 0,108 0,049 0,401 0,69-0,241 0,686-0,013-0,351 0,73 x4x4 0,214 0,082 0,326 2,601 0,02-1,879 0,524-0,137-3,589 0 According to the results of regression analysis, response surface polynomial of second degree for a clean coal ash is as follows:ash content (%)= x x x x x x 1 x x 1 x x 1x x x x 2 x x 2 x x x 3 x X 4 2 (4)

5 Özcan Öney, Mehmet Tanrıverdi / The Journal of ORE DRESSING For organic recovery; Organic recovery (%) = x x x x x x 1 x x 1 x x 1x x x 2 x x 2 x x x 3 x X 4 2. (5) Figure 3. Calculated and observed ash contents of clean coal Figure 5. Response surface predicting ash content from the model equation: effect of fluidization water and rotational speed at center level of solid ratio and clean coal collecting time Figure 4. Calculated and observed values for organic recovery The values of calculated and observed organic recovery are also evaluated with the statistical program Minitab. Regression coefficient of the assessment was 97%. A strong linear relationship between the observed and calculated values are exist (Figure 3.4). In order to gain a better understanding of the results, the predicted models are presented in figure 5 through 10 as the 3-D response surface plots. Fig.5 shows the effect of rotational speed and fluidization water on ash content of clean coal at the center level of solid ratio and clean coal collecting time. The ash content is high at low rotational speed and fluidization water. Ash content of clean coal increases slightly with increasing rotational speed at low fluidization water pressure. The most appropriate ash content of clean coal was obtained at rotational speed of 630 rpm and fluidization water of 2 l/min (5.26%). With increasing speed and water pressure from these levels, the highest ash content of clean coal (6.52 %) was obtained at 3.5 l/min of water pressure and rpm of rotational speed. Figure 6. Response surface predicting ash content from the model equation: effect of fluidization water and clean coal collecting time at center level of solid ratio and rotational speed The effect of clean coal collecting time and fluidization water on ash content of clean coal at the center level of solid ratio and rotational speed is illustrated in Fig.6. Ash content is high at low rotational speed and fluidization water. Ash content of clean coal increases slightly with increasing rotational speed at low fluidization water pressure. The most appropriate ash content of clean coal was obtained at rotational speed of 630 rpm and

6 16 Volume 14 - Issue 27 fluidization water of 2 l/min (5.26%). With increasing speed and water pressure from these levels, the highest ash content of clean coal (6.52 %) was obtained at 3.5 l/min of water pressure and rpm of rotational speed. Clean coal ash content is 5.41 at fluidization water rate of 1.5 l/min and clean coal collecting time of 20 seconds. Ash content of clean coal increases with increasing collecting time from 30 seconds and reaches to 5.82% at 60 seconds at this fluidization water rate. The highest ash content of clean coal was obtained as 7.85 at 60 seconds of collecting time and at 3.5 l/min of fluidization water pressure. l/min fluidization water and at rotational speed of 425 rpm. Figure 8. Response surface predicting organic recovery from the model equation: effect of rotational speed and fluidization water at center level of solid ratio and clean coal collecting time Figure 7. Response surface predicting ash content from the model equation: effect of solid ratio and clean coal collecting time at center level of fluidization water and rotational speed The effect of solid ratio and clean coal collecting time on ash content of clean coal at the center level of fluidization water and rotational speed is shown in Fig.7. Ash content is 5.39 % at the solid ratio of 15% and clean coal collecting time of 20 seconds. Ash content of clean coal increases in all the solid ratios. The lowest ash content of clean coal was obtained at solid ratio of 17.5 % and clean coal collecting time of 30 seconds. The highest ash content of clean coal (8.16 %) was obtained at 25% of solid ratio and at 60 seconds of collecting time. The effect of rotational speed and fluidization water at center level of solid ratio and clean coal collecting time on organic recovery is given in Fig.7. The lowest organic recovery (56.15) was obtained at 1.5 l/min fluidization water and at rotational speed of 425 rpm. If rotational speed increases at 1.5 liter/min fluidization water, organic recovery also increases rapidly. The highest organic recovery was at 3.5 Figure 9. Response surface predicting organic recovery from the model equation: effect of clean coal collecting time and fluidization water at center level of solid ratio and rotational speed The effect of clean coal collecting time and fluidization water on organic recovery at center level of solid ratio and rotational speed is illustrated in Fig.9. The lowest organic value was obtained at clean

7 Özcan Öney, Mehmet Tanrıverdi / The Journal of ORE DRESSING coal collecting time of 20 second. At all clean coal collecting time, organic recovery increases with increasing fluidization water values from lowest to the top. Ash content is 5.39% at the solid ratio of 15% and clean coal collecting time of 20 seconds. The highest organic value was 94.06% at 3 l/min fluidization water and at 60 seconds of collecting time. software to get information on the accuracy of the work performed. R square change of second order regression equation for ash content of clean coal and organic recovery was calculated as and respectively. This shows a good relationship between observed and calculated values. In order to gain a better understanding of the results, the predicted models are presented in figures 5 to 10 as the 3-D response surface plots. This study has stated that CCD could be used effectively for modeling and optimization of Knelson concentrator. ACKNOWLEDGEMENT The author gratefully thank to Dr. Yüksel VARDAR for using SPSS 16 software. REFERENCES Figure 10. Response surface predicting organic recovery from the model equation: effect of solid ratio and clean coal collecting time at center level of fluidization water and rotational speed Fig.10 shows the effect of solid ratio and clean coal collecting time on organic recovery at the center level of fluidization water and rotational speed. Organic recovery increases with increasing clean coal collecting time at all solid ratios. The highest organic recovery was obtained at 25% of solid ratio and at 60 seconds of clean coal collecting time. 6. CONCLUSIONS In this study, the application of central composite design (CCD) for modeling and optimization of some operating variables on the Knelson concentrator for fine coal cleaning is discussed. Several operating variables such as rotational speed, fluidization water, solid ratio and collecting time were experimented during the experiments based on CCD. A total of 30 experiments were conducted using Knelson concentrator on Armutçuk fine coal obtained from Zonguldak coal basin Turkey. The results obtained from the experiments were subjected to multiple regression analysis by using SPSS 16 (Statistical Packages for the Social Sciences) [1] A.K. Majumder, V. Tiwari, J.P. Barnwal, Seperation characteristics of coal fines in a knelson concentrator. International Journal of Coal Preparation and Utilization, Vol. 27, pp (2007) [2] M. McLeavy, B. Klein, I. Grewal, Knelson continous discharge concentrator: Analysis of operating variables. In proceedings of the International Heavy Minerals Conference, pp (2001) [3] G.H. Luttrell, R.Q. Honaker, D.I. Phillips, Enhanced gravity separators: new alternatives for fine coal cleaning; Proceedings of the 12th International Coal Preparation Conference, Lexington, Kentucky, pp (1995) [4] T. Uslu, E. Sahinoglu, Mehmet Yavuz Desulphurization and deashing of oxidized fine coal by Knelson concentrator, Fuel Processing Technology, Vol. 101, pp (2012) [5] S. Ozgen, V. O. Türksoy, E. Sabah, F. Oruc, Process Development Studies on Recovery of Clean Coal From Ultra-Fine Hardcoal Tailings Using Enhanced Gravity Separator, The Canadian Journal of Chemical Engineering, Vol. 87, pp (2009) [6] S. Özgen, Clean chromite production from fine chromite tailings by combination of Multi Gravity Separator and Hydrocyclone, Seperation Science and Technology, Vol. 47, Iss. 13 pp ((2012) [7] N. Aslan, Application of response surface methodology and central composite rotatable design for modelling and optimization of a multi-gravity separator for chromite concentration. Powder Technology, Vol. 185 pp (2008).

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