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Coprocessing of a Turkish lignite with a cellulosic waste material
Fuel Processing Technology 75 (2002) 117 – 127 www.elsevier.com/locate/fuproc
Coprocessing of a Turkish lignite with a cellulosic waste material 3. A statistical study on product yields and total conversion Fatma Karaca*, Esen Bolat, Salih Dincßer Chemical and Metallurgical Engineering Faculty, Chemical Engineering Department, Yıldız Technical University, Davutpasßa-Esenler, I˙stanbul, Turkey Accepted 15 November 2001
Abstract The objectives of this study were to evaluate statistically the effects of coprocessing parameters on liquefaction yields, to determine the key process variables affecting the oil + gas, oil and asphaltene yields and total conversion. A statistical experimental design based on Second Order Central Composite Desing was planned fixing the liquefaction period at 1 h. Parameters such as temperature, initial cold pressure, tetralin/(lignite + sawdust) and sawdust/lignite ratios coded as x1, x2, x3 and x4, respectively, were used. The parameters were investigated at five levels ( 2, 1, 0, 1 and 2). The effects of these factors on dependent variables, namely, oil + gas, oil and asphaltene yields and total conversion were investigated. To determine the significance of effects, the analysis of variance with 99.9% confidence limits was used. It was shown that within the experimental ranges examined, temperature and sawdust/lignite ratio were the variables of highest significance for oil + gas yields, oil yields and total conversion. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Lignite; Sawdust; Liquefaction; Coprocessing; Experimental design
1. Introduction Coprocessing of coal with waste materials is considered to be an alternative process with respect to liquefaction of coal alone. The production of liquids from coal is not economically competitive compared to the cost of petroleum technology, partly because *
Corresponding author. E-mail addresses: [email protected] (F. Karaca), [email protected] (E. Bolat).
0378-3820/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 8 2 0 ( 0 1 ) 0 0 2 5 3 - 3
118
F. Karaca et al. / Fuel Processing Technology 75 (2002) 117–127
of the cost of hydrogen. However, a way to reduce the cost of the liquefaction process may be the reduction of hydrogen consumption and a better usage of hydrogen supply through the utilization of different waste materials [1 –10]. At the same time, coprocessing these waste materials with coal is one way of utilizing these waste materials without having to dispose them. It has been reported that coprocessing of coal with biomass-type wastes has a positive effect on the liquefaction yields and product quality. Coughlin and Davoudzadeh [8] showed that the addition of lignin to coal in the presence of a hydrogen donor solvent and at high pressure, results in an enhancement in coal liquefaction. Lalvani et al. [11] reported that the addition of lignin to coal has a synergistic role in coal liquefaction. However, according to Stiller et al. [12], the coliquefaction of coal with sawdust produced no incremental benefit in conversion. Since there are so many parameters which must be considered in coprocessing, it appears that it is important to determine the most significant variables affecting the process. These include the properties of coal, the type of solvent, liquefaction atmosphere and catalyst, the reaction period, the rate of stirring, the type of waste material used in coprocessing, etc. However, these parameters are shown to affect quite differently. Therefore, some of them can be kept constant while the effects of the others are studied. This study was undertaken to investigate the effectiveness of process variables in the coliquefaction of Soma lignite with sawdust. In this work, the experiments were planned using a statistical experimental design based on Second Order Central Composite Desing. The effect of temperature, initial cold pressure, tetralin/(lignite + sawdust) ratio and sawdust/ lignite ratio were investigated. The choice of variables was made in the light of earlier reports [8– 18].
2. Experimental 2.1. Materials Soma lignite was liquefied with tetralin (Merck grade) under pressurized hydrogen atmosphere. Sawdust obtained as a SEKA (Dalaman, Turkey) paper industry waste, was used as a coliquefaction agent. The lignite samples were ground to 60 mesh size and the sawdust samples were ground and sifted to a particle size between 0.1 and 0.5 mm. The samples were dried under vacuum at 105 °C for 2 h. The proximate and ultimate analysis data for lignite and sawdust samples were presented in our previous paper [17]. 2.2. Procedure Liquefaction experiments were carried out in a 250-ml magnetically stirred and electrically heated stainless steel Ernst Haage 1220 Type autoclave. The temperature of autoclave was measured by a digital thermometer with a NiCr –Ni thermocouple, and it was controlled to within F 3 °C. The values of experimental conditions were fixed according to experimental design requirements. Measured amounts of lignite and sawdust were reacted in tetralin with hydrogen pressurized to the desired initial cold pressure. The mixture was heated to the reaction tem-
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Table 1 The parameters and the levels used for Central Composite Design Levels
T (°C)
P (atm)
S/(L + SD) (vol/wt)
SD/L (wt/wt)
2 1 0 1 2
300 325 350 375 400
10 25 40 55 70
1:1 2:1 3:1 4:1 5:1
0.5:1 0.75:1 1:1 1.25:1 1.5:1
perature in about 30 min and held at this temperature by mixing for 1 h. Detailed experimental details and calculations of yields have been reported previously [17,18]. 2.3. Statistical analysis The present work aims to be a study for the optimization of coprocessing conditions. The effects of reaction temperature (x1), initial cold pressure (x2), tetralin/(lignite + sawdust) ratio (x3), and sawdust/lignite ratio (x4) on the coprocessing yields and total conversion have been investigated. Each of the parameters was coded at five levels: 2, 1, 0, 1 and 2. Based on the type of experimental design used, 30 experiments were needed [19]. The central composite design combined the vertices of hypercube whose coordinates are given by the 2n factorial design (runs 1 – 8 and 11– 18) with the star points (runs 21– 28). The star points were added to the factorial design to provide for estimation of curvature of the model. Six replicates (runs 9, 10, 19, 20, 29 and 30) at the center of design were used to allow for estimation of the pure error sum of squares [19,20]. The expected form of the model can be expressed as follows [19]: Y ¼ b0 þ
n X
bi xi þ
i¼1
n X i¼1
bii x2i þ
n X n X i¼1
bij xi xj
j¼1 i
Here, Y represents percentages of the yields and total conversion obtained by coprocessing; b0 is the value of the fitted response at the center point of design, that is point (0, 0, 0, 0); bi, bii and bij and e are the linear, quadratic and interaction terms and error term, respectively. The relation between the coded and the original scales is given by Draper and Smith [21] as follows: x¼
original variable midpoint of original interval interval of the original range
In this work, parameters were investigated in the range of the values given in Table 1, which shows the parameters and their corresponding levels. The relations between the coded and the original scales are given as: x1 ¼
Temperature 350 25
x2 ¼
Pressure 40 15
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x3 ¼
½Tetralin=ðLignite þ SawdustÞ 3=1 1=1
x4 ¼
½Sawdust=Lignite 1=1 0:25=1
3. Results and discussions The results obtained from the second-order central composite design experiments were evaluated for oil + gas yields, oil yields, asphaltene yields and total conversions. The design matrix and the experimental results are shown in Table 2. The equations of fitted models are shown in Table 3 and the results for analysis of variance are given in Table 4. The b coefficients were determined as given in literature [19,21]. Table 2 The coded values for experimental design and the results for oil + gas yields, oil yields, asphaltene yields and total conversions Run
x1
x2
x3
x4
YOG (%, db)
YO (%, db)
YA (%, db)
YTC (%, db)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 2 2 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 2 2 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 2 2
53.23 51.59 47.29 52.08 50.95 54.79 54.06 47.33 43.92 34.33 30.22 35.82 32.85 39.06 41.92 29.89 42.08 43.54 44.89 43.38 44.11 43.66 63.78 30.18 43.14 42.06 43.09 41.80 52.03 37.51
43.06 37.93 33.23 41.69 37.53 44.07 43.36 33.30 34.38 21.77 18.86 25.95 22.42 28.39 32.44 18.21 30.26 31.49 32.34 31.21 31.91 30.87 46.87 20.93 33.58 29.54 33.42 27.92 42.35 21.20
17.00 13.91 17.30 14.67 18.60 15.93 16.60 14.10 10.00 9.48 9.45 10.91 11.56 13.34 8.33 10.23 14.83 14.98 13.75 14.67 15.14 13.61 15.26 7.12 12.97 13.85 14.45 10.03 17.80 14.36
77.00 69.39 71.26 74.96 72.36 76.17 75.13 69.90 61.60 49.10 48.13 53.56 52.63 57.18 56.97 46.67 63.12 64.29 64.48 64.72 64.46 63.18 82.59 40.85 63.56 60.20 64.58 57.63 74.32 58.65
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Table 3 The fitted model equations YOG ¼ 43:6 þ 7:48x1 þ 1:75x2 þ 0:688x3 þ 2:99x4 0:629x1 x3 1:03x1 x4 0:499x2 x3 0:492x2 x4 þ0:826x3 x4 þ 0:761x21 0:334x22 0:373x23 þ 0:208x24 0:263x1 x2 x4 0:688x1 x3 x4 þ 0:231x31 0:371x32 0:091x33 þ 0:159x34 YO ¼ 31:3 þ 7:15x1 þ 1:54x2 þ 0:706x3 þ 4:08x4 0:849x1 x3 0:607x1 x4 0:599x2 x4 þ 0:769x3 x4 þ 0:690x21 þ0:105x22 0:117x23 þ 0:159x24 0:257x1 x2 x4 0:628x1 x3 x4 0:166x31 0:132x32 þ 0:167x33 þ 0:302x34 YA ¼ 14:5 þ 3:06x1 þ 0:759x2 þ 0:154x3 0:107x4 0:168x1 x2 þ 0:969x1 x3 þ 0:171x2 x3 þ 0:206x2 x4 0:757x3 x4 0:829x21 0:274x22 0:567x23 þ 0:393x24 0:137x1 x2 x4 þ 0:146x1 x3 x4 0:373x2 x3 x4 0:255x31 0:245x32 þ 0:238x33 þ 0:242x34 YTC ¼ 64:0 þ 9:88x1 þ 1:29x2 þ 0:933x3 þ 3:12x4 0:719x1 x2 0:468x1 x3 0:777x1 x4 þ 0:334x2 x3 þ0:238x2 x4 0:505x21 0:465x22 0:659x23 þ 0:686x24 0:386x1 x3 x4 þ 0:138x31 0:113x32 þ 0:201x33 þ0:199x34
3.1. Checking the fitted models The results obtained in the experiments done according to the proposed experimental design were first analysed for the model equations to fit them best. The expected form of the model was second-order. However, it was observed that other interaction effects were also important. As a result, some modifications have been done and the response surfaces obtained for oil + gas yield, oil yield, asphaltene yield and total conversion were third order-like polynomials. The equations are given above. For oil + gas yield ( YOG), oil yield ( YO), asphaltene yield ( YA) and total conversion ( YTC) models, the correlation coefficients were calculated as follows: R2 ¼
Sum of squares due to regression Total sum of squares
It is known that when there are a lot of parameters in the experimental design, the correlation coefficient is not sufficient by itself to check the fitted models. Thus, the plots of residuals and the results of ANOVA were also used in this work (Figs. 1 – 4 and Table 4). Ftest tables given by Draper and Smith [21] were used to determine the F ratios of YOG, YO, YA and YTC. The absolute value of Fexp should be greater than the F-value ( Ftab) obtained from the standard F-distribution. 3.2. The effects of parameters The analysis of data obtained in this study shows that temperature had, as expected [8,9], the strongest effect on oil + gas yields, oil yields, asphaltene yields and total conversion. Increasing temperature increases oil + gas, oil, asphaltene yields and total conversions. The b coefficient of x1 is the largest positive coefficient for all the model equations. It is known that the larger the coefficient, the larger is the effect of related parameter. The positive sign also shows that there is a direct relation between the parameter and dependent variable. The models for YOG, YO and YTC indicate that temperature and sawdust/lignite ratio are the most important variables affecting oil + gas and oil yields and total conversion, re-
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Table 4 Analysis of variance for the fitted models Source
Degree of freedom
Sum of squares
Mean squares
F ratio
(a) For YOG (R = 0.996) Regression 19 Residual 10 Total 29
1894.465 7.162 1901.627
99.709 0.716
139.21
(b) For YO (R = 0.997) Regression 18 Residual 11 Total 29
1769.422 5.166 1774.588
98.301 0.470
209.30
(c) For YA (R = 0.99) Regression 20 Residual 9 Total 29
244.771 2.524 247.295
12.239 0.280
43.64
(d) For YTC (R = 0.999) Regression 18 Residual 11 Total 29
2917.22 3.97 2921.19
162.07 0.36
448.97
spectively. Increasing sawdust/lignite ratio increases oil + gas, oil yields and total conversion, but decreases asphaltene yield as expected [13]. Therefore, it can be said that increasing sawdust ratio in the lignite liquefaction medium increased reaction rate of lignite depolymerization under the conditions investigated. Coefficients related to these two
Fig. 1. Plot of residuals for oil plus gas yields.
F. Karaca et al. / Fuel Processing Technology 75 (2002) 117–127
123
Fig. 2. Plot of residuals for oil yields.
variables indicate significant first and second order (b1 and b4) and two-factor interaction term (b14). It was previously reported [22 – 24] that a change of initial cold pressure had high effect on the oil + gas yields, oil yields and total conversions. In this work, however, increasing initial pressure was observed to have no significant effect on oil + gas and oil yields and total conversion. Pressure was estimated to be the third variable affecting oil + gas yield, oil yield and total conversion after temperature and sawdust/lignite ratio. This result implies that the change of initial pressure does not help stabilize lignite free radicals generated in the initial thermal dissolution process. According to Mastral et al. [7], increasing initial pressure in-
Fig. 3. Plot of residuals for asphaltene yields.
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Fig. 4. Plot of residuals for total conversions.
creases asphaltene yields. Also, in present work, pressure was found to have the most important influence on asphaltene yields after temperature. Various studies [25 – 27] have shown that a good hydrogen donor solvent should be present to prevent retrogressive reactions of coal fragments during the early reaction stage. It has been shown that hydrogen donors such as tetralin promote the production of light products during coliquefaction [27]. In this work, the results indicate that increasing tetralin/(lignite + sawdust) ratio had not a significant effect on total conversion and oil + gas, oil and asphaltene yields since the coefficients of model equations have relatively small values. In other words, tetralin/(lignite + sawdust) ratio was therefore estimated to be the fourth strong variable affecting oil + gas yields, oil yield and total conversion. Model equation for asphaltene yield shows that temperature, initial cold pressure, and tetralin/ (lignite + sawdust) ratio were estimated to be the first-, second- and third-order important variables affecting asphaltene yield in positive direction, respectively. However, sawdust/ lignite ratio was estimated to be the fourth-order important variable in negative direction; in other words, with increasing sawdust ratio in the lignite liquefaction medium, asphaltene yield will decrease. 3.3. Optimization of models The related model equations are useful in indicating the direction in which the variables should be changed in order to optimize the oil + gas, oil and asphaltene yields and total conversion. A combination of optimum experimental values in coded form was found for each parameter by double differentials for oil + gas yield as follows: d2 YOG ¼ 0 ! x1 ¼ 1:1 dx21
F. Karaca et al. / Fuel Processing Technology 75 (2002) 117–127
125
d2 YOG ¼ 0 ! x2 ¼ 0:3 dx22 d2 YOG ¼ 0 ! x3 ¼ 1:36 dx23 d2 YOG ¼ 0 ! x4 ¼ 0:44 dx24 Thus, the working conditions at optimum point for oil + gas yield were determined as follows: Temperature = 323 °C Pressure = 36 atm Tetralin/(lignite + sawdust)ffi1.7:1, vol/wt Sawdust/lignite = 0.9:1, wt/wt As a result, the optimum oil + gas yield ( YOGopt) was calculated as 31.84%. The nearest experimental conditions to this optimum point were 325 °C; 25 atm; 2:1, vol/wt; 0.75:1, wt/ wt; and the corresponding oil + gas yield was 29.89% (see Table 2, run 16). The working conditions at optimum point for oil yield were found as follows: Temperature = 385 °C Pressure = 44 atm Tetralin/(lignite + sawdust)ffi3.23:1, vol/wt Sawdust/lignite = 0.96:1, wt/wt The optimum oil yield ( YOopt) was calculated as 41.87%. The nearest experimental conditions to this optimum point were 400 °C; 40 atm; 3:1, vol/wt; 1:1, wt/wt; and the corresponding oil yield was 46.87% (see Table 2, run 23). The optimum asphaltene yield was obtained at the conditions given below: Temperature = 323 °C Pressure = 34 atm Tetralin/(lignite + sawdust)ffi4:1, vol/wt Sawdust/lignite = 0.86:1, wt/wt The optimum asphaltene yield ( YAopt) was calculated as 9.7%. The nearest experimental conditions to this optimum point were 300 °C; 40 atm; 3:1, vol/wt; 1:1 wt/wt; and the corresponding asphaltene yield was 7.12% (see Table 2, run 24). The optimum total conversion was obtained at the following conditions: Temperature = 380 °C Pressure = 20 atm Tetralin/(lignite + sawdust)ffi4:1, vol/wt Sawdust/lignite = 0.71:1, wt/wt
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The optimum total conversion ( YTCopt) was calculated as 72.63%. The nearest experimental conditions to this optimum point were 375 °C; 25 atm; 4:1, vol/wt; 0.75:1, wt/wt; and the corresponding total conversion was 71.26% (see Table 2, run 3).
4. Conclusions In the light of the results obtained, it can be said that the third-order models based on central composite design predict the coprocessing experimental results quite satisfactorily. The central composite design helped to establish important relationships. The effectiveness of process variables in coprocessing runs was defined. The results indicated that during the coprocessing of Soma lignite with sawdust, temperature and the sawdust ratio in the lignite liquefaction medium showed to be the main controlling variables affecting total conversion and oil + gas, oil and asphaltene yields. On the other hand, pressure and tetralin ratio in the reaction mixture had not a significant effect on oil + gas yield, oil yield and total conversion in the range investigated. Acknowledgements ¨ AF (Project no. 95-B-07-01-02) for the accomplishment We appreciate the support of YU of this work. We are thankful to Prof. Dr. Aziz Bener from Yıldız Technical University for his contribution.
References [1] M. Farcasiu, C.M. Smith, Coprocessing of coal waste rubber, Prepr. - Am. Chem. Soc., Div. Fuel Chem. 37 (1) (1992) 472 – 479. [2] M.M. Taghiei, Z. Feng, E.F. Huggins, G.P. Huffman, Coliquefaction of waste plastics with coal, Energy Fuels 8 (6) (1994) 1228 – 1232. [3] L.L. Anderson, W. Tuntawiroon, W.B. Ding, Coliquefaction of coal and waste polymers, in: J.A. Pajares, J.M.D. Tascon (Eds.), Coal Science: Proceedings of the Eighth International Conference on Coal Science 2, Elsevier, Amsterdam, The Netherlands, 1995, pp. 1515 – 1518. [4] E.C. Orr, W. Tuntawiroon, W.B. Ding, E. Bolat, S. Rumpel, E.M. Eyring, L.L. Anderson, Thermal and catalytic coprocessing of coal and waste materials, Prepr. -Am. Chem. Soc., Div. Fuel Chem. 40 (1) (1995) 44 – 50. [5] G.P. Huffman, Coliquefaction of waste material with coal: a research program of the consortium for fossil fuel liquefaction science, in: M.R. Khan (Ed.), Conversion and Utilization of Waste Materials, Taylor and Francis, Washington, 1996, pp. 39 – 50. [6] Y. Tang, C.W. Curtis, Thermal and catalytic coprocessing of waste tires with coal, Fuel Process. Technol. 46 (3) (1996) 195 – 215. [7] A.M. Mastral, R. Murillo, M. Callen, M.J. Perez-Surio, M.C. Mayoral, Assessment of the tire role in coaltire hydrocoprocessing, Energy Fuels 11 (3) (l997) 676 – 680. [8] R.W. Coughlin, F. Davoudzadeh, Coliquefaction of lignin and bituminous coal, Fuel 65 (1986) 95 – 106. [9] P. Altieri, R.W. Coughlin, Characterization of products formed during coliquefaction of lignin and bituminous coal at 400 °C, Energy Fuels 1 (3) (1987) 253 – 256. [10] S. Sato, T. Murakata, T. Chiba, M. Tukakosi, S. Oda, Effect of lignin addition on coal liquefaction, International Conference On Coal Science, New Energy and Industrial Technology Development Organization, Tokyo, 1989, pp. 717 – 720.
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[11] S.B. Lalvani, C.B. Muchmore, J. Koropchak, B. Akash, C. Chavez, P. Rajagopal, Coal liquefaction in lignin-derived liquids under low severity conditions, Fuel 70 (12) (1991) 1433 – 1438. [12] A.H. Stiller, D.B. Dadyburjor, J. Wann, D. Tian, J.W. Zondlo, Co-processing of agricultural and biomass waste with coal, Fuel Process. Technol. 49 (1996) 167 – 175. [13] H. Jung, J.W. Tierney, I. Wender, G.D. Holder, Coliquefaction of paper with other wastes and coals, Eighth Annual Technical Meeting of The Consortium for Fossil Fuel Liquefaction Science SLC, 6 – 7 July, Pittsburgh, 1994, pp. 49 – 51. [14] P. Sivakumar, H. Jung, J. Tierney, I. Wender, Liquefaction of lignocellulosic and plastic waste with coal using carbon monoxide and aqueous alkali, Fuel Process. Technol. 49 (1996) 219 – 232. [15] B.A. Akash, C.B. Muchmore, S.B. Lalvani, Coliquefaction of coal and newsprint-derived lignin, Fuel Process. Technol. 37 (1994) 203 – 210. [16] H. Jung, J.W. Tierney, I. Wender, Coprocessing of cellulosic waste and coal, Prepr. -Am. Chem. Soc., Div. Fuel Chem. 38 (1993) 880 – 888. [17] F. Karaca, E. Bolat, Coprocessing of a Turkish lignite with a cellulosic waste material 1. The effect of coprocessing on liquefaction yields at different reaction temperatures, Fuel Process. Technol. 64 (2000) 47 – 55. [18] F. Karaca, E. Bolat, Coprocessing of a Turkish lignite with a cellulosic waste material 2. The effect of coprocessing on liquefaction yields at different reaction pressures and sawdust/lignite ratios, Fuel Process. Technol. (submitted for publication). [19] W.G. Cochran, G.M. Cox, Experimental Design, Wiley, New York, 1957. [20] G.E.P. Box, W.G. Hunter, J.S. Hunter, Statistics for Experiments, Wiley, New York, 1978. [21] N.R. Draper, H. Smith, Applied Regression Analysis, 2nd edn., Wiley, New York, 1981. [22] E. Gorin, in: M.A. Elliot (Ed.), Fundamentals of Coal Liquefaction, Chemistry of Coal Utilization, 2nd Suppl., Wiley, New York, 1981. [23] D.D. Whitehurst, T.D. Mitchell, M. Farcasiu, Coal Liquefaction, Academic Press, New York, 1980. [24] W.L. Yoon, G.T. Jin, Y.I. Kim, I.S. Choi, W.K. Lee, Statistical study of the liquefaction of an Indonesian subbituminous coal, Fuel 68 (1989) 614 – 617. [25] D.D. Whitehurst, T.D. Mitchell, Prepr. -Am. Chem. Soc., Div. Fuel Chem. 21 (5) (1977) 127. [26] B.R. Utz, H.R. Appell, B.D. Blaustein, The roles of vehicle and gaseous hydrogen during short contact time coal liquefaction, Fuel 65 (1986) 1085 – 1089. [27] C.W. Curtis, K.J. Tsai, J.A. Guin, Effects of solvent composition on coprocessing coal with petroleum residuals, Fuel Process. Technol. 16 (1) (1987) 71 – 87.
Coprocessing of a Turkish lignite with a cellulosic waste material 3. A statistical study on product yields and total conversion Fatma Karaca*, Esen Bolat, Salih Dincßer Chemical and Metallurgical Engineering Faculty, Chemical Engineering Department, Yıldız Technical University, Davutpasßa-Esenler, I˙stanbul, Turkey Accepted 15 November 2001
Abstract The objectives of this study were to evaluate statistically the effects of coprocessing parameters on liquefaction yields, to determine the key process variables affecting the oil + gas, oil and asphaltene yields and total conversion. A statistical experimental design based on Second Order Central Composite Desing was planned fixing the liquefaction period at 1 h. Parameters such as temperature, initial cold pressure, tetralin/(lignite + sawdust) and sawdust/lignite ratios coded as x1, x2, x3 and x4, respectively, were used. The parameters were investigated at five levels ( 2, 1, 0, 1 and 2). The effects of these factors on dependent variables, namely, oil + gas, oil and asphaltene yields and total conversion were investigated. To determine the significance of effects, the analysis of variance with 99.9% confidence limits was used. It was shown that within the experimental ranges examined, temperature and sawdust/lignite ratio were the variables of highest significance for oil + gas yields, oil yields and total conversion. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Lignite; Sawdust; Liquefaction; Coprocessing; Experimental design
1. Introduction Coprocessing of coal with waste materials is considered to be an alternative process with respect to liquefaction of coal alone. The production of liquids from coal is not economically competitive compared to the cost of petroleum technology, partly because *
Corresponding author. E-mail addresses: [email protected] (F. Karaca), [email protected] (E. Bolat).
0378-3820/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 8 2 0 ( 0 1 ) 0 0 2 5 3 - 3
118
F. Karaca et al. / Fuel Processing Technology 75 (2002) 117–127
of the cost of hydrogen. However, a way to reduce the cost of the liquefaction process may be the reduction of hydrogen consumption and a better usage of hydrogen supply through the utilization of different waste materials [1 –10]. At the same time, coprocessing these waste materials with coal is one way of utilizing these waste materials without having to dispose them. It has been reported that coprocessing of coal with biomass-type wastes has a positive effect on the liquefaction yields and product quality. Coughlin and Davoudzadeh [8] showed that the addition of lignin to coal in the presence of a hydrogen donor solvent and at high pressure, results in an enhancement in coal liquefaction. Lalvani et al. [11] reported that the addition of lignin to coal has a synergistic role in coal liquefaction. However, according to Stiller et al. [12], the coliquefaction of coal with sawdust produced no incremental benefit in conversion. Since there are so many parameters which must be considered in coprocessing, it appears that it is important to determine the most significant variables affecting the process. These include the properties of coal, the type of solvent, liquefaction atmosphere and catalyst, the reaction period, the rate of stirring, the type of waste material used in coprocessing, etc. However, these parameters are shown to affect quite differently. Therefore, some of them can be kept constant while the effects of the others are studied. This study was undertaken to investigate the effectiveness of process variables in the coliquefaction of Soma lignite with sawdust. In this work, the experiments were planned using a statistical experimental design based on Second Order Central Composite Desing. The effect of temperature, initial cold pressure, tetralin/(lignite + sawdust) ratio and sawdust/ lignite ratio were investigated. The choice of variables was made in the light of earlier reports [8– 18].
2. Experimental 2.1. Materials Soma lignite was liquefied with tetralin (Merck grade) under pressurized hydrogen atmosphere. Sawdust obtained as a SEKA (Dalaman, Turkey) paper industry waste, was used as a coliquefaction agent. The lignite samples were ground to 60 mesh size and the sawdust samples were ground and sifted to a particle size between 0.1 and 0.5 mm. The samples were dried under vacuum at 105 °C for 2 h. The proximate and ultimate analysis data for lignite and sawdust samples were presented in our previous paper [17]. 2.2. Procedure Liquefaction experiments were carried out in a 250-ml magnetically stirred and electrically heated stainless steel Ernst Haage 1220 Type autoclave. The temperature of autoclave was measured by a digital thermometer with a NiCr –Ni thermocouple, and it was controlled to within F 3 °C. The values of experimental conditions were fixed according to experimental design requirements. Measured amounts of lignite and sawdust were reacted in tetralin with hydrogen pressurized to the desired initial cold pressure. The mixture was heated to the reaction tem-
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Table 1 The parameters and the levels used for Central Composite Design Levels
T (°C)
P (atm)
S/(L + SD) (vol/wt)
SD/L (wt/wt)
2 1 0 1 2
300 325 350 375 400
10 25 40 55 70
1:1 2:1 3:1 4:1 5:1
0.5:1 0.75:1 1:1 1.25:1 1.5:1
perature in about 30 min and held at this temperature by mixing for 1 h. Detailed experimental details and calculations of yields have been reported previously [17,18]. 2.3. Statistical analysis The present work aims to be a study for the optimization of coprocessing conditions. The effects of reaction temperature (x1), initial cold pressure (x2), tetralin/(lignite + sawdust) ratio (x3), and sawdust/lignite ratio (x4) on the coprocessing yields and total conversion have been investigated. Each of the parameters was coded at five levels: 2, 1, 0, 1 and 2. Based on the type of experimental design used, 30 experiments were needed [19]. The central composite design combined the vertices of hypercube whose coordinates are given by the 2n factorial design (runs 1 – 8 and 11– 18) with the star points (runs 21– 28). The star points were added to the factorial design to provide for estimation of curvature of the model. Six replicates (runs 9, 10, 19, 20, 29 and 30) at the center of design were used to allow for estimation of the pure error sum of squares [19,20]. The expected form of the model can be expressed as follows [19]: Y ¼ b0 þ
n X
bi xi þ
i¼1
n X i¼1
bii x2i þ
n X n X i¼1
bij xi xj
j¼1 i
Here, Y represents percentages of the yields and total conversion obtained by coprocessing; b0 is the value of the fitted response at the center point of design, that is point (0, 0, 0, 0); bi, bii and bij and e are the linear, quadratic and interaction terms and error term, respectively. The relation between the coded and the original scales is given by Draper and Smith [21] as follows: x¼
original variable midpoint of original interval interval of the original range
In this work, parameters were investigated in the range of the values given in Table 1, which shows the parameters and their corresponding levels. The relations between the coded and the original scales are given as: x1 ¼
Temperature 350 25
x2 ¼
Pressure 40 15
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x3 ¼
½Tetralin=ðLignite þ SawdustÞ 3=1 1=1
x4 ¼
½Sawdust=Lignite 1=1 0:25=1
3. Results and discussions The results obtained from the second-order central composite design experiments were evaluated for oil + gas yields, oil yields, asphaltene yields and total conversions. The design matrix and the experimental results are shown in Table 2. The equations of fitted models are shown in Table 3 and the results for analysis of variance are given in Table 4. The b coefficients were determined as given in literature [19,21]. Table 2 The coded values for experimental design and the results for oil + gas yields, oil yields, asphaltene yields and total conversions Run
x1
x2
x3
x4
YOG (%, db)
YO (%, db)
YA (%, db)
YTC (%, db)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 2 2 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 2 2 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 2 2
53.23 51.59 47.29 52.08 50.95 54.79 54.06 47.33 43.92 34.33 30.22 35.82 32.85 39.06 41.92 29.89 42.08 43.54 44.89 43.38 44.11 43.66 63.78 30.18 43.14 42.06 43.09 41.80 52.03 37.51
43.06 37.93 33.23 41.69 37.53 44.07 43.36 33.30 34.38 21.77 18.86 25.95 22.42 28.39 32.44 18.21 30.26 31.49 32.34 31.21 31.91 30.87 46.87 20.93 33.58 29.54 33.42 27.92 42.35 21.20
17.00 13.91 17.30 14.67 18.60 15.93 16.60 14.10 10.00 9.48 9.45 10.91 11.56 13.34 8.33 10.23 14.83 14.98 13.75 14.67 15.14 13.61 15.26 7.12 12.97 13.85 14.45 10.03 17.80 14.36
77.00 69.39 71.26 74.96 72.36 76.17 75.13 69.90 61.60 49.10 48.13 53.56 52.63 57.18 56.97 46.67 63.12 64.29 64.48 64.72 64.46 63.18 82.59 40.85 63.56 60.20 64.58 57.63 74.32 58.65
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Table 3 The fitted model equations YOG ¼ 43:6 þ 7:48x1 þ 1:75x2 þ 0:688x3 þ 2:99x4 0:629x1 x3 1:03x1 x4 0:499x2 x3 0:492x2 x4 þ0:826x3 x4 þ 0:761x21 0:334x22 0:373x23 þ 0:208x24 0:263x1 x2 x4 0:688x1 x3 x4 þ 0:231x31 0:371x32 0:091x33 þ 0:159x34 YO ¼ 31:3 þ 7:15x1 þ 1:54x2 þ 0:706x3 þ 4:08x4 0:849x1 x3 0:607x1 x4 0:599x2 x4 þ 0:769x3 x4 þ 0:690x21 þ0:105x22 0:117x23 þ 0:159x24 0:257x1 x2 x4 0:628x1 x3 x4 0:166x31 0:132x32 þ 0:167x33 þ 0:302x34 YA ¼ 14:5 þ 3:06x1 þ 0:759x2 þ 0:154x3 0:107x4 0:168x1 x2 þ 0:969x1 x3 þ 0:171x2 x3 þ 0:206x2 x4 0:757x3 x4 0:829x21 0:274x22 0:567x23 þ 0:393x24 0:137x1 x2 x4 þ 0:146x1 x3 x4 0:373x2 x3 x4 0:255x31 0:245x32 þ 0:238x33 þ 0:242x34 YTC ¼ 64:0 þ 9:88x1 þ 1:29x2 þ 0:933x3 þ 3:12x4 0:719x1 x2 0:468x1 x3 0:777x1 x4 þ 0:334x2 x3 þ0:238x2 x4 0:505x21 0:465x22 0:659x23 þ 0:686x24 0:386x1 x3 x4 þ 0:138x31 0:113x32 þ 0:201x33 þ0:199x34
3.1. Checking the fitted models The results obtained in the experiments done according to the proposed experimental design were first analysed for the model equations to fit them best. The expected form of the model was second-order. However, it was observed that other interaction effects were also important. As a result, some modifications have been done and the response surfaces obtained for oil + gas yield, oil yield, asphaltene yield and total conversion were third order-like polynomials. The equations are given above. For oil + gas yield ( YOG), oil yield ( YO), asphaltene yield ( YA) and total conversion ( YTC) models, the correlation coefficients were calculated as follows: R2 ¼
Sum of squares due to regression Total sum of squares
It is known that when there are a lot of parameters in the experimental design, the correlation coefficient is not sufficient by itself to check the fitted models. Thus, the plots of residuals and the results of ANOVA were also used in this work (Figs. 1 – 4 and Table 4). Ftest tables given by Draper and Smith [21] were used to determine the F ratios of YOG, YO, YA and YTC. The absolute value of Fexp should be greater than the F-value ( Ftab) obtained from the standard F-distribution. 3.2. The effects of parameters The analysis of data obtained in this study shows that temperature had, as expected [8,9], the strongest effect on oil + gas yields, oil yields, asphaltene yields and total conversion. Increasing temperature increases oil + gas, oil, asphaltene yields and total conversions. The b coefficient of x1 is the largest positive coefficient for all the model equations. It is known that the larger the coefficient, the larger is the effect of related parameter. The positive sign also shows that there is a direct relation between the parameter and dependent variable. The models for YOG, YO and YTC indicate that temperature and sawdust/lignite ratio are the most important variables affecting oil + gas and oil yields and total conversion, re-
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Table 4 Analysis of variance for the fitted models Source
Degree of freedom
Sum of squares
Mean squares
F ratio
(a) For YOG (R = 0.996) Regression 19 Residual 10 Total 29
1894.465 7.162 1901.627
99.709 0.716
139.21
(b) For YO (R = 0.997) Regression 18 Residual 11 Total 29
1769.422 5.166 1774.588
98.301 0.470
209.30
(c) For YA (R = 0.99) Regression 20 Residual 9 Total 29
244.771 2.524 247.295
12.239 0.280
43.64
(d) For YTC (R = 0.999) Regression 18 Residual 11 Total 29
2917.22 3.97 2921.19
162.07 0.36
448.97
spectively. Increasing sawdust/lignite ratio increases oil + gas, oil yields and total conversion, but decreases asphaltene yield as expected [13]. Therefore, it can be said that increasing sawdust ratio in the lignite liquefaction medium increased reaction rate of lignite depolymerization under the conditions investigated. Coefficients related to these two
Fig. 1. Plot of residuals for oil plus gas yields.
F. Karaca et al. / Fuel Processing Technology 75 (2002) 117–127
123
Fig. 2. Plot of residuals for oil yields.
variables indicate significant first and second order (b1 and b4) and two-factor interaction term (b14). It was previously reported [22 – 24] that a change of initial cold pressure had high effect on the oil + gas yields, oil yields and total conversions. In this work, however, increasing initial pressure was observed to have no significant effect on oil + gas and oil yields and total conversion. Pressure was estimated to be the third variable affecting oil + gas yield, oil yield and total conversion after temperature and sawdust/lignite ratio. This result implies that the change of initial pressure does not help stabilize lignite free radicals generated in the initial thermal dissolution process. According to Mastral et al. [7], increasing initial pressure in-
Fig. 3. Plot of residuals for asphaltene yields.
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F. Karaca et al. / Fuel Processing Technology 75 (2002) 117–127
Fig. 4. Plot of residuals for total conversions.
creases asphaltene yields. Also, in present work, pressure was found to have the most important influence on asphaltene yields after temperature. Various studies [25 – 27] have shown that a good hydrogen donor solvent should be present to prevent retrogressive reactions of coal fragments during the early reaction stage. It has been shown that hydrogen donors such as tetralin promote the production of light products during coliquefaction [27]. In this work, the results indicate that increasing tetralin/(lignite + sawdust) ratio had not a significant effect on total conversion and oil + gas, oil and asphaltene yields since the coefficients of model equations have relatively small values. In other words, tetralin/(lignite + sawdust) ratio was therefore estimated to be the fourth strong variable affecting oil + gas yields, oil yield and total conversion. Model equation for asphaltene yield shows that temperature, initial cold pressure, and tetralin/ (lignite + sawdust) ratio were estimated to be the first-, second- and third-order important variables affecting asphaltene yield in positive direction, respectively. However, sawdust/ lignite ratio was estimated to be the fourth-order important variable in negative direction; in other words, with increasing sawdust ratio in the lignite liquefaction medium, asphaltene yield will decrease. 3.3. Optimization of models The related model equations are useful in indicating the direction in which the variables should be changed in order to optimize the oil + gas, oil and asphaltene yields and total conversion. A combination of optimum experimental values in coded form was found for each parameter by double differentials for oil + gas yield as follows: d2 YOG ¼ 0 ! x1 ¼ 1:1 dx21
F. Karaca et al. / Fuel Processing Technology 75 (2002) 117–127
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d2 YOG ¼ 0 ! x2 ¼ 0:3 dx22 d2 YOG ¼ 0 ! x3 ¼ 1:36 dx23 d2 YOG ¼ 0 ! x4 ¼ 0:44 dx24 Thus, the working conditions at optimum point for oil + gas yield were determined as follows: Temperature = 323 °C Pressure = 36 atm Tetralin/(lignite + sawdust)ffi1.7:1, vol/wt Sawdust/lignite = 0.9:1, wt/wt As a result, the optimum oil + gas yield ( YOGopt) was calculated as 31.84%. The nearest experimental conditions to this optimum point were 325 °C; 25 atm; 2:1, vol/wt; 0.75:1, wt/ wt; and the corresponding oil + gas yield was 29.89% (see Table 2, run 16). The working conditions at optimum point for oil yield were found as follows: Temperature = 385 °C Pressure = 44 atm Tetralin/(lignite + sawdust)ffi3.23:1, vol/wt Sawdust/lignite = 0.96:1, wt/wt The optimum oil yield ( YOopt) was calculated as 41.87%. The nearest experimental conditions to this optimum point were 400 °C; 40 atm; 3:1, vol/wt; 1:1, wt/wt; and the corresponding oil yield was 46.87% (see Table 2, run 23). The optimum asphaltene yield was obtained at the conditions given below: Temperature = 323 °C Pressure = 34 atm Tetralin/(lignite + sawdust)ffi4:1, vol/wt Sawdust/lignite = 0.86:1, wt/wt The optimum asphaltene yield ( YAopt) was calculated as 9.7%. The nearest experimental conditions to this optimum point were 300 °C; 40 atm; 3:1, vol/wt; 1:1 wt/wt; and the corresponding asphaltene yield was 7.12% (see Table 2, run 24). The optimum total conversion was obtained at the following conditions: Temperature = 380 °C Pressure = 20 atm Tetralin/(lignite + sawdust)ffi4:1, vol/wt Sawdust/lignite = 0.71:1, wt/wt
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The optimum total conversion ( YTCopt) was calculated as 72.63%. The nearest experimental conditions to this optimum point were 375 °C; 25 atm; 4:1, vol/wt; 0.75:1, wt/wt; and the corresponding total conversion was 71.26% (see Table 2, run 3).
4. Conclusions In the light of the results obtained, it can be said that the third-order models based on central composite design predict the coprocessing experimental results quite satisfactorily. The central composite design helped to establish important relationships. The effectiveness of process variables in coprocessing runs was defined. The results indicated that during the coprocessing of Soma lignite with sawdust, temperature and the sawdust ratio in the lignite liquefaction medium showed to be the main controlling variables affecting total conversion and oil + gas, oil and asphaltene yields. On the other hand, pressure and tetralin ratio in the reaction mixture had not a significant effect on oil + gas yield, oil yield and total conversion in the range investigated. Acknowledgements ¨ AF (Project no. 95-B-07-01-02) for the accomplishment We appreciate the support of YU of this work. We are thankful to Prof. Dr. Aziz Bener from Yıldız Technical University for his contribution.
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