Statistical Experimental Design For The Optimisation ... - IngentaConnect

South African Journal of Chemical Engineering, vol. 19, 2014, no. 2, pp 32-41

32

Statistical Experimental Design For The Optimisation Of Dilute Sulphuric Acid Hydrolysis Of Oil Palm Empty Fruit Bunch Amenaghawon Andre, Ogbeide Samuel, Okieimen Charity University of Benin, Chemical Engineering Department Keywords: Lignocellulosic biomass, Fermentable sugar, Box-Behnken design, Response Surface Methodology, Optimisation Abstract-Dilute acid hydrolysis was applied for the pretreatment of oil palm empty fruit bunch (OPEFB) to produce fermentable sugars. Chemical composition analysis of the OPEFB used in this study revealed that the major components were cellulose, hemicellulose and lignin which accounted for 40.2, 22.1 and 19.2% of the material respectively. The OPEFB was subjected to dilute sulphuric acid hydrolysis and the effect of temperature (100–150 oC), acid concentration (0.5–3.0 %w/w), time (10–40 min) and liquid to solid ratio (30–40 mL/g) on the hydrolysis process was quantitatively evaluated using a four variable Box-Behnken design. Response surface methodology (RSM) was used to optimise the hydrolysis process in order to obtain maximum fermentable sugar yield. The optimum hydrolysis temperature, acid concentration, time and liquid to solid ratio were obtained as 150 oC, 3 %w/w, 30 minutes and 40 mL/g respectively. Under these conditions, the maximum sugar yield was obtained to be 94.74%.

INTRODUCTION Most of the liquid fuels used for road transportation are currently derived from petroleum based sources. The burning of these fuels in internal combustion engines contributes to environmental pollution through the emission of greenhouse gases (Gomez et al., 2008). The transportation sector is very much dependent on crude oil which is mainly produced in the politically unstable regions of the Middle East (Hosseini and Shah, 2009). Moreover, the exploitation and production of crude oil has resulted in political crises and differences in certain countries such as Nigeria and Sudan. As a result of these and the increased awareness of the effects of fossil fuel usage on the environment, bioethanol fuel has received considerable attention as a suitable alternative to petroleum based fuels. Bioethanol is renewable and can be produced from agricultural products with the potential to lessen the dependence on fossil fuels (Amenaghawon et al., 2013). Bioconversion of lignocellulosic biomass to fermentable sugars and subsequent fermentation to bioethanol has been considered to be a cost effective process route for the production of bioethanol (Chen et al., 2009). Lignocellulosic biomass include naturally occurring materials such as waste paper and paper products, wood and wood wastes, agricultural and forest residues. These materials are renewable and abundantly available at little or no cost which makes them suitable feedstocks for bioethanol production (Agbro and Ogie, 2012). Several waste products typically result from the processing of oil palm fruit to palm oil. Of these waste products, OPEFB is produced in the highest proportion (Piarpuzan et al., 2011). With the demand for fresh palm oil increasing, the oil palm industry in countries such as Nigeria and Malaysia has expanded and this has resulted in the production of large amounts of OPEFB (Hassan et al.,2013). In Nigeria, about 80% of the 2.4 million hectares of palm plantations are due for replanting and about 134 million tonnes of biomass is expected to be generated from this operation. This represents an enormous resource waiting to be exploited. Oil palm biomass is currently employed in the mill as solid fuel, in the plantation as mulch to enhance moisture and as organic fertiliser. However, in order for the oil palm industry to run in a sustainable and environmentally friendly manner, there is a need to expand the reuse capacity of the OPEFB by processing them into value added products (Hassan et al., 2013; Rahman et al., 2007). The lignocellulosic biomass from OPEFB has been identified as a suitable feedstock for the production of bioethanol and other renewable fuels (Piarpuzan et al., 2011). The pretreatment of lignocellulosic materials to produce fermentable sugars is a very important step as it determines the sustainability of producing bioethanol from these materials (Zhang et al., 2012). Different pretreatment strategies utilising physical, chemical and biological processing have been adopted for altering the structural and chemical compositions of lignocellulose to enhance the yield of fermentable sugars (Fang et al.,

South African Journal of Chemical Engineering, vol. 19, 2014, no. 2, pp 32-41

33

2010). Amongst these strategies, dilute acid hydrolysis has received the most attention. It has been applied for the pretreatment of a lot of lignocellulosic materials as a result of its promising commercial potential (Amenaghawon et al., 2013; Amenaghawon et al., 2014; Canettieri et al., 2007; Ferrer et al., 2013; Hu et al., 2010; Martin et al., 2006; Zhang et al., 2012; Zhao et al., 2008). Previous investigations carried out on the dilute acid hydrolysis of different lignocellulosic materials such as sugar cane bagasse, sorghum straw, corn cobs and eucalyptus wood chips showed that xylose was the main sugar produced when the process was carried out under controlled condition (Rahman et al., 2007). It has also been reported that the type of biomass used and the operating conditions of the process such as hydrolysis time, biomass particle size, hydrolysis temperature and acid concentration influenced the amount of sugars produced during hydrolysis (Hosseini and Shah, 2009). Acid concentration is an important variable that influences the rate of sugar production while temperature if not controlled could result in the degradation of sugars to by-products such as acetic acid and furfural which are inhibitors to fermenting microorganisms (Rahman et al., 2007; Zhang et al., 2012). To maximise the yield of fermentable sugars while minimising the concentration of by products, it is necessary to optimise the variables which influence the hydrolysis process. Response surface methodology based on statistically designed experiments has been found to be very useful in optimising multivariable processes. It is employed for multiple regression analysis of quantitative data obtained from statistically designed experiments (Montgomery 2005). The aim of this study was to determine the effect of acid concentration, hydrolysis temperature, time and liquid to solid ratio on the production of fermentable sugars from OPEFB. Box-Behnken design for RSM was adopted to optimise the hydrolysis process for maximum sugar formation.

MATERIALS AND METHODS Raw materials OPEFB was provided by the Nigerian Institute for Oil Palm Research (NIFOR) in Edo State, Southern Nigeria. It was sun dried and milled to 1 mm particles. The homogenised OPEFB was oven dried overnight at 105 oC prior to analysis (Najafpour et al., 2007). Chemical composition analysis Chemical composition analysis of the OPEFB was carried out to determine the percentage of structural carbohydrates, lignin, ash and extractives according to the National Renewable Energy Laboratory (NREL) standard analytical procedures. The moisture content of the OPEFB was determined gravimetrically (Sluiter et al., 2008a). Structural carbohydrates were determined by quantitative acid hydrolysis of the extractive-free material through chromatographic quantification using a High Performance Liquid Chromatography (HPLC) system equipped with an Aminex HPX- 87 P column (Bio-Rad, USA) and refractive index (RI) detector (Refracto MonitorR III, Model 1109, LDC/Milton Roy, USA) (Sluiter et al., 2008b). Total extractives determination was carried out by ethanol extraction in a Soxhlet extraction apparatus (Sluiter et al., 2008c). Klason and acid-soluble lignin content were determined following the quantitative acid hydrolysis step (Sluiter et al., 2008b). The ash content was determined after combustion at 575 oC (Sluiter et al., 2008d). Dilute sulphuric acid hydrolysis Dilute acid hydrolysis of OPEFB was carried out in an autoclave. The operating conditions of the hydrolysis reaction were as follows: sulphuric acid concentration (0.5-3 %w/w), temperature (100- 150oC), time (10-40 min) and liquid to solid ratio (10-40 mL/g). At the end of the hydrolysis process, the solid residue was separated by centrifugation and the pH of the resulting supernatant was adjusted to 10 using 2N Ca(OH) 2. The resulting precipitate was removed by centrifugation and the supernatant was adjusted to a pH of 6.5 using 10% H2SO4 (Silva et al., 1998). Analytical methods The amount of fermentable sugars produced during hydrolysis was expressed as total reducing sugar yield. This was quantified by the colorimetric method using glucose as standard (Miller, 1959). The reducing sugars in the final hydrolysate were reacted with 3,5-dinitro-salicylic acid (DNSA) which was reduced to 3-amino-5-nitrosalicylic acid. This was quantified by measuring the absorbance at a wavelength of 540 nm using a UV-Vis

South African Journal of Chemical Engineering, vol. 19, 2014, no. 2, pp 32-41

34

spectrophotometer (PG Instruments model T70). The reducing sugars were measured as follows: To a test tube were added the following; 0.2 mL reducing sugar solution, 1.8 mL distilled water and 2 mL DNSA reagent. The mixture in the test tube was boiled for 5 minutes in a water bath followed by cooling to room temperature and dilution to 24 mL. A glucose calibration curve was prepared from known concentrations of glucose from which the yield of reducing sugars was determined. Experimental design A four variable Box-Behnken design (BBD) was employed to fit a second order statistical model to the experimental data. The experimental design made up of 29 runs and the statistical model were developed using Design ExpertR 7.0.0 (Stat-ease, Inc. Minneapolis, USA). The second order model that was selected for predicting the optimal point is expressed as: Yi = bo + ΣbiXj + ΣbijXiXj + ΣbiiXi2+ei

(1)

where Yi is predicted response, Xi and Xj are independent variables, bo and bi are offset and linear effects terms respectively while bij and bii are interaction terms and ei is the error term. The independent variables were coded according to Eq. (2). X −X xi = i o (2) ∆X i

where xi and Xi are the coded and actual values of the independent variable respectively. Xo is the actual value of the independent variable at the centre point and IXi is the step change in the actual value of the independent variable.

RESULTS AND DISCUSSION Chemical composition of empty fruit bunch Figure 1 shows the result of chemical composition analysis of the OPEFB. The major components of the raw material were determined to be cellulose, hemicellulose and lignin (klason and acid soluble) each accounting for 40.2, 22.1 and 19.2% respectively. Figure 1 also shows that the composition of the OPEFB used in this study was similar to that of other lignocellulosic biomass typically used for bioethanol production (Han et al., 2011; Kim and Kim, 2013; Martin et al., 2006; Rahman et al., 2007). Cellulose had the highest percentage in the raw OPEFB. In contrast, the percentage of lignin in the OPEFB was almost half that of cellulose. The compositional percentage of cellulose makes OPEFB a natural feedstock from which fermentable sugars can be recovered. Also, the low percentage of lignin makes it easier to get access to the cellulose during hydrolysis using acids or enzymes (Piarpuzan et al., 2011).

Figure 1: Chemical composition of OPEFB compared with other biomass resources

35

South African Journal of Chemical Engineering, vol. 19, 2014, no. 2, pp 32-41

Statistical modelling The experimental range and levels of independent variables i.e. hydrolysis temperature (X 1), acid concentration (X2), hydrolysis time (X3) and liquid to solid ratio (X4) studied are shown in Table 1. The results obtained from the 29 experimental runs carried out according to the Box-Behnken design are shown in Table 2. The chosen response was the yield of fermentable sugars Y (%). The quadratic model in terms of actual variables is shown in Eq. (3). Y = 776.497-2.863X1-9.859X2+2.339X3-32.272X4+0.0808X1X2+0.00451X1X3 +0.0698X1X4+0.111X2X3-0.020X3X4+0.00128X12+0.836X22-0.0449X32+0.338X42

(3)

where Y represents sugar yield as a function of hydrolysis temperature (X1), acid concentration (X2), hydrolysis time (X3) and liquid to solid ratio (X4). Table 1: Experimental range and levels for independent variables Coded and Actual Levels Independent variables Symbols -1 0 Temperature (°C) X1 100 125 Acid concentration (w/w %) X2 0.5 1.75 Time (min X3 10 25 Liquid to Solid Ratio (mL/g) X4 30 35

1 150 3.0 40 40

The predicted response levels of total sugar yield according to Eq. (3) are also presented in Table 2. The fit of the statistical model was assessed by performing analysis of variance (ANOVA) and the results are presented in Tables 3 and 4. Table 2: Box-Behnken design matrix for the optimisation of variables during acid hydrolysis Factors Run No 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

X1 -1 0 -1 1 1 0 0 -1 1 1 0 1 0 0 0 -1 -1 0 1 -1 0 0 0 0 0 0 0 0 0

Coded levels X2 X3 0 -1 1 1 1 0 1 0 0 1 1 0 0 0 0 1 -1 0 0 0 -1 1 0 0 0 0 1 0 1 -1 -1 0 0 0 0 1 0 -1 0 0 0 0 0 1 -1 0 0 -1 -1 -1 0 0 -1 0 0 -1 0 0

X4 0 0 0 0 0 -1 0 0 0 1 0 -1 0 1 0 0 1 -1 0 -1 0 1 1 1 0 0 -1 -1 0

X1 100 125 100 150 150 125 125 100 150 150 125 150 125 125 125 100 100 125 150 100 125 125 125 125 125 125 125 125 125

Actual values X2 X3 1.75 10 3.00 40 3.00 25 3.00 25 1.75 40 3.00 25 1.75 25 1.75 40 0.50 25 1.75 25 0.50 40 1.75 25 1.75 25 3.00 25 3.00 10 0.5 25 1.75 25 1.75 40 1.75 10 1.75 25 1.75 25 1.75 40 0.5 25 1.75 10 0.5 10 1.75 25 0.5 25 1.75 10 1.75 25

X4 35 35 35 35 35 30 35 35 35 40 35 30 35 40 35 35 40 30 35 30 35 40 40 40 35 35 30 30 35

Response Sugar Yield (%) Observed Predicted 52.33 49.54 64.96 66.19 65.05 66.39 80.33 79.22 60.15 61.77 83.59 82.43 66.53 63.27 51.90 50.61 57.31 59.32 84.48 83.16 50.23 47.20 70.98 69.66 66.53 63.27 79.62 78.47 56.75 57.60 52.13 56.59 58.78 57.92 66.58 67.32 53.82 53.94 80.20 79.34 66.53 63.27 59.62 60.35 63.64 63.62 59.29 58.91 50.32 46.90 66.53 63.27 67.60 67.58 57.52 59.86 66.53 63.27

36

South African Journal of Chemical Engineering, vol. 19, 2014, no. 2, pp 32-41

The model F-value was 10.57 while the p-value was less than 0.0001 as shown in Table 3. The low model p value implies that the model was significant. The "Lack of Fit" F value of 0.14 implies that there was insignificant lack of fit. The "Lack of Fit" (Prob > F) value of 0.9951 implies that there was a 99.51% chance that the “Lack of Fit" F-value could occur due to noise. A high R2 value of 0.9016 as shown in Table 4 shows that the model was able to adequately represent the actual relationship between the variables studied. An R2 value of 0.9016 indicates that the model explained 90.16% of the variability in the response for the region studied. The coefficient of variation (CV) obtained was 7.04%. This parameter shows the degree of precision with which the runs were carried out. A low value of C.V suggests a high reliability and reproducibility of the design (Montgomery 2005). An Adequate precision value of 11.723 was obtained. According to Cao et al. (2009), this parameter measures the signal to noise ratio and a value greater than 4 is generally desirable. A ratio of 11.723 indicates an adequate signal meaning that the model can be used to navigate the design space. Table 3: Analysis of variance (ANOVA) for quadratic model for total sugar yield Sum of Mean Sources df F value Squares Squares Model 2723.78 13 209.52 10.57 X1 181.56 1 181.56 9.16 X2 661.28 1 661.28 33.37 X3 59.32 1 59.32 2.99 X4 47.05 1 47.05 2.37 X1X2 25.53 1 25.53 1.29 X1X3 11.42 1 11.42 0.58 X1X4 304.88 1 304.88 15.39 X2X4 17.20 1 17.20 0.87 X3X4 9.04 1 9.04 0.46 X 12 4.14 1 4.14 0.21 X 22 11.08 1 11.08 0.56 X 32 662.72 1 662.72 33.44 X 42 462.62 1 462.62 23.35 Residual 297.24 15 19.52 Lack of Fit 84.66 11 7.70 0.14 Pure Error 212.58 4 53.15 Cor Total 3021.02 28

p-value