The filamentous fungus Trichoderma harzianum was used for liquid state bioconversion of POME for cellulase production. Statistical optimization was carried out to evaluate the physico-chemical parameters (factors) for maximum cellulase production by 2-level fractional factorial design with six central points. The polynomial regression model was developed using the experimental data including the effects of linear, quadratic and interaction of the factors. The factors involved were substrate (POME) and co-substrate (wheat flour) concentrations, temperature, pH, inoculum and agitation. Statistical analysis showed that the optimum conditions were: temperature of 300C, substrate concentration of 2%, wheat flour concentration of 3%, pH of 4, inoculum of 3% and agitation of 200 rpm. Under these conditions, the model predicted the enzyme production to be about 14 FPU/ml. Analysis of variance (ANOVA) of the design showed a high coefficient of determination (R2) value of 0.99, thus ensuring a high satisfactory adjustment of the quadratic model with the experimental data

Abd. Karim, M. Ismail

Alam, Md. Zahangir

Hamisan, Ahmad Fadhlan

Jalal, K.C.A.

Jamal, Parveen

English

Universiti Teknologi Malaysia Institutional Repository

Proceedings of the 1st International Conference on Natural Resources Engineering & Technology 2006 24-25th July 2006; Putrajaya, Malaysia, 226-232    226Liquid State Bioconversion of Palm Oil Mill Effluent for Cellulase Production: Statistical Optimization of Process Conditions   Md. Zahangir Alam1,∗, M. Ismail Abd Karim1, Ahmad Fadhlan Hamisan1, Parveen Jama1l, K. C. A. Jalal2  1Bioenvironmental Engineering Research Unit (BERU), Faculty of Engineering, 2Faculty of Science,  International Islamic University Malaysia (IIUM), Jalan Gombak, 50728 Kuala Lumpur, Malaysia.    Abstract  The filamentous fungus Trichoderma harzianum was used for liquid state bioconversion of POME for cellulase production. Statistical optimization was carried out to evaluate the physico-chemical parameters (factors) for maximum cellulase production by 2-level fractional factorial design with six central points. The polynomial regression model was developed using the experimental data including the effects of linear, quadratic and interaction of the factors. The factors involved were substrate (POME) and co-substrate (wheat flour) concentrations, temperature, pH, inoculum and agitation. Statistical analysis showed that the optimum conditions were: temperature of 300C, substrate concentration of 2%, wheat flour concentration of 3%, pH of 4, inoculum of 3% and agitation of 200 rpm. Under these conditions, the model predicted the enzyme production to be about 14 FPU/ml. Analysis of variance (ANOVA) of the design showed a high coefficient of determination (R2) value of 0.99, thus ensuring a high satisfactory adjustment of the quadratic model with the experimental data.   Key words: Palm oil mill effluent-cellulase enzyme-bioconversion-statistical optimization   1.0 Introduction  Currently, Malaysia produces 11.9 million tonnes of crude palm oil per year from which about 8.9 million tonnes of palm oil is exported, accounting for 52% of the total world production [1]. The process to extract oil from the fresh fruit bunch (FBB) requires large amount of water, mainly for sterilizing the fruits and for oil clarification, resulting in the discharge of organic, non-toxic wastewater known as palm oil mill effluent (POME). The quantity of POME produced is about 60% for every tonne of FBB processed. Thus, an average of 30 tonnes FBB/hr mill in this country is generated about 18-19.5 tonnes effluent (POME)/hr [2].   Several techniques have been developed in order to treat the highly biodegradable POME. Ponding, anaerobic and aeration systems are the most adopted treatment processes practiced by more than 85% of the palm oil mills in the country [3]. The drawbacks of these systems are the requirement of a large land area and the system suffers from control and maintenance problems, biogas generation caused in air pollution etc [4]. The production of this effluent always contributes an environmental problem such as the generation of methane during its                                                  ∗ Corresponding author: Tel: +603-61964571, Fax: +603-61964442, Email:  zahangir@iiu.edu.my Proceedings of the 1st International Conference on Natural Resources Engineering & Technology 2006 24-25th July 2006; Putrajaya, Malaysia, 226-232    227anaerobic treatment and the production of high COD [5]. It is estimated that POME contain 95-96% water, 0.6-0.7% oil, 4-5% total solids, 2-4% suspended solids, pH 4.7, BOD-25000mg/L, COD-50000mg/L and total nitrogen-750mg/L [2]. The carbohydrate and other nutrients content in POME effluent enable it to serve as substrate for the production of cellulolytic enzymes by liquid state bioconversion besides its treatment.   Cellulolytic enzyme production has attracted a world-wide attention due to the possibility of using this enzyme complex for conversion of abundantly available renewable lignocellulosic biomass for production of carbohydrates for numerous industrial applications including bioethanol [6-8]. Economical production of cellulases is key for feasible bioethanol production from lignocellulosic biomass using cellulase-based processes. To date the production of cellulase has been widely studied in submerged culture processes (liquid state) but the relatively high cost of enzyme production has hindered the industrial application of cellulose bioconversion through various expensive media are used to produce such enzymes [9]. The aim of this study was to determine the process conditions utilizing palm oil mill effluent (POME) as substrate which is abundant in Malaysia (about 10 million tonnes per year) for the production of cellulase enzyme by liquid state bioconversion.   2.0 Materials and Methods  2.1 Fermentation Media   The major substrate/media used in this study was palm oil mill effluent (POME). The POME of 5 - 6% w/w of TSS (pH 4.8) was collected from oil palm industry named Seri Ulu Langat Palm Oil Mill Sdn. Bhd., Dengkil, Selangor, Malaysia. The POME concentration of 0.5 – 2% w/w of TSS have been prepared by removing excess water. This was done by leaving the sample for a few days to let the heavy particle to settle down at the bottom of the pail. Then a certain amount of excess water was removed according to the material balance to maintain the actual TSS in the sample (POME). The final pH of POME was recorded. The POME was supplemented with co-substrate of 1–3% w/w of wheat flour as available nutrients for microbe throughout the study [10].   2.2 Microbial Strain and Its Inoculum Preparation   Culture of Trichoderma harzianum was obtained from lab stock, Bioenvironmental Engineering Lab, IIUM. The fungus was maintained on potato dextrose agar (PDA) plates and subcultured once in month. PDA was prepared by dissolving 9.75 g/L of PDA powder (OXOID Ltd. England) in 240mL of distilled water, sterilize for 20 minutes in 121°C autoclave before poured it into the plates to prepare for the solid PDA. Then the fungus was cultured onto the agar, incubated at 32°C until the entire plate was covered by fungus. After seven days, 100mL sterilized water was poured onto the surface of four agar plates containing the spore culture. The spore on the surface was gently scraped with sterilized glass rod. The spore (2.8 – 3.2 x 105) suspension of Trichoderma harzianum was then filtered into a 250mL Erlenmeyer flask and collected for further experiments.     Proceedings of the 1st International Conference on Natural Resources Engineering & Technology 2006 24-25th July 2006; Putrajaya, Malaysia, 226-232    2282.3 Experimental Procedure for Cellulase Production  1-3% (w/v) of wheat flour was added into flask containing 0.5%-2.0% (w/v) of POME. The pH of the mixture was adjusted to 4-6 by adding HCl to increase acidity or NaOH to increase alkalinity. The mixture was steam sterilized at 120°C for 30 minutes. 1% - 3% of inoculums with (2.8 – 3.2 x 105) spores was added into the flask and shaken in the shaker with agitation speed of 100 -250 rpm. The pH and temperature was set at 4.0 – 6.0 and 30 - 35°C respectively during fermentation process. Sampling was done after four days of fermentation, enzyme activities was analyzed and measured.   2.4 Experimental Design and Optimization  Fractional factorial design with six center points was performed in order to determine the optimal process conditions for the production of cellulase enzyme by Trichoderma harzianum. The experiments were conducted according to the designated two-level factorial design. In this design, total number of experiments and also level for each factor was determined. The experiments studied six factors for optimization of cellulase production by liquid state bioconversion. The experiment was design using MinitabTM statistical software. The levels and factors are shown in Table 1.   Table 1 Levels of experimental factors for optimization Factor Low(-1) Center(0) High(+1) Temperature 30°C 32.5°C 35°C Substrate concentration 0.5% 1.25% 2.0% Co-substrate concentration 1% 2% 3% Agitation 100 rpm 175 rpm 250 rpm Initial pH 4 5 6 Inoculums size (v/v) 1% 2% 3%  2.5 Statistical and Analytical Analysis  Statistical software, MinitabTM was used to analyze regression model of experimental data. The value of F-test, p-test, t-test and R2 were identified to evaluate the model as well as to determine the optimum conditions. Each factor has two levels, high and low including central points, and each experiment is run in three replications. The fractional factorial design with central points including actual values is shown in Table 2. The enzyme activities, TSS, COD as well as the pH were analyzed. The TSS as biosolids and chemical oxygen demand (COD) were determined according to the standard methods [11]. Cellulase activity assay was carried out by the method suggested by Mandel et al. [12].   3.0 Results and Discussion  The statistical optimization approach using fractional factorial design was used to study the linear quadratic and interactive effects of various parameters on higher cellulase production by Trichoderma harzianum. The observed (experimental) and predicted results for cellulase enzyme production were obtained with different conditions is shown in Table 2. The highest cellulase activity (13.44 FPU/ml) was observe at run number 23, where the factors temperature, substrate and co-substrate concentration, inoculums size, pH and agitation were Proceedings of the 1st International Conference on Natural Resources Engineering & Technology 2006 24-25th July 2006; Putrajaya, Malaysia, 226-232    229used at their level of low 30°C, high 2%, high 3%, high 3%, pH 4 and high 250 rpm respectively. The experimental result was then analyzed by regression analysis, which gave the following regression Equation (1) of the levels of cellulase  produced (FPU/ml) as a function of temperature (x1), substrate concentration (x2), co-substrate concentration (x3), inoculums size (x4), pH (x5) and agitation (x6).  Table 4.1. Result using two level fractional factorial design and six center points showing observed and predicted response (cellulase) Run Temp, 0C Subs conc (%) Co-s conc (%) pH Inoculum Size (%) Agitation rpm Cellulase,FPU/ml Cellulase,Predicted1 30 0.5 1 4 1 100 3.277 2.673 2 35 0.5 1 4 1 250 2.643 1.941 3 30 2 1 4 1 250 2.643 2.194 4 35 2 1 4 1 100 7.400 6.848 5 30 0.5 3 4 1 250 2.696 2.118 6 35 0.5 3 4 1 100 2.273 1.540 7 30 2 3 4 1 100 0.000 -0.490 8 35 2 3 4 1 250 3.859 3.243 9 30 0.5 1 6 1 250 0.000 -0.578 10 35 0.5 1 6 1 100 3.753 2.984 11 30 2 1 6 1 100 0.000 -0.486 12 35 2 1 6 1 250 1.797 1.225 13 30 0.5 3 6 1 100 10.360 9.678 14 35 0.5 3 6 1 250 5.550 4.749 15 30 2 3 6 1 250 12.421 11.900 16 35 2 3 6 1 100 0.000 -0.676 17 30 0.5 1 4 3 250 1.850 1.349 18 35 0.5 1 4 3 100 5.286 4.661 19 30 2 1 4 3 100 0.000 -0.362 20 35 2 1 4 3 250 4.863 4.431 21 30 0.5 3 4 3 100 6.343 5.861 22 35 0.5 3 4 3 250 2.326 1.750 23 30 2 3 4 3 250 13.443 13.048 24 35 2 3 4 3 100 0.439 -0.034 25 30 0.5 1 6 3 100 7.559 7.063 26 35 0.5 1 6 3 250 2.114 1.568 27 30 2 1 6 3 250 3.330 2.994 28 35 2 1 6 3 100 1.216 0.774 29 30 0.5 3 6 3 250 2.167 1.673 30 35 0.5 3 6 3 100 5.920 5.263 31 30 2 3 6 3 100 0.000 -0.374 32 35 2 3 6 3 250 6.449 5.993 33 32.5 1.5 2 5 2 175 1.586 0.836 34 32.5 1.5 2 5 2 175 1.691 0.836 35 32.5 1.5 2 5 2 175 1.427 0.836 36 32.5 1.5 2 5 2 175 1.269 0.836 37 32.5 1.5 2 5 2 175 1.004 0.836 38 32.5 1.5 2 5 2 175 1.163 0.836 Proceedings of the 1st International Conference on Natural Resources Engineering & Technology 2006 24-25th July 2006; Putrajaya, Malaysia, 226-232    230FPU/ml = 155 - 17.0*x1 - 89.4*x2 + 50.4*x3 + 59.2*x4 + 77.2*x5 + 0.0841*x6 + 0.382 *x12 + 2.66*x1*x2 - 1.69*x1*x3 - 1.79*x1*x4 - 2.30*x1*x5 - 0.00450*x1*x6 + 9.82*x2*x3 + 2.31*x2*x4 + 0.830*x2*x5 + 0.326*x2*x6 - 7.91*x3*x4 - 6.61*x3*x5 + 0.0204*x3*x6 - 12.9*x4*x5 - 0.0979*x4*x6 + 0.00218*x5*x6 - 0.297*x1*x2*x3 - 0.0942*x1*x2*x4 - 0.0273*x1*x2*x5 - 0.00892*x1*x2*x6 + 0.262*x1*x3*x4 + 0.199*x1*x3*x5 - 0.000208*x1*x3*x6 + 0.383*x1*x4*x5 + 0.00296*x1*x4*x6           (1)  The ANOVA from the analysis are shown in Table 3. The results showed that the coefficient of determination (R-sq) was 99.9% which ensured satisfactory adjustment of the quadratic model to the experimental data. The value of adjusted determination coefficient is also very high indicating a high significance of the model [13,14].  The F value is a measure of variation of the data about the mean. The high F-value and very low probability (p>F=0.0000) indicates that the present model is in good prediction of the experimental result [15]. The F-value, and p- and t-value are shown in Table 3 and 4 respectively.  Table 3 ANOVA for polynomial model derived from experimental data  Source DF SS MS F P Regression 31 419.1 13.519 237.40 0.000 Residual error 6 0.324 0.057   Total 37 419.442     R2 = 0.999, R-adj=99.5%, SS, sum of squares; DF, degree of freedom; MS. Mean  square  The t-value and probability value (p-value) serves as a tool for checking the significant of each of the coefficient. The pattern of interactions between the variables is indicated by these coefficients.  The larger the magnitude of t-test value and smaller the p-value indicates the high significance of the corresponding coefficient [16]. The variable with low probability levels contribute to the model, whereas the other can be neglected and eliminated from the model. The p-value for the linear, quadratic and polynomial terms is shown in Table 4. The p-value suggest that the coefficient for linear effect of temperature, x1, substrate concentration, x2, co-substrate concentration, x3,  inoculums size, x4, and pH, x5, are most significant with value of 0.000. The value of probability for coefficient of linear effect of agitation, x6, was quite high (0.092). This means that approximately 90.8% of the model affected by this variable. In contrast the p-value of the coefficient of interactive effect of substrate concentration and agitation (x2x6) had value of 0.0000, which is highly significant. Thus, from this result, it was clear that through the linear effect of agitation was not highly significant for cellulase production from Trichoderma harzianum, its addition to the design could not be totally overruled because of its interactive effect with wheat flour.          Proceedings of the 1st International Conference on Natural Resources Engineering & Technology 2006 24-25th July 2006; Putrajaya, Malaysia, 226-232    231 Table 4 t-value and p-value for the polynomial models Predictor Coef SE Coef t p Constant 15535 21.96 7.07 0.000 Temp, x1 -17.004 1.179 -14.43 0.000 Subs conc, x2 -89.410 4.606 -19.41 0.000 Co-s conc, x3 50.352 3.401 14.81 0.000 pH, x4 59.167 2.283 25.92 0.000 Inoculums, x5 77.239 3.151 24.52 0.000 rpm, x5 0.08407 0.04200 2.00 0.092 x1*x1 0.38238 0.01713 22.32 0.000 x1*x2 2.6573 0.1413 18.81 0.000 x1*x3 -1.6896 0.1043 -16.20 0.000 x1*x4 -1.78785 0.07003 -25.53 0.000 x1*x5 -2.30089 0.09661 -23.82 0.000 x1*x6 -0.004497 0.001288 -3.49 0.013 x2*x3 9.8247 0.7334 13.40 0.000 x2*x4 2.3059 0.7334 3.14 0.020 x2*x5 0.8301 0.7334 1.13 0.301 x2*x6 0.326193 0.009778 33.36 0.000 x3*x4 -7.9104 0.5500 -14.38 0.000 x3*x5 -6.6121 0.5500 -12.02 0.000 x3*x6 0.020441 0.007334 2.79 0.032 x4*x5 -12.8988 0.550 -23.45 0.000 x4*x6 0.097878 0.007334 -13.35 0.000 x5*x6 0.0021810 0.0005625 3.88 0.008 x1*x2*x3 -0.29690 0.02250 -13.20 0.000 x1*x2*x4 -0.09424 0.02250 -4.19 0.006 x1*x2*x5 -0.02733 0.02250 -1.21 0.270 x1*x2*x6 -0.0089226 0.0003 -29.74 0.000 x1*x3*x4 0.29232 0.01687 15.55 0.000 x1*x3*x5 0.19886 0.01687 11.78 0.000 x1*x3*x6 -0.0002081 0.000225 -0.93 0.391 x1*x4*x5 0.38257 0.01687 22.67 0.000 x1*x4*x6 0.0029567 0.0002250 13.14 0.000   4.0 Conclusion  It can be concluded that the optimum conditions for maximum cellulase production (14 FPU/ml) using palm oil mills effluent by liquid state bioconversion was the substrate concentration, 2.0% (w/v); temperature, 30°C; pH, 4; co-substrate concentration, 3.0% (w/v); inoculums size, 3.0% (w/v) and agitation, 250 rpm. The optimization study of process conditions using two-level fractional factorial design would enhance the production of enzymes at multi-stage level using POME as substrate. The lab-scale study on cellulase production from POME as major substrate might give the basic information of further development for large scale production.  Proceedings of the 1st International Conference on Natural Resources Engineering & Technology 2006 24-25th July 2006; Putrajaya, Malaysia, 226-232    232References  [1] Malaysian Palm Oil Board (MPOB). 2003. Latest Development and Commercialization of Oil Palm Biomass. In MPOB-FMM Seminar on Business Opportunity in Oil Palm Biomass, 21 August, Selangor, Malaysia. [2] Ahmad, A. L., S., Ismail and S., Bhatia 2003. Water Recycling from Palm Oil Mill Effluent (POME) using Membrane Technology. Desalination 157: 87-95. [3] Ma. A.N. and A.S.H., Ong 1985. Pollution Control in Palm Oil Mills in Malaysia. J. Amer. Oil Chem. Soc. 62: 261-266. [5] Ma. A. N. 2001. 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Standard Methods for The Examination of Water and Wastewater, 17th edition, American Public Health Association, Washington, DC, 1989 [12] Mandels, M., R., Andreotti, and C. Roche, 1976. Biotechnol. Bioeng. Symp, 6, 17. [13] Akhnazarova, S., and V., Kefarov 1982. Experiment Optimization in Chemistry and Chemical Engineering. Moscow. Mir Publisher. [14] Khuri, A., and J. A., Cornell 1987. Response Surface: Design and Analysis. New York: Marcell Decker Inc. [15] Dey, G., A., Mitra, R., Banerjee, and B.R., Maiti, 2001. Enhanced Production of Amylase by Optimization of Nutritional Constituent using Response Surface Methodology. Biochemical Engineering Journal 7: 227-231.  [16] Karthikeyan, R. S, S. K, Rakshit, A., Baradarajan 1996. Optimization of Batch Fermentation Conditions for Dextran Production. Bioprocess Eng 15: 247-251 

Liquid state bioconversion of palm oil mill effluent for cellulase production: statistical optimization of process conditions

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Liquid state bioconversion of palm oil mill effluent for cellulase production: statistical optimization of process conditions

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