- Email: [email protected]
Optimization of rice husk pretreatment for energy production
Renewable Energy 77 (2015) 512e520
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Optimization of rice husk pretreatment for energy production Alireza Bazargan a, Majid Bazargan b, Gordon McKay a, c, * a
Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clearwater Bay, Hong Kong Department of Mechanical Engineering, K.N. Toosi University of Science and Technology, 15 Pardis St, Mollasadra Ave, Tehran, Iran c Division of Sustainable Development, College of Science, Engineering and Technology, Hamad bin Khalifa University, Qatar Foundation, Doha, Qatar b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 December 2013 Accepted 22 November 2014 Available online
One of the most widely cultivated crops in the world is rice, leading to millions of tons of rice husks (also known as rice hulls or chaffs). The large amount of this lignocellulosic biowaste has resulted in an extensive search for its utilization. One such usage of this waste is for the production of electricity, such as in combined heat and power or gasification units. However, one of the disadvantages of using rice husks is their high silica content which produces large amounts of undesirable ash upon combustion leading to operation problems such as slagging and clogging. Here, alkali pretreatment for the extraction of silica in the form of sodium silicate has been studied using response surface methodology (RSM) and Analysis of Variance (ANOVA). Three independent variables namely reaction temperature, duration, and alkali concentration were considered using a BoxeBehnken design (BBD). The operating conditions were optimized under different scenarios. The first optimization focused on the two goals of high ash removal and high solid yield while the next optimization rounds added the criteria of low NaOH usage and robust design (using propagation of error (POE)). The final treated rice husks can therefore be more suitably used as feed for thermal and/or electric units. The developed empirical predictive models were successfully validated through additional experimentation. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Rice husk/hull/chaff Response surface method (RSM) BoxeBehnken design (BBD) Sodium silicate leaching Alkali extraction of ash
1. Introduction With the increase in public awareness regarding sustainable development, the utilization of renewable resources is on the rise. In the wake of problems associated with fossil fuels, biomass is commonly recognized as one the most important alternatives [1]. One of the most widely cultivated plants in the world is rice. China is the world leader in rice production followed by India and Indonesia. At just under 700 million tons per year, rice is currently the third most widely produced crop in the world after sugar cane and maize [2]. The grain is covered and protected by an outer layer known as the husk or hull. The husk is not edible, is removed in the first stage of the milling process, and composes approximately 20e22% of the total weight of the milled paddy [3,4]. Rice husks are particularly known among agricultural biowastes for their low percentage of protein and available carbohydrates, and high percentage of ash containing mostly silica. The exact composition of
* Corresponding author. Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clearwater Bay, Hong Kong. Tel.: þ852 23588412. E-mail addresses: [email protected] (A. Bazargan), [email protected] (M. Bazargan), [email protected] (G. McKay). http://dx.doi.org/10.1016/j.renene.2014.11.072 0960-1481/© 2014 Elsevier Ltd. All rights reserved.
rice husk which influences its proximate and ultimate analysis depends on many factors such as the weather and cultivation conditions [5e8]. The silica content in the dry season (mean 18%) has been reported to be higher than in the wet season (mean 15%) [3]. Various applications have been proposed for rice husk utilization [9e11] from which silica production [12e16] and energy generation [17e23] have been among the most scrutinized. However, one of the main drawbacks to energy (electricity) generation from rice husks is their high ash content [24]. Aside from the fact that the higher heating value of biomass is inversely proportional to its ash content [25], the silica can form undesired deposits and slags during combustion, which can lead to operational problems [26]. The melting of the rice husk ashes [27] has been known to cause agglomeration, fouling, and corrosion of heat transfer surfaces [28,29]. The removal of silica from rice husks by using sodium hydroxide has been documented from as early as nearly a century ago [30]. Recent years have also seen numerous sporadic studies regarding treatment of husks with sodium hydroxide [1,31e40]. Nonetheless, a parametric study using Response Surface Methodology (RSM) has never been provided on the alkali leaching of silica from raw rice husk. For example Singh et al. have previously used RSM to evaluate
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
the saccharification of rice straw and hull by microwaveealkali pretreatment, but have unfortunately not considered the silica content as a response [36]. RSM is a statistical technique for modeling and analysis of the effects of designated independent variables on selected response variables. Various RSM designs help obtain statistically valid results with running limited experimental trials. The powerful and popular tool of RSM has been applied widely across fields [41e43]. In this manuscript, RSM is used for the first time to optimize operating conditions which allow for maximum silica removal while retaining most organics in the solid phase. If this target is achieved, the rice husks will become more suitable for heat and energy generation purposes. The extracted silicate can in turn be used for the production of silica products with further treatment [44e46] such as in the production of zeolites through hydrothermal reactions. Elsewhere, Bazargan et al. have used RSM to investigate the effect of alkali treatment on the moisture content and drying kinetics of rice husks which is another important factor effecting their usage in energy production [47]. In the literature, the word “ash” is usually used to refer to solid residues of combustion. Although no combustion is employed herein, the word ash is used to refer to the mineral content of the husk removed with alkali treatment.
513
where Wi and Wf are the weight of the rice husks before and after the treatment, and Ashi and Ashf are the initial and final ash content (weight %) of the husk respectively. 2.3. Design of experiment A 3-factor, 3-level BoxeBehnken design (BBD) was used for the Design of Experiments (DOE). The three parameters to evaluate were selected as temperature (X1), treatment duration (X2), and NaOH concentration (X3). The NaOH concentration is defined as the weight ratio of NaOH in the solution to dry rice husk. For example, a 25% concentration represents a 1:4 ratio (i.e. the weight of NaOH used is solution is one fourth of the dry weight of the rice husk). Ash removal percentage and solid yield were used as response variables. The low, center, and high levels of each factor were denoted as 1, 0 and þ1. The corresponding values for these factors are shown in Table 1. A total of 17 experiments were planned by the Design Expert v.7 statistical software in randomized order. A polynomial equation was used to model the mathematical relationship between the variables and responses as follows:
Y ¼ b0 þ
N X
bi Xi þ
i¼1
N X
bii Xi2 þ
i¼1
N 1 X
N X
bij Xi Xj
(2)
i¼1 j¼iþ1
2. Materials and methods 2.1. Alkali extraction of silica The rice husks were harvested in China and donated by Peako Biomass Energy Ltd. The husks were kept in an oven at 90 C and used as-is without washing or any other pretreatment. In each experimental run, the husks were treated with a sodium hydroxide solution at the desired temperature under reflux. The solid loading was fixed at 10 g/L of solution. A magnetic stirrer was used in every experiment to ensure adequate stirring. After the reaction duration was completed, the solids were washed with 4 L of deionized water per gram of husk to ensure all soluble fractions were removed. The as-prepared husk was dried and collected.
where Y is the predicted response and N is the number of variables, fixed at 3 in this study. Xi and Xj are the independent variables, and b0, bi, bii and bij are the intercept term, the linear effects, the squared effects and the interaction effects, respectively. 3. Results and discussion In this study, three factors with three levels were used in a BBD to evaluate alkali leaching of silica from rice husks. The results of the BBD experiments are displayed in Table 2. A “logit” transformation, defined as follows, is applied to the ash removal response (y):
2.2. Ash removal measurements In the literature, the major constituent of rice husk ash is shown to be silica (SiO2), although other element oxides such as Al, K, P, Ca, Mg, Na, and Fe are also present is small amounts [28,48]. Since the overwhelming majority of the ash is composed of silica, it is safe to use the ash content as a good estimate of silica content. The composition of the rice husk as measured by X-ray fluorescence (XRF) spectroscopy is provided in the Supplementary Material. The XRF data shows that the majority of the ash is composed of silicon (84.1% wt), followed by potassium (9.4% wt), and calcium (3.2% wt). Since potassium oxides, nitrate, carbonate, sulfate, and silicate are all soluble in water, the aqueous sodium hydroxide solution will be capable of removing at least 93.5% of the minerals. Predicting the solubility of the various other mineral components which may exist is less straightforward. The ash content is measured using thermogravimetric analysis (TGA). The samples are heated to 650 C for a prolonged period under air atmosphere until constant weight is reached. The ash removal efficiency is defined as:
Ash removal % ¼
1
Wf Ashf Wi Ashi
100
(1)
y yl yu y
ytrans ¼ ln
(3)
where ytrans is the response after the transformation has been applied, yl is the lower limit of the response (here set at 0%) and yu is the upper limit of the response (here set at 100%). The logit transformation is best suited for responses bound between lower and upper limits. 3.1. Fit summary The fit summary, including the sequential model sum of squares, lack of fit tests, and model summary statistics for ash removal and solid yield are presented in Tables 3 and 4 respectively. By considering the Sequential Model Sum of Squares, the highest order polynomial where additional terms are significant and the model is
Table 1 Experimental levels of the independent variables. Factor Name X1 X2 X3
Units Low (1) Center (0) High (þ1) Actual Value Actual Value Actual Value
Temperature C Time h NaOH Concentration % (wt alkali/wt husk)
25 4 1
57.5 14 13
90 24 25
514
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
Table 2 BBD and experimental results.
Table 4 Multi regression analysis of response 2: solid yield %.
Run X1 (temperature, C)
X2 (time, hr)
X3 (NaOH Response 1 concentration, %) (ash removal, %)
Response 2 (solid yield, %)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
4 14 14 14 14 24 14 4 14 4 24 24 4 14 14 14 24
1 25 25 1 13 13 13 13 13 13 13 1 25 13 13 1 25
88 83 57 81 76 68 76 90 75 72 81 87 76 76 75 89 67
57.5 25 90 90 57.5 90 57.5 25 57.5 90 25 57.5 57.5 57.5 57.5 25 57.5
34 38 99 36 91 99 96 14 93 96 47 43 92 94 95 18 95
Sequential model sum of squares Source
Sum of squares
Degrees of Mean freedom square
Mean Linear 2FI Quadratic Cubic Residual Total
102,028.76 1074.75 103.25 34.29 2.75 1.20 103,245.00
1 3 3 3 3 4 17
F-value p-Value
102028.76 358.25 32.92 34.42 9.00 11.43 20.25 0.92 3.06 0.30 6073.24
Comments
<0.0001 0.0034 0.0008 Chosen 0.1544 Aliased
Lack of fit tests Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comments
Linear 2FI Quadratic Cubic Pure Error
140.285 37.035 2.750 e 1.200
9 6 3 e 4.000
15.587 6.173 0.917 e 0.300
51.958 20.575 3.056 e
0.0009 0.0057 0.1544 e
Chosen Aliased
Model summary statistics
not aliased is chosen. Here, a quadratic model is best for both responses. One must keep in mind that the values listed in Tables 3 and 4 are initial fit summaries before any interaction term is removed and the model is tweaked.
Source
Std. dev.
Rsquared
Adj RSquared
Pred. Rsquared
PRESS
Comments
Linear 2FI Quadratic Cubic
3.299 1.955 0.751 0.548
0.884 0.969 0.997 0.999
0.857 0.950 0.993 0.996
0.771 0.900 0.962 e
278.136 121.497 45.875 e
Chosen Aliased
3.2. Analysis of variance and model fitting The statistical significance of the models' coefficients was evaluated using ANOVA tables. Interestingly, as can be seen from Table 5, the response for ash removal showed no significant dependence on non-linear terms containing reaction time. Table 6 shows ANOVA results for the ash removal response after the non-linear terms regarding reaction duration are eliminated. Table 7 shows data regarding the solid yield response before any terms are removed. In the interest of space, data regarding the ANOVA table for the solid yield after elimination of insignificant terms is not shown. Table 3 Multi regression analysis of response 1: ash removal%. Sequential model sum of squares Source
Sum of squares
Degrees of freedom
Mean square
Mean Linear 2FI Quadratic Cubic Residual Total
39.97 52.56 4.32 12.44 2.32 0.44 112.05
1.00 3.00 3.00 3.00 3.00 4.00 17.00
39.97 17.52 1.44 4.15 0.77 0.11 6.59
F-value
p-Value
Comments
11.67 0.95 10.53 7.06
0.0005 0.4545 0.0055 0.0447
Suggested Aliased
Lack of fit tests Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comments
Linear 2FI Quadratic Cubic Pure Error
19.075 14.759 2.319 0.000 0.438
9.000 6.000 3.000 0.000 4.000
2.119 2.460 0.773 e 0.110
19.355 22.464 7.058 e
0.0059 0.0048 0.0447 e
Suggested Aliased
Model summary statistics Source
Std. dev.
R-squared
Adj R-squared
Pred. R-Squared
PRESS
Linear 2FI Quadratic Cubic
1.225 1.233 0.628 0.331
0.729 0.789 0.962 0.994
0.667 0.663 0.913 0.976
0.539 0.356 0.476 e
33.229 46.418 37.783 e
High F-values and low p-values are sought. When the p-value for a particular coefficient is smaller than 0.05, it is deemed significant. The non-significant coefficients will be removed from the final model. High p-values for lack of fit imply that the lack of fit is not significant relative to the pure error. A high R2 indicates that the data satisfactorily fits the model. However, adding variables to the model (irrespective of whether or not they are statistically significant) will always increase R2; therefore its high value is not always an accurate indication of a favorable regression model. Therefore high values obtained for Adjusted R2 and Predicted R2 are used to confirm the favorability of the model. Values for CV% are a measure expressing the standard deviation as a percentage of the mean. The smaller the CV values are the better the reproducibility of the results will be. Generally CV values lower than 10 are desired. In addition, low PRESS values suggest the adequacy of the models for predictive applications. The signal-to-noise ratio is assessed by using the adequate precision; a ratio greater than 4 is preferred [49]. Empirical relationships between the response and independent variables were found. The final models obtained in terms of actual factors are given below: Logit (Ash Removal%) ¼ Ln[(Ash Removal%)/(100 Ash removal %)] ¼ 5.86149 þ 0.13152*Temperature þ 0.04995*Time þ 0.21581 *NaOH þ 0.00266*Temperature*NaOH 0.00092*Temperature2 0.00926*NaOH2 (4) Solid Yield% ¼ 100.08644 0.15385*Temperature 0.87409*Time 0.079265*NaOH þ 0.00384*Temperature*Time 0.011538*Temperature*NaOH 0.016667*Time*NaOH þ 0.020789*Time2 þ 0.012701*NaOH2 (5)
Comments
3.3. Diagnostics and adequacy checking Suggested Aliased
It is imperative to check the adequacy of the fitted models before proceeding to optimization in order to avoid misleading results. As
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
515
Table 5 ANOVA for response 1 (ash removal) before removal of insignificant terms. Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comment
Model X1-Temperature X2-Time X3-NaOH concentration X1X2 X1X3 X2X3 X21 X22 X23 Residual Lack of fit
69.320 30.946 1.996 19.623
9 1 1 1
7.702 30.946 1.996 19.623
19.558 78.581 5.068 49.828
0.0004 <0.0001 0.0591 0.0002
Significant
0.019 4.293 0.004 3.810 0.499 7.118 2.757 2.319
1 1 1 1 1 1 7 3
0.019 4.293 0.004 3.810 0.499 7.118 0.394 0.773
0.049 10.900 0.009 9.674 1.267 18.075
0.8310 0.0131 0.9261 0.0171 0.2974 0.0038
Not significant
7.058
0.0447
Significant
Std. dev. 0.63
PRESS 37.78
C.V. % 40.93
Adeq precision 14.681
R-squared 0.962
Adj R-squared 0.913
Pred R-squared 0.476
Not significant Not significant
Table 6 ANOVA for response 1 (ash removal) after removal of insignificant terms. Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comment
Model X1-Temperature X2-Time X3-NaOH concentration X1X3 X21 X23 Residual Lack of Fit
68.798 30.946 1.996 19.623 4.293 3.967 7.338 3.279 2.841
6 1 1 1 1 1 1 10 6
11.466 30.946 1.996 19.623 4.293 3.967 7.338 0.328 0.473
34.973 94.387 6.087 59.851 13.093 12.100 22.382
<0.0001 <0.0001 0.0333 <0.0001 0.0047 0.0059 0.0008
Significant
4.324
0.0890
not Significant
Std. dev. 0.52
PRESS 16.08
C.V. % 37.34
Adeq precision 19.231
R-squared 0.955
Adj R-squared 0.927
Pred R-squared 0.777
Table 7 ANOVA for response 2 (solid yield) before removal of insignificant terms. Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comment
Model X1-Temperature X2-Time X3-NaOH concentration X1X2 X1X3 X2X3 X21 X22 X23 Residual Lack of fit
1212.285 528.125 66.125 480.500
9 1 1 1
134.698 528.125 66.125 480.500
238.706 935.918 117.184 851.519
<0.0001 <0.0001 <0.0001 <0.0001
Significant
6.250 81.000 16.000 0.024 18.129 14.024 3.950 2.750
1 1 1 1 1 1 7 3
6.250 81.000 16.000 0.024 18.129 14.024 0.564 0.917
11.076 143.544 28.354 0.042 32.127 24.852
0.0126 <0.0001 0.0011 0.8435 0.0008 0.0016
3.056
0.1544
Not significant
Std. dev. 0.751
PRESS 45.875
C.V. % 0.970
Adeq precision 57.061
R-squared 0.997
Adj R-squared 0.993
Pred R-squared 0.962
shown in Fig. 1, the normal probability plots of the internally studentized residuals are linear. In calculating the internally studentized residuals, a residual is compared to the residual variance (entire set including itself). With this simple graphical method, the normal distribution of the observed data is confirmed. If the data in Fig. 1 were not linear, it would indicate that the error terms are not normally distributed, and suggest that the model's underlying assumptions may have been violated. Fig. 2 satisfactorily shows that the residuals are randomly scattered when plotted against the predicted response. Furthermore, Fig. 3 shows that the
Not significant
experimental data is in fair agreement with the values predicted by the developed models (with the solid yield response showing a more accurate predictive capability). Hence the adequacies of the developed models are confirmed. 3.4. Response surfaces Fig. 4 depicts the response surfaces showing the effects of the independent variables on the ash removal and solid yield. Regarding the solid yield, it is evident that as the temperature,
516
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
Fig. 1. Normal probability plot for responses (a) ash removal and (b) solid yield.
reaction duration, and alkali concentration increase, there is a larger loss of solids. Although all three factors show significant effects, the reaction temperature and alkali concentration appear to be more influential on the solid yield than the treatment duration.
At higher temperatures, the NaOH concentration has a drastic effect on the solid yield. This effect is less pronounced at lower temperatures. Regarding the ash removal, from among the three variables considered, the duration is of least influence. The combination of
Fig. 2. Plot of internally studentized residuals vs. predicted response for (a) ash removal and (b) solid yield.
Fig. 3. Diagnostic plots of the model precision for (a) ash removal and (b) solid yield.
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
517
Fig. 4. Response surfaces for (a) ash removal and (bed) solid yield. For all surfaces, the input variables not shown are fixed at the center level.
high temperature and high alkali concentration leads to highest ash removal. It is also noteworthy that 13% NaOH is high enough and removes more than 90% of the ash at moderate temperatures (57.5 C). Any further increase of temperature or concentration does not significantly increase silica ash removal. The slightly higher ash content when the NaOH concentration is increased above 13% could be ascribed to the fact that the sodium which does not react with the silica to form soluble silicate, can attach to the fiber in other forms. This, in turn, adds to the final measured ash amount upon thermal degradation. Hsieh et al. [1] concluded that at a fixed reaction duration, the reaction temperature can enhance SiO2 removal slightly while a change of NaOH concentration has remarkable influence. They found that a 1:5 ratio of NaOH:husk at 50 C for 3 h was adequate to remove nearly all the soluble silica while retaining 63% of the organic matter as solid residue. Further increase of the temperature or alkali concentration did not greatly enhance the silica removal. Approximately 14% of the rice husk was soluble when the treatment was done with pure water. Johar et al. [37] have reported that almost all the silica was removed with alkali treatment under reflux for 6 h (3 runs, 2 h each). The alkali treatment was reported to efficiently remove hemicellulose as well. The smooth surface of the untreated rice husk becomes rough after alkali treatment. This was ascribed to the removal the outer non-cellulosic layer composed of materials such as hemicelluloses, lignin, pectin (protective layer), wax, and other impurities. The diameter of the husk fiber bundles were reported to remain constant after alkali treatment indicating that a large fraction of the lignin (which acts as a cementing agent)
persisted. According to Ndazi et al. [31] higher temperatures, longer treatment, and higher concentration removes more lignin, but at lower temperatures the lignin might not degrade. In rice husks, alkali treatment has previously been shown to break the cell wall by dissolving hemicellulose, lignin, and silica; by hydrolyzing uronic and acetic acid esters; and by swelling cellulose [50]. Endwise degradation, also known as endwise depolymerization or peeling, of cellulose can also occur in alkali media [51]. The hydrogen bond of cellulose is disrupted as a result of alkali treatment and thereby the surface roughness of the samples increases. When cellulose is mercerized, the hydrolysis rate increases markedly (70%). This means that the number of accessible glycosidic linkages in mercerized cellulose is higher than the native one [52]. Hemicelluloses contain a variety of sugar units in addition to glucose and can act as a physical barrier which surrounds and protects the cellulose fibers [53]. The nonglucose units of hemicellulose exhibit distinctly different reactivities when compared to glucose units because of their different ring structures and/or hydroxyl configurations. Generally, hemicelluloses are more reactive than cellulose. Their degradation is similar to that of cellulose but proceeds much more readily and extensively [52]. If the alkali concentration is increased above the limits of this paper, the rice husk will break down to a puffy (mercerized) material which can be used as a sorbent for liquids such as in oil spills [54,55]. Silica has a melting point above 1400 C. In addition to adhesion effects of the sintered ash, in combustion, the alkali oxides and salts can react with silica to form eutectic mixtures. These compounds such as the one formed through the following reaction have
518
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
melting points below that of the individual components (below 800 C) [28]: 4SiO2 þ K2CO3 / K2O $ 4SiO2 þ CO2 Hence the more minerals are removed from the rice husks, the less the operational problems arising from agglomeration, fouling, slagging, and clogging will be. For a review of possible operation problems arising from excessive ash content in combustion systems, refer to the work of Van Caneghem et al. [26]. The silicon in rice husk is thought to be bound to the organic molecules. Since cellulose and lignin do not possess considerable bonding ability with silicon, hemicellulose has been speculated to be responsible for this binding. This can be attributed to the polar electrometric effect in the aldehyde groups [48,56]:
The nature of the silicon bonding is believed to be complex. It has been speculated that it is bound to four carbohydrates as shown below [48,56]:
When these bonds are broken, the SieO groups become attached to each other and form low temperature silica. The removal of silica by alkali treatment is thought to be the result of the following reaction: SiO2 þ 2NaOH / Na2SiO3 þ H2O The leached silica in the liquid can then be used for the production of porous silica-based materials. 3.5. Optimization Optimization has been defined as improving a system's performance in order to obtain the maximum benefit from it [57]. When a set of responses are influenced by several variables, the system is optimized with the objective to simultaneously adjust the levels of the input variables in order to attain the most desirable overall response. In the previous section we have already established the effects of temperature, duration, and alkali ratio on the solid yield and silica removal. First, the system will be optimized with the following criteria: 1. To minimize the loss of solid residue due to the treatment 2. To maximize the removal of silica ash from the solid residue The following two criteria will be added for a second round of optimization: 3. To minimize the use of alkali (for cost-effectiveness of process) 4. To have the most robust design (i.e. minimum propagation of error for both ash removal and solid yield) Errors in input variables such as temperature, duration, and concentration can transmit to the response (silica removal and solid yield). In examining the Propagation of Error (POE), conditions are sought which minimize the transmitted variation of inputs thus “creating a process that's robust to the factor settings” [58]. The
lower POE values are, “the less of the error in control factors will be transmitted to the selected response which results in a more robust process” [58]. If the response surface is linear, POE plots become valueless. This is because under linear conditions errors will be transmitted equally throughout the region. Here, in POE analysis, a standard deviation of 0.5 is chosen for each input variable.
Table 8 Operation parameters under different optimization scenarios. Proposed values for input variables
Optimization criteria
Temperature Time (hr) ( C)
1 and 2 only 59.5 (equal weight) 1, 2, 3, and 4 44.3 (equal weight) 48.4 1 and 2 (more weight), 3 and 4 (less weight)
Predicted responses under optimized conditions
Concentration Ash Solid (%) removal (%) yield (%)
4
15.7
92.6
78.7
22.5
9.8
84.4
79.7
10.2
89.2
78.8
24
With the inclusion of additional optimization criteria, or designating different weights to imposed criteria, various optimized operation conditions emerge. A trade-off occurs during optimization as all criteria cannot be met 100%. The optimization process implies trying to meet the criteria (even when conflicting) as best as possible. As a case in point, adding criteria 3 to the optimization process leads to a decrease in silica removal which is unwanted. This could be expected because designating ‘using less alkali’ as an optimization target logically leads to less soluble sodium silicate formation. Ultimately the optimized operating conditions and the weight of each criterion depend on the choice of the end-user. For example, under conditions in which access to NaOH is difficult and costly, a higher silica content in the final residue may be tolerated. Meanwhile, if cheap alkali is accessible and/or the CHP unit to be used is highly sensitive to ash content, the importance of criterion 2 could be increased while the importance of criterion 3 is diminished. For a more clear understanding, the results of various optimization scenarios have been tabulated in Table 8. Note that numerous solutions are provided during optimization, and Table 8 only shows the ones with the highest desirability. The results from Table 8 show that with optimum operating conditions, nearly 80% of the solid could be kept while approximately 90% of the ash is removed. Such conditions could be very advantageous for more favorable utilization of rice husks as solid fuel. Note that the amount of silica in the raw husk sample used was approximately 12% dry weight. Hence if the husks used have a higher initial silica content, the results would be expected to shift. 3.6. Model validation In order to verify whether the proposed models can accurately estimate the behavior of the system additional experiments are performed and compared to the estimated values from the model. These points are chosen within the design area and not superimposing any previous experimental condition used for developing the model. The values for 95% PI low and 95% PI high (not shown here) designate the range in which the actual response should be. If the actual experimental result for any point is out of the corresponding range, the model is negated. Here, all validation experiments led to response values within the appropriate range and hence validated the model. Table 9 shows two experimental runs used for validation.
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520 Table 9 Validation experiments for the proposed models.
Temperature ( C) Duration (hr) Alkali concentration (%) Actual ash removal (%) Predicted ash removal (%) Actual solid yield (%) Predicted solid removal (%)
Validation experiment 1
Validation experiment 2
70 14 14 99 96.7 71 71.7
58 16 23 92 95.8 71 69.4
4. Conclusion Satisfactory utilization of rice husks for electricity or heat generation is often hampered due to the high ash content of these wastes. Here, a simple alkali treatment is proposed for the removal of the silica ash, so that problems such as clogging, fouling, and slagging could be avoided during energy generation processes. Response Surface Methodology (RSM) with a BoxeBehnken design (BBD) has been used to consider the three variables of reaction temperature, duration, and alkali concentration. The results showed that with optimum operating conditions, nearly 80% of the rice husk can be retained in the solid phase while approximately 90% of the ash is removed. Such pretreatment could therefore be very suitable for rendering the rice husks more favorable for energy production, while retaining a large portion of the organics in the solid phase. Future work may focus on a more detailed understanding of the ash removal mechanism from the rice husks. This could include mass and energy balances for the extraction of minerals (in particular silica), kinetics analyses, and chemical equilibria. Acknowledgments The authors would like to sincerely thank the staff at the MCPF and AEMF facilities at the Hong Kong University and Technology, as well as Mr. Ronnie Lo of Peako Biomass Energy Co. for their assistance. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.renene.2014.11.072. References [1] Hsieh Y, Du Y, Jin F, Zhou Z, Enomoto H. Alkaline pre-treatment of rice hulls for hydrothermal production of acetic acid. Chem Eng Res Des 2009;87:13e8. [2] FAOSTAT. Food and Agriculture Organization of the United Nations Statistics. 2013. [3] Juliano BO. Rice hull and rice straw. In: Juliano BO, editor. Rice: chemistry and technology. 2nd ed. American Association of Cereal Chemists; 1985. p. 689e755. [4] Adam F, Appaturi JN, Iqbal A. The utilization of rice husk silica as a catalyst: review and recent progress. Catal Today 2012;190:2e14. [5] Wang W, Martin J, Zhang N, Ma C, Han A, Sun L. Harvesting silica nanoparticles from rice husks. J Nanopart Res 2011;13:6981e90. €m K, Andersson S, Tervala L, Torkkeli M, Knaapila M, et al. [6] Virtanen T, Svedstro A physico-chemical characterisation of new raw materials for microcrystalline cellulose manufacturing. Cellulose 2012;19:219e35. [7] Nazemi L, Nazmara S, Eshraghyan MR, Nasseri S, Djafarian K, Yunesian M, et al. Selenium status in soil, water and essential crops of Iran. Iran J Environ Health Sci Eng 2012;9. [8] Mehdinia SM, Latif PA, Taghipour H. A comparative evaluation of dried activated sludge and mixed dried activated sludge with rice husk silica to remove hydrogen sulfide. Iran J Environ Health Sci Eng 2013;10. [9] Chowdhury S, Das Saha P. Adsorption of malachite green from aqueous solution by NAOH-modified rice husk: fixed-bed column studies. Environ Prog Sustain Energy 2013;32:633e9.
519
[10] Mehdinia SM, Latif PA, Taghipour H. Removal of hydrogen sulfide by physicobiological filter using mixed Rice husk silica and dried activated sludge. CLEAN Soil Air Water 2013;41:949e54. [11] Kordatos K, Ntziouni A, Iliadis L, Kasselouri-Rigopoulou V. Utilization of amorphous rice husk ash for the synthesis of ZSM-5 zeolite under low temperature. J Mat Cycl Waste Manage 2013;15:571e80. [12] Xing A, Tian S, Tang H, Losic D, Bao Z. Mesoporous silicon engineered by the reduction of biosilica from rice husk as a high-performance anode for lithiumion batteries. RSC Adv 2013;3:10145e9. [13] Chen H, Wang W, Martin JC, Oliphant AJ, Doerr PA, Xu JF, et al. Extraction of lignocellulose and synthesis of porous silica nanoparticles from rice husks: a comprehensive utilization of rice husk biomass. ACS Sustain Chem Eng 2012;1:254e9. [14] Ugheoke B, Mamat O, Ari-Wahjoedi B. Thermal expansion behavior, phase transitions and some physico-mechanical characteristics of fired doped rice husk silica refractory. J Adv Ceram 2013;2:79e86. [15] Alyosef HA, Eilert A, Welscher J, Ibrahim SS, Denecke R, Schwieger W, et al. Characterization of biogenic silica generated by thermo chemical treatment of rice husk. Part Sci Technol 2013;31:524e32. [16] Gu S, Zhou J, Luo Z, Wang Q, Ni M. A detailed study of the effects of pyrolysis temperature and feedstock particle size on the preparation of nanosilica from rice husk. Ind Crops Prod 2013;50:540e9. [17] Gupta R, Pandit A, Nirjar A, Gupta P. Husk power systems: bringing light to rural India and tapping fortune at the bottom of the pyramid. Asian J Manag Cases 2013;10:129e43. [18] Mustonen S, Raiko R, Luukkanen J. Bioenergy consumption and biogas potential in cambodian households. Sustain Switz 2013;5:1875e92. [19] Yoon SJ, Son Y-I, Kim Y-K, Lee J-G. Gasification and power generation characteristics of rice husk and rice husk pellet using a downdraft fixed-bed gasifier. Renew Energy 2012;42:163e7. [20] Shackley S, Carter S, Knowles T, Middelink E, Haefele S, Haszeldine S. Sustainable gasificationebiochar systems? A case-study of rice-husk gasification in Cambodia, part II: field trial results, carbon abatement, economic assessment and conclusions. Energy Policy 2012;41:618e23. [21] Shackley S, Carter S, Knowles T, Middelink E, Haefele S, Sohi S, et al. Sustainable gasificationebiochar systems? A case-study of rice-husk gasification in Cambodia, part I: context, chemical properties, environmental and health and safety issues. Energy Policy 2012;42:49e58. [22] Abu Bakar MS, Titiloye JO. Catalytic pyrolysis of rice husk for bio-oil production. J Anal Appl Pyrolysis 2013;103:362e8. [23] Qi P, Lin N, Liu Y, Zhao J. Improvement of oil/water selectivity by stearic acid modified expanded perlite for oil spill cleanup. J Shanghai Jiaot Univ Sci 2013;18:500e7. [24] Karmakar MK, Mandal J, Haldar S, Chatterjee PK. Investigation of fuel gas generation in a pilot scale fluidized bed autothermal gasifier using rice husk. Fuel 2013;111:584e91. [25] Lim JS, Abdul Manan Z, Wan Alwi SR, Hashim H. A review on utilisation of biomass from rice industry as a source of renewable energy. Renew Sustain Energy Rev 2012;16:3084e94. [26] Van Caneghem J, Brems A, Lievens P, Block C, Billen P, Vermeulen I, et al. Fluidized bed waste incinerators: design, operational and environmental issues. Prog Energy Combust Sci 2012;38:551e82. HJ. [27] Virmond E, De Sena RF, Albrecht W, Althoff CA, Moreira RFPM, Jose Characterisation of agroindustrial solid residues as biofuels and potential application in thermochemical processes. Waste Manag 2012;32:1952e61. [28] Armesto L, Bahillo A, Veijonen K, Cabanillas A, Otero J. Combustion behaviour of rice husk in a bubbling fluidised bed. Biomass Bioenergy 2002;23:171e9. [29] Bakker RR, Jenkins BM, Williams RB. Fluidized bed combustion of leached rice straw. Energy Fuels 2002;16:356e65. [30] Archibald JG. The effect of sodium hydroxid on the composition, digestibility, and feeding value of grain hulls and their fibrous material. J Agric Res 1924;27:245e65. [31] Ndazi BS, Nyahumwa C, Tesha J. Chemical and thermal stability of rice husks against alkali treatment. BioResources 2008;3:1267e77. [32] Ndazi BS, Karlsson S, Tesha JV, Nyahumwa CW. Chemical and physical modifications of rice husks for use as composite panels. Compos Part A Appl Sci Manuf 2007;38:925e35. varo SL, Lopes MS, Vieira de Carvalho Neto AG, Roge rio de Santana R, [33] Fa Radovanovic E. Chemical, morphological, and mechanical analysis of rice husk/post-consumer polyethylene composites. Compos Part A Appl Sci Manuf 2010;41:154e60. [34] Chakraborty S, Chowdhury S, Das Saha P. Adsorption of Crystal Violet from aqueous solution onto NaOH-modified rice husk. Carbohydr Polym 2011;86: 1533e41. [35] Salgado-Delgado R, Vazquez SR, Alvarez-Castillo A, Licea-Claveríe A, ~ o VM. Chemical modification of rice husk surface through alkaline Castan treatments. Mater Technol 2005;20:26e9. [36] Singh A, Tuteja S, Singh N, Bishnoi NR. Enhanced saccharification of rice straw and hull by microwave-alkali pretreatment and lignocellulolytic enzyme production. Bioresour Technol 2011;102:1773e82. [37] Johar N, Ahmad I, Dufresne A. Extraction, preparation and characterization of cellulose fibres and nanocrystals from rice husk. Ind Crops Prod 2012;37: 93e9. [38] Kuan C-Y, Yuen K-H, Liong M-T. Physical, chemical and physicochemical characterization of rice husk. Br Food J 2012;114:853e67.
520
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
[39] Huntanuwatr N, Hunds FC, Davis C. An evaluation of methods for improving the in vitro digestibility of rice hulls. J Anim Sci 1974;38:140e8. [40] McManus WR, Choung CC. Studies on forage cell walls: 2. Conditions for alkali treatment of rice straw and rice hulls. J Agric Sci 1976;86:453e70. [41] Taheri M, Alavi Moghaddam MR, Arami M. Optimization of Acid Black 172 decolorization by electrocoagulation using response surface methodology. Iran J Environ Health Sci Eng 2012;9. [42] Usharani K, Muthukumar M. Optimization of aqueous methylparathion biodegradation by Fusarium sp in batch scale process using response surface methodology. Int J Environ Sci Technol 2013;10:591e606. [43] Jadhav SB, Surwase SN, Phugare SS, Jadhav JP. Response surface methodology mediated optimization of Remazol Orange decolorization in plain distilled water by Pseudomonas aeruginosa BCH. Int J Environ Sci Technol 2013;10: 181e90. [44] Mohamed RM, Mkhalid IA, Abdel Salam M, Barakat MA. Zeolite Y from rice husk ash encapsulated with Ag-TiO2: characterization and applications for photocatalytic degradation catalysts. Desalin Water Treat 2013;51:7562e9. [45] Bohra S, Dey K, Kundu D, Naskar M. Synthesis of zeolite T powders by direct dissolution of rice husk ash: an agro-waste material. J Mater Sci 2013;48: 7893e901. [46] Braga R, Barros JF, Melo DA, Melo MF, Aquino FM, Freitas JO, et al. Kinetic study of template removal of MCM-41 derived from rice husk ash. J Therm Anal Calorim 2013;111:1013e8. [47] Bazargan A, Gebreegziabher T, Hui CW, McKay G. The effect of alkali treatment on rice husk moisture content and drying kinetics. Biomass and Bioenergy 2014;70:468e75. http://dx.doi.org/10.1016/j.biombioe.2014.08.018. [48] Patel M, Karera A, Prasanna P. Effect of thermal and chemical treatments on carbon and silica contents in rice husk. J Mater Sci 1987;22:2457e64.
[49] Samavati V, Adeli M. Isolation and characterization of hydrophobic compounds from carbohydrate matrix of Pistacia atlantica. Carbohydr Polym 2014;101:890e6. [50] Jackson MG. Review article: the alkali treatment of straws. Anim Feed Sci Technol 1977;2:105e30. [51] Li X, Tabil LG, Panigrahi S. Chemical treatments of natural fiber for use in natural fiber-reinforced composites: a review. J Polym Environ 2007;15: 25e33. [52] Lai Y-Z. Reactivity and accessibility of cellulose, hemicelluloses, and lignins. In: Hon DN-S, editor. Chemical modification of lignocellulosic materials. New York: Marcel Dekker; 1996. p. 35e95. [53] Taherzadeh MJ, Karimi K. Pretreatment of lignocellulosic wastes to improve ethanol and biogas production: a review. Int J Mol Sci 2008;9:1621e51. [54] Bazargan A, Tan J, Hui C, McKay G. Utilization of rice husks for the production of oil sorbent materials. Cellulose 2014;21:1679e88. [55] Bazargan A, Hui C, Mckay G. Marine residual fuel sorption and desorption kinetics by alkali treated rice husks. Cellulose 2014;21:1997e2006. [56] Vlaev L, Petkov P, Dimitrov A, Genieva S. Cleanup of water polluted with crude oil or diesel fuel using rice husks ash. J Taiwan Inst Chem Eng 2011;42: 957e64. [57] Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 2008;76:965e77. [58] Design expert 7 user's guide, multifactor RSM tutorial e part 3: adding propagation of error (POE) to the optimization, design expert 7 user's guide. 2004. p. 1e10.
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Optimization of rice husk pretreatment for energy production Alireza Bazargan a, Majid Bazargan b, Gordon McKay a, c, * a
Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clearwater Bay, Hong Kong Department of Mechanical Engineering, K.N. Toosi University of Science and Technology, 15 Pardis St, Mollasadra Ave, Tehran, Iran c Division of Sustainable Development, College of Science, Engineering and Technology, Hamad bin Khalifa University, Qatar Foundation, Doha, Qatar b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 December 2013 Accepted 22 November 2014 Available online
One of the most widely cultivated crops in the world is rice, leading to millions of tons of rice husks (also known as rice hulls or chaffs). The large amount of this lignocellulosic biowaste has resulted in an extensive search for its utilization. One such usage of this waste is for the production of electricity, such as in combined heat and power or gasification units. However, one of the disadvantages of using rice husks is their high silica content which produces large amounts of undesirable ash upon combustion leading to operation problems such as slagging and clogging. Here, alkali pretreatment for the extraction of silica in the form of sodium silicate has been studied using response surface methodology (RSM) and Analysis of Variance (ANOVA). Three independent variables namely reaction temperature, duration, and alkali concentration were considered using a BoxeBehnken design (BBD). The operating conditions were optimized under different scenarios. The first optimization focused on the two goals of high ash removal and high solid yield while the next optimization rounds added the criteria of low NaOH usage and robust design (using propagation of error (POE)). The final treated rice husks can therefore be more suitably used as feed for thermal and/or electric units. The developed empirical predictive models were successfully validated through additional experimentation. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Rice husk/hull/chaff Response surface method (RSM) BoxeBehnken design (BBD) Sodium silicate leaching Alkali extraction of ash
1. Introduction With the increase in public awareness regarding sustainable development, the utilization of renewable resources is on the rise. In the wake of problems associated with fossil fuels, biomass is commonly recognized as one the most important alternatives [1]. One of the most widely cultivated plants in the world is rice. China is the world leader in rice production followed by India and Indonesia. At just under 700 million tons per year, rice is currently the third most widely produced crop in the world after sugar cane and maize [2]. The grain is covered and protected by an outer layer known as the husk or hull. The husk is not edible, is removed in the first stage of the milling process, and composes approximately 20e22% of the total weight of the milled paddy [3,4]. Rice husks are particularly known among agricultural biowastes for their low percentage of protein and available carbohydrates, and high percentage of ash containing mostly silica. The exact composition of
* Corresponding author. Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clearwater Bay, Hong Kong. Tel.: þ852 23588412. E-mail addresses: [email protected] (A. Bazargan), [email protected] (M. Bazargan), [email protected] (G. McKay). http://dx.doi.org/10.1016/j.renene.2014.11.072 0960-1481/© 2014 Elsevier Ltd. All rights reserved.
rice husk which influences its proximate and ultimate analysis depends on many factors such as the weather and cultivation conditions [5e8]. The silica content in the dry season (mean 18%) has been reported to be higher than in the wet season (mean 15%) [3]. Various applications have been proposed for rice husk utilization [9e11] from which silica production [12e16] and energy generation [17e23] have been among the most scrutinized. However, one of the main drawbacks to energy (electricity) generation from rice husks is their high ash content [24]. Aside from the fact that the higher heating value of biomass is inversely proportional to its ash content [25], the silica can form undesired deposits and slags during combustion, which can lead to operational problems [26]. The melting of the rice husk ashes [27] has been known to cause agglomeration, fouling, and corrosion of heat transfer surfaces [28,29]. The removal of silica from rice husks by using sodium hydroxide has been documented from as early as nearly a century ago [30]. Recent years have also seen numerous sporadic studies regarding treatment of husks with sodium hydroxide [1,31e40]. Nonetheless, a parametric study using Response Surface Methodology (RSM) has never been provided on the alkali leaching of silica from raw rice husk. For example Singh et al. have previously used RSM to evaluate
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
the saccharification of rice straw and hull by microwaveealkali pretreatment, but have unfortunately not considered the silica content as a response [36]. RSM is a statistical technique for modeling and analysis of the effects of designated independent variables on selected response variables. Various RSM designs help obtain statistically valid results with running limited experimental trials. The powerful and popular tool of RSM has been applied widely across fields [41e43]. In this manuscript, RSM is used for the first time to optimize operating conditions which allow for maximum silica removal while retaining most organics in the solid phase. If this target is achieved, the rice husks will become more suitable for heat and energy generation purposes. The extracted silicate can in turn be used for the production of silica products with further treatment [44e46] such as in the production of zeolites through hydrothermal reactions. Elsewhere, Bazargan et al. have used RSM to investigate the effect of alkali treatment on the moisture content and drying kinetics of rice husks which is another important factor effecting their usage in energy production [47]. In the literature, the word “ash” is usually used to refer to solid residues of combustion. Although no combustion is employed herein, the word ash is used to refer to the mineral content of the husk removed with alkali treatment.
513
where Wi and Wf are the weight of the rice husks before and after the treatment, and Ashi and Ashf are the initial and final ash content (weight %) of the husk respectively. 2.3. Design of experiment A 3-factor, 3-level BoxeBehnken design (BBD) was used for the Design of Experiments (DOE). The three parameters to evaluate were selected as temperature (X1), treatment duration (X2), and NaOH concentration (X3). The NaOH concentration is defined as the weight ratio of NaOH in the solution to dry rice husk. For example, a 25% concentration represents a 1:4 ratio (i.e. the weight of NaOH used is solution is one fourth of the dry weight of the rice husk). Ash removal percentage and solid yield were used as response variables. The low, center, and high levels of each factor were denoted as 1, 0 and þ1. The corresponding values for these factors are shown in Table 1. A total of 17 experiments were planned by the Design Expert v.7 statistical software in randomized order. A polynomial equation was used to model the mathematical relationship between the variables and responses as follows:
Y ¼ b0 þ
N X
bi Xi þ
i¼1
N X
bii Xi2 þ
i¼1
N 1 X
N X
bij Xi Xj
(2)
i¼1 j¼iþ1
2. Materials and methods 2.1. Alkali extraction of silica The rice husks were harvested in China and donated by Peako Biomass Energy Ltd. The husks were kept in an oven at 90 C and used as-is without washing or any other pretreatment. In each experimental run, the husks were treated with a sodium hydroxide solution at the desired temperature under reflux. The solid loading was fixed at 10 g/L of solution. A magnetic stirrer was used in every experiment to ensure adequate stirring. After the reaction duration was completed, the solids were washed with 4 L of deionized water per gram of husk to ensure all soluble fractions were removed. The as-prepared husk was dried and collected.
where Y is the predicted response and N is the number of variables, fixed at 3 in this study. Xi and Xj are the independent variables, and b0, bi, bii and bij are the intercept term, the linear effects, the squared effects and the interaction effects, respectively. 3. Results and discussion In this study, three factors with three levels were used in a BBD to evaluate alkali leaching of silica from rice husks. The results of the BBD experiments are displayed in Table 2. A “logit” transformation, defined as follows, is applied to the ash removal response (y):
2.2. Ash removal measurements In the literature, the major constituent of rice husk ash is shown to be silica (SiO2), although other element oxides such as Al, K, P, Ca, Mg, Na, and Fe are also present is small amounts [28,48]. Since the overwhelming majority of the ash is composed of silica, it is safe to use the ash content as a good estimate of silica content. The composition of the rice husk as measured by X-ray fluorescence (XRF) spectroscopy is provided in the Supplementary Material. The XRF data shows that the majority of the ash is composed of silicon (84.1% wt), followed by potassium (9.4% wt), and calcium (3.2% wt). Since potassium oxides, nitrate, carbonate, sulfate, and silicate are all soluble in water, the aqueous sodium hydroxide solution will be capable of removing at least 93.5% of the minerals. Predicting the solubility of the various other mineral components which may exist is less straightforward. The ash content is measured using thermogravimetric analysis (TGA). The samples are heated to 650 C for a prolonged period under air atmosphere until constant weight is reached. The ash removal efficiency is defined as:
Ash removal % ¼
1
Wf Ashf Wi Ashi
100
(1)
y yl yu y
ytrans ¼ ln
(3)
where ytrans is the response after the transformation has been applied, yl is the lower limit of the response (here set at 0%) and yu is the upper limit of the response (here set at 100%). The logit transformation is best suited for responses bound between lower and upper limits. 3.1. Fit summary The fit summary, including the sequential model sum of squares, lack of fit tests, and model summary statistics for ash removal and solid yield are presented in Tables 3 and 4 respectively. By considering the Sequential Model Sum of Squares, the highest order polynomial where additional terms are significant and the model is
Table 1 Experimental levels of the independent variables. Factor Name X1 X2 X3
Units Low (1) Center (0) High (þ1) Actual Value Actual Value Actual Value
Temperature C Time h NaOH Concentration % (wt alkali/wt husk)
25 4 1
57.5 14 13
90 24 25
514
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
Table 2 BBD and experimental results.
Table 4 Multi regression analysis of response 2: solid yield %.
Run X1 (temperature, C)
X2 (time, hr)
X3 (NaOH Response 1 concentration, %) (ash removal, %)
Response 2 (solid yield, %)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
4 14 14 14 14 24 14 4 14 4 24 24 4 14 14 14 24
1 25 25 1 13 13 13 13 13 13 13 1 25 13 13 1 25
88 83 57 81 76 68 76 90 75 72 81 87 76 76 75 89 67
57.5 25 90 90 57.5 90 57.5 25 57.5 90 25 57.5 57.5 57.5 57.5 25 57.5
34 38 99 36 91 99 96 14 93 96 47 43 92 94 95 18 95
Sequential model sum of squares Source
Sum of squares
Degrees of Mean freedom square
Mean Linear 2FI Quadratic Cubic Residual Total
102,028.76 1074.75 103.25 34.29 2.75 1.20 103,245.00
1 3 3 3 3 4 17
F-value p-Value
102028.76 358.25 32.92 34.42 9.00 11.43 20.25 0.92 3.06 0.30 6073.24
Comments
<0.0001 0.0034 0.0008 Chosen 0.1544 Aliased
Lack of fit tests Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comments
Linear 2FI Quadratic Cubic Pure Error
140.285 37.035 2.750 e 1.200
9 6 3 e 4.000
15.587 6.173 0.917 e 0.300
51.958 20.575 3.056 e
0.0009 0.0057 0.1544 e
Chosen Aliased
Model summary statistics
not aliased is chosen. Here, a quadratic model is best for both responses. One must keep in mind that the values listed in Tables 3 and 4 are initial fit summaries before any interaction term is removed and the model is tweaked.
Source
Std. dev.
Rsquared
Adj RSquared
Pred. Rsquared
PRESS
Comments
Linear 2FI Quadratic Cubic
3.299 1.955 0.751 0.548
0.884 0.969 0.997 0.999
0.857 0.950 0.993 0.996
0.771 0.900 0.962 e
278.136 121.497 45.875 e
Chosen Aliased
3.2. Analysis of variance and model fitting The statistical significance of the models' coefficients was evaluated using ANOVA tables. Interestingly, as can be seen from Table 5, the response for ash removal showed no significant dependence on non-linear terms containing reaction time. Table 6 shows ANOVA results for the ash removal response after the non-linear terms regarding reaction duration are eliminated. Table 7 shows data regarding the solid yield response before any terms are removed. In the interest of space, data regarding the ANOVA table for the solid yield after elimination of insignificant terms is not shown. Table 3 Multi regression analysis of response 1: ash removal%. Sequential model sum of squares Source
Sum of squares
Degrees of freedom
Mean square
Mean Linear 2FI Quadratic Cubic Residual Total
39.97 52.56 4.32 12.44 2.32 0.44 112.05
1.00 3.00 3.00 3.00 3.00 4.00 17.00
39.97 17.52 1.44 4.15 0.77 0.11 6.59
F-value
p-Value
Comments
11.67 0.95 10.53 7.06
0.0005 0.4545 0.0055 0.0447
Suggested Aliased
Lack of fit tests Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comments
Linear 2FI Quadratic Cubic Pure Error
19.075 14.759 2.319 0.000 0.438
9.000 6.000 3.000 0.000 4.000
2.119 2.460 0.773 e 0.110
19.355 22.464 7.058 e
0.0059 0.0048 0.0447 e
Suggested Aliased
Model summary statistics Source
Std. dev.
R-squared
Adj R-squared
Pred. R-Squared
PRESS
Linear 2FI Quadratic Cubic
1.225 1.233 0.628 0.331
0.729 0.789 0.962 0.994
0.667 0.663 0.913 0.976
0.539 0.356 0.476 e
33.229 46.418 37.783 e
High F-values and low p-values are sought. When the p-value for a particular coefficient is smaller than 0.05, it is deemed significant. The non-significant coefficients will be removed from the final model. High p-values for lack of fit imply that the lack of fit is not significant relative to the pure error. A high R2 indicates that the data satisfactorily fits the model. However, adding variables to the model (irrespective of whether or not they are statistically significant) will always increase R2; therefore its high value is not always an accurate indication of a favorable regression model. Therefore high values obtained for Adjusted R2 and Predicted R2 are used to confirm the favorability of the model. Values for CV% are a measure expressing the standard deviation as a percentage of the mean. The smaller the CV values are the better the reproducibility of the results will be. Generally CV values lower than 10 are desired. In addition, low PRESS values suggest the adequacy of the models for predictive applications. The signal-to-noise ratio is assessed by using the adequate precision; a ratio greater than 4 is preferred [49]. Empirical relationships between the response and independent variables were found. The final models obtained in terms of actual factors are given below: Logit (Ash Removal%) ¼ Ln[(Ash Removal%)/(100 Ash removal %)] ¼ 5.86149 þ 0.13152*Temperature þ 0.04995*Time þ 0.21581 *NaOH þ 0.00266*Temperature*NaOH 0.00092*Temperature2 0.00926*NaOH2 (4) Solid Yield% ¼ 100.08644 0.15385*Temperature 0.87409*Time 0.079265*NaOH þ 0.00384*Temperature*Time 0.011538*Temperature*NaOH 0.016667*Time*NaOH þ 0.020789*Time2 þ 0.012701*NaOH2 (5)
Comments
3.3. Diagnostics and adequacy checking Suggested Aliased
It is imperative to check the adequacy of the fitted models before proceeding to optimization in order to avoid misleading results. As
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
515
Table 5 ANOVA for response 1 (ash removal) before removal of insignificant terms. Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comment
Model X1-Temperature X2-Time X3-NaOH concentration X1X2 X1X3 X2X3 X21 X22 X23 Residual Lack of fit
69.320 30.946 1.996 19.623
9 1 1 1
7.702 30.946 1.996 19.623
19.558 78.581 5.068 49.828
0.0004 <0.0001 0.0591 0.0002
Significant
0.019 4.293 0.004 3.810 0.499 7.118 2.757 2.319
1 1 1 1 1 1 7 3
0.019 4.293 0.004 3.810 0.499 7.118 0.394 0.773
0.049 10.900 0.009 9.674 1.267 18.075
0.8310 0.0131 0.9261 0.0171 0.2974 0.0038
Not significant
7.058
0.0447
Significant
Std. dev. 0.63
PRESS 37.78
C.V. % 40.93
Adeq precision 14.681
R-squared 0.962
Adj R-squared 0.913
Pred R-squared 0.476
Not significant Not significant
Table 6 ANOVA for response 1 (ash removal) after removal of insignificant terms. Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comment
Model X1-Temperature X2-Time X3-NaOH concentration X1X3 X21 X23 Residual Lack of Fit
68.798 30.946 1.996 19.623 4.293 3.967 7.338 3.279 2.841
6 1 1 1 1 1 1 10 6
11.466 30.946 1.996 19.623 4.293 3.967 7.338 0.328 0.473
34.973 94.387 6.087 59.851 13.093 12.100 22.382
<0.0001 <0.0001 0.0333 <0.0001 0.0047 0.0059 0.0008
Significant
4.324
0.0890
not Significant
Std. dev. 0.52
PRESS 16.08
C.V. % 37.34
Adeq precision 19.231
R-squared 0.955
Adj R-squared 0.927
Pred R-squared 0.777
Table 7 ANOVA for response 2 (solid yield) before removal of insignificant terms. Source
Sum of squares
Degrees of freedom
Mean square
F-value
p-Value
Comment
Model X1-Temperature X2-Time X3-NaOH concentration X1X2 X1X3 X2X3 X21 X22 X23 Residual Lack of fit
1212.285 528.125 66.125 480.500
9 1 1 1
134.698 528.125 66.125 480.500
238.706 935.918 117.184 851.519
<0.0001 <0.0001 <0.0001 <0.0001
Significant
6.250 81.000 16.000 0.024 18.129 14.024 3.950 2.750
1 1 1 1 1 1 7 3
6.250 81.000 16.000 0.024 18.129 14.024 0.564 0.917
11.076 143.544 28.354 0.042 32.127 24.852
0.0126 <0.0001 0.0011 0.8435 0.0008 0.0016
3.056
0.1544
Not significant
Std. dev. 0.751
PRESS 45.875
C.V. % 0.970
Adeq precision 57.061
R-squared 0.997
Adj R-squared 0.993
Pred R-squared 0.962
shown in Fig. 1, the normal probability plots of the internally studentized residuals are linear. In calculating the internally studentized residuals, a residual is compared to the residual variance (entire set including itself). With this simple graphical method, the normal distribution of the observed data is confirmed. If the data in Fig. 1 were not linear, it would indicate that the error terms are not normally distributed, and suggest that the model's underlying assumptions may have been violated. Fig. 2 satisfactorily shows that the residuals are randomly scattered when plotted against the predicted response. Furthermore, Fig. 3 shows that the
Not significant
experimental data is in fair agreement with the values predicted by the developed models (with the solid yield response showing a more accurate predictive capability). Hence the adequacies of the developed models are confirmed. 3.4. Response surfaces Fig. 4 depicts the response surfaces showing the effects of the independent variables on the ash removal and solid yield. Regarding the solid yield, it is evident that as the temperature,
516
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
Fig. 1. Normal probability plot for responses (a) ash removal and (b) solid yield.
reaction duration, and alkali concentration increase, there is a larger loss of solids. Although all three factors show significant effects, the reaction temperature and alkali concentration appear to be more influential on the solid yield than the treatment duration.
At higher temperatures, the NaOH concentration has a drastic effect on the solid yield. This effect is less pronounced at lower temperatures. Regarding the ash removal, from among the three variables considered, the duration is of least influence. The combination of
Fig. 2. Plot of internally studentized residuals vs. predicted response for (a) ash removal and (b) solid yield.
Fig. 3. Diagnostic plots of the model precision for (a) ash removal and (b) solid yield.
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
517
Fig. 4. Response surfaces for (a) ash removal and (bed) solid yield. For all surfaces, the input variables not shown are fixed at the center level.
high temperature and high alkali concentration leads to highest ash removal. It is also noteworthy that 13% NaOH is high enough and removes more than 90% of the ash at moderate temperatures (57.5 C). Any further increase of temperature or concentration does not significantly increase silica ash removal. The slightly higher ash content when the NaOH concentration is increased above 13% could be ascribed to the fact that the sodium which does not react with the silica to form soluble silicate, can attach to the fiber in other forms. This, in turn, adds to the final measured ash amount upon thermal degradation. Hsieh et al. [1] concluded that at a fixed reaction duration, the reaction temperature can enhance SiO2 removal slightly while a change of NaOH concentration has remarkable influence. They found that a 1:5 ratio of NaOH:husk at 50 C for 3 h was adequate to remove nearly all the soluble silica while retaining 63% of the organic matter as solid residue. Further increase of the temperature or alkali concentration did not greatly enhance the silica removal. Approximately 14% of the rice husk was soluble when the treatment was done with pure water. Johar et al. [37] have reported that almost all the silica was removed with alkali treatment under reflux for 6 h (3 runs, 2 h each). The alkali treatment was reported to efficiently remove hemicellulose as well. The smooth surface of the untreated rice husk becomes rough after alkali treatment. This was ascribed to the removal the outer non-cellulosic layer composed of materials such as hemicelluloses, lignin, pectin (protective layer), wax, and other impurities. The diameter of the husk fiber bundles were reported to remain constant after alkali treatment indicating that a large fraction of the lignin (which acts as a cementing agent)
persisted. According to Ndazi et al. [31] higher temperatures, longer treatment, and higher concentration removes more lignin, but at lower temperatures the lignin might not degrade. In rice husks, alkali treatment has previously been shown to break the cell wall by dissolving hemicellulose, lignin, and silica; by hydrolyzing uronic and acetic acid esters; and by swelling cellulose [50]. Endwise degradation, also known as endwise depolymerization or peeling, of cellulose can also occur in alkali media [51]. The hydrogen bond of cellulose is disrupted as a result of alkali treatment and thereby the surface roughness of the samples increases. When cellulose is mercerized, the hydrolysis rate increases markedly (70%). This means that the number of accessible glycosidic linkages in mercerized cellulose is higher than the native one [52]. Hemicelluloses contain a variety of sugar units in addition to glucose and can act as a physical barrier which surrounds and protects the cellulose fibers [53]. The nonglucose units of hemicellulose exhibit distinctly different reactivities when compared to glucose units because of their different ring structures and/or hydroxyl configurations. Generally, hemicelluloses are more reactive than cellulose. Their degradation is similar to that of cellulose but proceeds much more readily and extensively [52]. If the alkali concentration is increased above the limits of this paper, the rice husk will break down to a puffy (mercerized) material which can be used as a sorbent for liquids such as in oil spills [54,55]. Silica has a melting point above 1400 C. In addition to adhesion effects of the sintered ash, in combustion, the alkali oxides and salts can react with silica to form eutectic mixtures. These compounds such as the one formed through the following reaction have
518
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
melting points below that of the individual components (below 800 C) [28]: 4SiO2 þ K2CO3 / K2O $ 4SiO2 þ CO2 Hence the more minerals are removed from the rice husks, the less the operational problems arising from agglomeration, fouling, slagging, and clogging will be. For a review of possible operation problems arising from excessive ash content in combustion systems, refer to the work of Van Caneghem et al. [26]. The silicon in rice husk is thought to be bound to the organic molecules. Since cellulose and lignin do not possess considerable bonding ability with silicon, hemicellulose has been speculated to be responsible for this binding. This can be attributed to the polar electrometric effect in the aldehyde groups [48,56]:
The nature of the silicon bonding is believed to be complex. It has been speculated that it is bound to four carbohydrates as shown below [48,56]:
When these bonds are broken, the SieO groups become attached to each other and form low temperature silica. The removal of silica by alkali treatment is thought to be the result of the following reaction: SiO2 þ 2NaOH / Na2SiO3 þ H2O The leached silica in the liquid can then be used for the production of porous silica-based materials. 3.5. Optimization Optimization has been defined as improving a system's performance in order to obtain the maximum benefit from it [57]. When a set of responses are influenced by several variables, the system is optimized with the objective to simultaneously adjust the levels of the input variables in order to attain the most desirable overall response. In the previous section we have already established the effects of temperature, duration, and alkali ratio on the solid yield and silica removal. First, the system will be optimized with the following criteria: 1. To minimize the loss of solid residue due to the treatment 2. To maximize the removal of silica ash from the solid residue The following two criteria will be added for a second round of optimization: 3. To minimize the use of alkali (for cost-effectiveness of process) 4. To have the most robust design (i.e. minimum propagation of error for both ash removal and solid yield) Errors in input variables such as temperature, duration, and concentration can transmit to the response (silica removal and solid yield). In examining the Propagation of Error (POE), conditions are sought which minimize the transmitted variation of inputs thus “creating a process that's robust to the factor settings” [58]. The
lower POE values are, “the less of the error in control factors will be transmitted to the selected response which results in a more robust process” [58]. If the response surface is linear, POE plots become valueless. This is because under linear conditions errors will be transmitted equally throughout the region. Here, in POE analysis, a standard deviation of 0.5 is chosen for each input variable.
Table 8 Operation parameters under different optimization scenarios. Proposed values for input variables
Optimization criteria
Temperature Time (hr) ( C)
1 and 2 only 59.5 (equal weight) 1, 2, 3, and 4 44.3 (equal weight) 48.4 1 and 2 (more weight), 3 and 4 (less weight)
Predicted responses under optimized conditions
Concentration Ash Solid (%) removal (%) yield (%)
4
15.7
92.6
78.7
22.5
9.8
84.4
79.7
10.2
89.2
78.8
24
With the inclusion of additional optimization criteria, or designating different weights to imposed criteria, various optimized operation conditions emerge. A trade-off occurs during optimization as all criteria cannot be met 100%. The optimization process implies trying to meet the criteria (even when conflicting) as best as possible. As a case in point, adding criteria 3 to the optimization process leads to a decrease in silica removal which is unwanted. This could be expected because designating ‘using less alkali’ as an optimization target logically leads to less soluble sodium silicate formation. Ultimately the optimized operating conditions and the weight of each criterion depend on the choice of the end-user. For example, under conditions in which access to NaOH is difficult and costly, a higher silica content in the final residue may be tolerated. Meanwhile, if cheap alkali is accessible and/or the CHP unit to be used is highly sensitive to ash content, the importance of criterion 2 could be increased while the importance of criterion 3 is diminished. For a more clear understanding, the results of various optimization scenarios have been tabulated in Table 8. Note that numerous solutions are provided during optimization, and Table 8 only shows the ones with the highest desirability. The results from Table 8 show that with optimum operating conditions, nearly 80% of the solid could be kept while approximately 90% of the ash is removed. Such conditions could be very advantageous for more favorable utilization of rice husks as solid fuel. Note that the amount of silica in the raw husk sample used was approximately 12% dry weight. Hence if the husks used have a higher initial silica content, the results would be expected to shift. 3.6. Model validation In order to verify whether the proposed models can accurately estimate the behavior of the system additional experiments are performed and compared to the estimated values from the model. These points are chosen within the design area and not superimposing any previous experimental condition used for developing the model. The values for 95% PI low and 95% PI high (not shown here) designate the range in which the actual response should be. If the actual experimental result for any point is out of the corresponding range, the model is negated. Here, all validation experiments led to response values within the appropriate range and hence validated the model. Table 9 shows two experimental runs used for validation.
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520 Table 9 Validation experiments for the proposed models.
Temperature ( C) Duration (hr) Alkali concentration (%) Actual ash removal (%) Predicted ash removal (%) Actual solid yield (%) Predicted solid removal (%)
Validation experiment 1
Validation experiment 2
70 14 14 99 96.7 71 71.7
58 16 23 92 95.8 71 69.4
4. Conclusion Satisfactory utilization of rice husks for electricity or heat generation is often hampered due to the high ash content of these wastes. Here, a simple alkali treatment is proposed for the removal of the silica ash, so that problems such as clogging, fouling, and slagging could be avoided during energy generation processes. Response Surface Methodology (RSM) with a BoxeBehnken design (BBD) has been used to consider the three variables of reaction temperature, duration, and alkali concentration. The results showed that with optimum operating conditions, nearly 80% of the rice husk can be retained in the solid phase while approximately 90% of the ash is removed. Such pretreatment could therefore be very suitable for rendering the rice husks more favorable for energy production, while retaining a large portion of the organics in the solid phase. Future work may focus on a more detailed understanding of the ash removal mechanism from the rice husks. This could include mass and energy balances for the extraction of minerals (in particular silica), kinetics analyses, and chemical equilibria. Acknowledgments The authors would like to sincerely thank the staff at the MCPF and AEMF facilities at the Hong Kong University and Technology, as well as Mr. Ronnie Lo of Peako Biomass Energy Co. for their assistance. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.renene.2014.11.072. References [1] Hsieh Y, Du Y, Jin F, Zhou Z, Enomoto H. Alkaline pre-treatment of rice hulls for hydrothermal production of acetic acid. Chem Eng Res Des 2009;87:13e8. [2] FAOSTAT. Food and Agriculture Organization of the United Nations Statistics. 2013. [3] Juliano BO. Rice hull and rice straw. In: Juliano BO, editor. Rice: chemistry and technology. 2nd ed. American Association of Cereal Chemists; 1985. p. 689e755. [4] Adam F, Appaturi JN, Iqbal A. The utilization of rice husk silica as a catalyst: review and recent progress. Catal Today 2012;190:2e14. [5] Wang W, Martin J, Zhang N, Ma C, Han A, Sun L. Harvesting silica nanoparticles from rice husks. J Nanopart Res 2011;13:6981e90. €m K, Andersson S, Tervala L, Torkkeli M, Knaapila M, et al. [6] Virtanen T, Svedstro A physico-chemical characterisation of new raw materials for microcrystalline cellulose manufacturing. Cellulose 2012;19:219e35. [7] Nazemi L, Nazmara S, Eshraghyan MR, Nasseri S, Djafarian K, Yunesian M, et al. Selenium status in soil, water and essential crops of Iran. Iran J Environ Health Sci Eng 2012;9. [8] Mehdinia SM, Latif PA, Taghipour H. A comparative evaluation of dried activated sludge and mixed dried activated sludge with rice husk silica to remove hydrogen sulfide. Iran J Environ Health Sci Eng 2013;10. [9] Chowdhury S, Das Saha P. Adsorption of malachite green from aqueous solution by NAOH-modified rice husk: fixed-bed column studies. Environ Prog Sustain Energy 2013;32:633e9.
519
[10] Mehdinia SM, Latif PA, Taghipour H. Removal of hydrogen sulfide by physicobiological filter using mixed Rice husk silica and dried activated sludge. CLEAN Soil Air Water 2013;41:949e54. [11] Kordatos K, Ntziouni A, Iliadis L, Kasselouri-Rigopoulou V. Utilization of amorphous rice husk ash for the synthesis of ZSM-5 zeolite under low temperature. J Mat Cycl Waste Manage 2013;15:571e80. [12] Xing A, Tian S, Tang H, Losic D, Bao Z. Mesoporous silicon engineered by the reduction of biosilica from rice husk as a high-performance anode for lithiumion batteries. RSC Adv 2013;3:10145e9. [13] Chen H, Wang W, Martin JC, Oliphant AJ, Doerr PA, Xu JF, et al. Extraction of lignocellulose and synthesis of porous silica nanoparticles from rice husks: a comprehensive utilization of rice husk biomass. ACS Sustain Chem Eng 2012;1:254e9. [14] Ugheoke B, Mamat O, Ari-Wahjoedi B. Thermal expansion behavior, phase transitions and some physico-mechanical characteristics of fired doped rice husk silica refractory. J Adv Ceram 2013;2:79e86. [15] Alyosef HA, Eilert A, Welscher J, Ibrahim SS, Denecke R, Schwieger W, et al. Characterization of biogenic silica generated by thermo chemical treatment of rice husk. Part Sci Technol 2013;31:524e32. [16] Gu S, Zhou J, Luo Z, Wang Q, Ni M. A detailed study of the effects of pyrolysis temperature and feedstock particle size on the preparation of nanosilica from rice husk. Ind Crops Prod 2013;50:540e9. [17] Gupta R, Pandit A, Nirjar A, Gupta P. Husk power systems: bringing light to rural India and tapping fortune at the bottom of the pyramid. Asian J Manag Cases 2013;10:129e43. [18] Mustonen S, Raiko R, Luukkanen J. Bioenergy consumption and biogas potential in cambodian households. Sustain Switz 2013;5:1875e92. [19] Yoon SJ, Son Y-I, Kim Y-K, Lee J-G. Gasification and power generation characteristics of rice husk and rice husk pellet using a downdraft fixed-bed gasifier. Renew Energy 2012;42:163e7. [20] Shackley S, Carter S, Knowles T, Middelink E, Haefele S, Haszeldine S. Sustainable gasificationebiochar systems? A case-study of rice-husk gasification in Cambodia, part II: field trial results, carbon abatement, economic assessment and conclusions. Energy Policy 2012;41:618e23. [21] Shackley S, Carter S, Knowles T, Middelink E, Haefele S, Sohi S, et al. Sustainable gasificationebiochar systems? A case-study of rice-husk gasification in Cambodia, part I: context, chemical properties, environmental and health and safety issues. Energy Policy 2012;42:49e58. [22] Abu Bakar MS, Titiloye JO. Catalytic pyrolysis of rice husk for bio-oil production. J Anal Appl Pyrolysis 2013;103:362e8. [23] Qi P, Lin N, Liu Y, Zhao J. Improvement of oil/water selectivity by stearic acid modified expanded perlite for oil spill cleanup. J Shanghai Jiaot Univ Sci 2013;18:500e7. [24] Karmakar MK, Mandal J, Haldar S, Chatterjee PK. Investigation of fuel gas generation in a pilot scale fluidized bed autothermal gasifier using rice husk. Fuel 2013;111:584e91. [25] Lim JS, Abdul Manan Z, Wan Alwi SR, Hashim H. A review on utilisation of biomass from rice industry as a source of renewable energy. Renew Sustain Energy Rev 2012;16:3084e94. [26] Van Caneghem J, Brems A, Lievens P, Block C, Billen P, Vermeulen I, et al. Fluidized bed waste incinerators: design, operational and environmental issues. Prog Energy Combust Sci 2012;38:551e82. HJ. [27] Virmond E, De Sena RF, Albrecht W, Althoff CA, Moreira RFPM, Jose Characterisation of agroindustrial solid residues as biofuels and potential application in thermochemical processes. Waste Manag 2012;32:1952e61. [28] Armesto L, Bahillo A, Veijonen K, Cabanillas A, Otero J. Combustion behaviour of rice husk in a bubbling fluidised bed. Biomass Bioenergy 2002;23:171e9. [29] Bakker RR, Jenkins BM, Williams RB. Fluidized bed combustion of leached rice straw. Energy Fuels 2002;16:356e65. [30] Archibald JG. The effect of sodium hydroxid on the composition, digestibility, and feeding value of grain hulls and their fibrous material. J Agric Res 1924;27:245e65. [31] Ndazi BS, Nyahumwa C, Tesha J. Chemical and thermal stability of rice husks against alkali treatment. BioResources 2008;3:1267e77. [32] Ndazi BS, Karlsson S, Tesha JV, Nyahumwa CW. Chemical and physical modifications of rice husks for use as composite panels. Compos Part A Appl Sci Manuf 2007;38:925e35. varo SL, Lopes MS, Vieira de Carvalho Neto AG, Roge rio de Santana R, [33] Fa Radovanovic E. Chemical, morphological, and mechanical analysis of rice husk/post-consumer polyethylene composites. Compos Part A Appl Sci Manuf 2010;41:154e60. [34] Chakraborty S, Chowdhury S, Das Saha P. Adsorption of Crystal Violet from aqueous solution onto NaOH-modified rice husk. Carbohydr Polym 2011;86: 1533e41. [35] Salgado-Delgado R, Vazquez SR, Alvarez-Castillo A, Licea-Claveríe A, ~ o VM. Chemical modification of rice husk surface through alkaline Castan treatments. Mater Technol 2005;20:26e9. [36] Singh A, Tuteja S, Singh N, Bishnoi NR. Enhanced saccharification of rice straw and hull by microwave-alkali pretreatment and lignocellulolytic enzyme production. Bioresour Technol 2011;102:1773e82. [37] Johar N, Ahmad I, Dufresne A. Extraction, preparation and characterization of cellulose fibres and nanocrystals from rice husk. Ind Crops Prod 2012;37: 93e9. [38] Kuan C-Y, Yuen K-H, Liong M-T. Physical, chemical and physicochemical characterization of rice husk. Br Food J 2012;114:853e67.
520
A. Bazargan et al. / Renewable Energy 77 (2015) 512e520
[39] Huntanuwatr N, Hunds FC, Davis C. An evaluation of methods for improving the in vitro digestibility of rice hulls. J Anim Sci 1974;38:140e8. [40] McManus WR, Choung CC. Studies on forage cell walls: 2. Conditions for alkali treatment of rice straw and rice hulls. J Agric Sci 1976;86:453e70. [41] Taheri M, Alavi Moghaddam MR, Arami M. Optimization of Acid Black 172 decolorization by electrocoagulation using response surface methodology. Iran J Environ Health Sci Eng 2012;9. [42] Usharani K, Muthukumar M. Optimization of aqueous methylparathion biodegradation by Fusarium sp in batch scale process using response surface methodology. Int J Environ Sci Technol 2013;10:591e606. [43] Jadhav SB, Surwase SN, Phugare SS, Jadhav JP. Response surface methodology mediated optimization of Remazol Orange decolorization in plain distilled water by Pseudomonas aeruginosa BCH. Int J Environ Sci Technol 2013;10: 181e90. [44] Mohamed RM, Mkhalid IA, Abdel Salam M, Barakat MA. Zeolite Y from rice husk ash encapsulated with Ag-TiO2: characterization and applications for photocatalytic degradation catalysts. Desalin Water Treat 2013;51:7562e9. [45] Bohra S, Dey K, Kundu D, Naskar M. Synthesis of zeolite T powders by direct dissolution of rice husk ash: an agro-waste material. J Mater Sci 2013;48: 7893e901. [46] Braga R, Barros JF, Melo DA, Melo MF, Aquino FM, Freitas JO, et al. Kinetic study of template removal of MCM-41 derived from rice husk ash. J Therm Anal Calorim 2013;111:1013e8. [47] Bazargan A, Gebreegziabher T, Hui CW, McKay G. The effect of alkali treatment on rice husk moisture content and drying kinetics. Biomass and Bioenergy 2014;70:468e75. http://dx.doi.org/10.1016/j.biombioe.2014.08.018. [48] Patel M, Karera A, Prasanna P. Effect of thermal and chemical treatments on carbon and silica contents in rice husk. J Mater Sci 1987;22:2457e64.
[49] Samavati V, Adeli M. Isolation and characterization of hydrophobic compounds from carbohydrate matrix of Pistacia atlantica. Carbohydr Polym 2014;101:890e6. [50] Jackson MG. Review article: the alkali treatment of straws. Anim Feed Sci Technol 1977;2:105e30. [51] Li X, Tabil LG, Panigrahi S. Chemical treatments of natural fiber for use in natural fiber-reinforced composites: a review. J Polym Environ 2007;15: 25e33. [52] Lai Y-Z. Reactivity and accessibility of cellulose, hemicelluloses, and lignins. In: Hon DN-S, editor. Chemical modification of lignocellulosic materials. New York: Marcel Dekker; 1996. p. 35e95. [53] Taherzadeh MJ, Karimi K. Pretreatment of lignocellulosic wastes to improve ethanol and biogas production: a review. Int J Mol Sci 2008;9:1621e51. [54] Bazargan A, Tan J, Hui C, McKay G. Utilization of rice husks for the production of oil sorbent materials. Cellulose 2014;21:1679e88. [55] Bazargan A, Hui C, Mckay G. Marine residual fuel sorption and desorption kinetics by alkali treated rice husks. Cellulose 2014;21:1997e2006. [56] Vlaev L, Petkov P, Dimitrov A, Genieva S. Cleanup of water polluted with crude oil or diesel fuel using rice husks ash. J Taiwan Inst Chem Eng 2011;42: 957e64. [57] Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 2008;76:965e77. [58] Design expert 7 user's guide, multifactor RSM tutorial e part 3: adding propagation of error (POE) to the optimization, design expert 7 user's guide. 2004. p. 1e10.