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Wan Razali, W. A., Samsu Baharuddin, A., Zaini, L. A., Mokhtar, M. N., Taip, F. S., and Zakaria, R. (2014). "Effect of seed sludge quality using oil palm empty fruit bunch (OPEFB) bio-char for composting," BioRes. 9(2), 2739-2756.

Abstract

In this study, a comparison between oil palm empty fruit bunch (OPEFB) composting using palm oil mill effluent bio-char solution (POMEBS) aerobic sludge and palm oil mill effluent (POME) anaerobic sludge was reported. A set of experiments was designed by central composite design (CCD) using response surface methodology (RSM) to statistically evaluate the POMEBS aerobic sludge as microbial seeding. The bacteria count of POMEBS aerobic sludge (3.7×106 CFU/mL) at the optimum point was higher than that of POME anaerobic sludge (2.5×105 CFU/mL). Denaturing gradient gel electrophoresis (DGGE) and Fourier transform infrared spectroscopy (FTIR) were also performed. A rotary drum composter was then used to compost OPEFB with POMEBS aerobic sludge and POME anaerobic sludge, separately. Thermogravimetric analysis (TGA) showed that composting OPEFB with POMEBS aerobic sludge had a higher degradation rate compared to composting OPEFB with POME anaerobic sludge. In addition, the final N:P:K values for composting OPEFB with POMEBS aerobic and POME anaerobic sludge were 3.7:0.8:6.2 and 1.5:0.3:3.4, respectively. POMEBS aerobic sludge improved the composting process and compost quality.


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Effect of Seed Sludge Quality using Oil Palm Empty Fruit Bunch (OPEFB) Bio-Char for Composting

Wan Aizuddin Wan Razali,Azhari Samsu Baharuddin,a,b,* Liyana Anissa Zaini,Mohd Noriznan Mokhtar,Farah Saleena Taip,and Rabitah Zakaria a

In this study, a comparison between oil palm empty fruit bunch (OPEFB) composting using palm oil mill effluent bio-char solution (POMEBS) aerobic sludge and palm oil mill effluent (POME) anaerobic sludge was reported. A set of experiments was designed by central composite design (CCD) using response surface methodology (RSM) to statistically evaluate the POMEBS aerobic sludge as microbial seeding. The bacteria count of POMEBS aerobic sludge (3.7×10CFU/mL) at the optimum point was higher than that of POME anaerobic sludge (2.5×10CFU/mL). Denaturing gradient gel electrophoresis (DGGE) and Fourier transform infrared spectroscopy (FTIR) were also performed. A rotary drum composter was then used to compost OPEFB with POMEBS aerobic sludge and POME anaerobic sludge, separately. Thermogravimetric analysis (TGA) showed that composting OPEFB with POMEBS aerobic sludge had a higher degradation rate compared to composting OPEFB with POME anaerobic sludge. In addition, the final N:P:K values for composting OPEFB with POMEBS aerobic and POME anaerobic sludge were 3.7:0.8:6.2 and 1.5:0.3:3.4, respectively. POMEBS aerobic sludge improved the composting process and compost quality.

Keywords: Palm oil mill effluent (POME) anaerobic sludge; Palm oil mill effluent bio-char solution (POMEBS) aerobic sludge; Response surface methodology (RSM); Denaturing gradient gel electrophoresis (DGGE); Rotary drum composting; Fourier transform infrared (FTIR); Thermogravimetric analysis (TGA)

Contact information: a: Department of Process and Food Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia, Malaysia; b: Laboratory of Biopolymer and Derivatives, Institute of Tropical Forestry and Forest Products, Putra Infoport, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; *Corresponding author: azharis@upm.edu.my

INTRODUCTION

Oil palm is currently the most productive oil crop in the world, more productive even than soybean and corn. The oil can either be obtained from its mesocarp or its kernel. This fact also means that oil palm produces the greatest amount of biomass. In Malaysia, it is estimated that the industry generates at least 30 million tonnes of biomass per year, including the fronds, trunks, empty fruit bunches, and leaves (Hashim et al. 2012). In addition to this solid biomass, the industry also produces a large amount of liquid waste known as palm oil mill effluent (POME). For every tonne of oil palm fresh fruit bunch (OPFFB) processed, about 0.7 tonne of POME will be generated, which comprises 26.3 kg of biological oxygen demand (BOD), 53 kg of chemical oxygen demand (COD), 19 kg of suspended solids (SS), and 6 kg of oil and grease (Lorestani et al. 2006). One of the most widely used techniques to utilize these solid and liquid wastes is the production of compost. This involves the use of microbes to degrade the wastes, which can later be used to improve the quality of degraded soils.

In Malaysia, composting OPEFB with POME anaerobic sludge is a popular method of waste treatment that has been reported in a previous study using an in-vessel composter and windrow system (Wan Razali et al. 2012). The compost was completed in 40 days with a N:P:K ratio of 2.8:0.4:2.8. Composting is a process whereby degradable organic matter is converted into stable matter containing a humic-like substance (Ishii et al. 2000). The existing composting technology requires a long decomposition time because microbes need to adapt to temperature changes from mesophilic to thermophilic. Thus, in order to improve the existing composting time, POME anaerobic sludge needs to be modified so that its characteristics are suitable for the aerobic composting process. In order to induce microbial proliferation during the composting process, higher surface area material such as bio-char can be added. Bio-char’s natural characteristics allow it to act as a host for microbes to colonize (Fischer and Glaser 2012). In addition, urea can be added to improve the final N content. Hence, palm oil mill effluent bio-char solution (POMEBS) was formulated using response surface methodology (RSM) to improve the composting process and the final compost quality.

In order to identify the microbes present in POMEBS, denaturing gradient gel electrophoresis (DGGE) was used. DGGE is a useful tool for revealing microbial succession because it can separate DNA fragments amplified by polymerase chain reaction (PCR) according to the differences in base-pair sequences and visualize the bacterial community as a band finger print (Baharuddin et al. 2009).

Compost quality is generally defined on the basis of two criteria, which are stability and maturity. Compost stability refers to the resistance of organic compost matter to further degrade, whereas compost maturity is related to suitability for plant growth and humification (Som et al. 2009). Thermogravimetric analysis (TGA) can be used to characterize compost stability (Melis and Castaldi 2004). Thermogravimetry (TG) is a technique in which weight changes are measured during incremental heating of a sample, while the first derivative of TG trace (DTG) shows the steps by which the reactions take place. DTG does not contain any new information; however, the temperatures at which mass loss is at a maximum and superimposed transformation are clearly shown as DTG peaks (Dell’ Abate et al. 1998). The suitability for plant growth is usually referred to as N:P:K values, where nitrogen is important to promote the growth of leaves; phosphorus is important for photosynthesis, energy transfer within plants, and fruit growth; and potassium is important for the manufacture and movement of sugars, cell division, and root development (Yadav and Garg 2011; Tairo and Ndakidemi 2013).

Therefore, the main objective of this study is to evaluate POMEBS aerobic sludge as microbial seeding for the OPEFB composting process in terms of degradation performance and final compost quality.

EXPERIMENTAL

Raw Material Preparation

Pressed, shredded OPEFB was obtained from Seri Ulu Langat Palm Oil Mill (Dengkil, Selangor, Malaysia). The OPEFB was dried in an industrial oven at 60 °C before it was ground with a ring knife flaker (Pallmann, Germany) to an average size of 0.5 to 2.0 mm. A small particle size is important to improve the accessibility of carbon sources for microorganisms (Bernal et al. 2009). POME anaerobic sludge was obtained from FELDA Serting Hilir Palm Oil Mill (Serting, Negeri Sembilan, Malaysia). The POME anaerobic sludge was stored at 4 °C prior to use. OPEFB bio-char (2.0 to 5.0 mm) was obtained from Nasmech Technologies Sdn Bhd and was produced via slow pyrolysis (300 to 400 °C) in a batch process at atmospheric pressure (Salleh et al. 2010). It was ground into particles (0.25 mm) using a universal cutting mill Pulverisette 19 (Fritsch, Germany). Urea beads N46 (Malaysia) were used as nitrogen sources.

Experimental Procedure

POMEBS aerobic sludge was prepared by mixing POME anaerobic sludge with ground OPEFB bio-char and urea beads. The mixture was mixed in conical flasks according to the composition generated by Design Expert software. The mixture was aerated with oxygen at a constant flow rate (1.05 mL/s) through a tube. To prevent nitrogen losses to the atmosphere by vaporization, the flask was covered with aluminum foil before it was incubated in a water bath for 24 h. Then, total solids (TS), total suspended solids (TSS), and volatile suspended solids (VSS) were determined according to the standard method (APHA 2005). TS, TSS, and VSS were chosen as dependent variables because they can be correlated with the number of bacteria in the sludge (Otero et al. 2002). Composting experiments using OPEFB with POMEBS aerobic sludge and POME anaerobic sludge were run using a rotary drum composter (Jora JK400, SWEDEN). Ten kg of OPEFB were mixed with 30 L of POMEBS aerobic sludge and 30 L of POME anaerobic sludge, separately. The drum was manually rotated every day for 10 days and continued once every two days until the 40-day composting process was complete.

Sampling and Analysis

The presence of viable bacteria in POMEBS aerobic sludge and POME anaerobic sludge was determined by the plate counting method (Brock et al. 2012). Temperature and oxygen concentration in the composter were measured using a portable temperature and oxygen detector manufactured by Demista Instrument USA (Model CM 2006, USA). Compost samples were collected every five days throughout the composting process and stored at -20 °C prior to analysis.

Moisture content was determined using an AND MX90 Moisture Analyzer (MX90, JAPAN), and the pH value was measured using a pH meter (model DELTA 320, Mettler Toledo, USA). Carbon and nitrogen were determined using an elemental analyzer (Thermo Finnigan, Italy). Nutrients and heavy metal elements were analyzed using Inductively Coupled Plasma (ICP)-OES, (Perkin Elmer, USA).

Central Composite Design (CCD)

To determine the optimum mixture conditions, a series of experiments were carried out with POME anaerobic sludge, OPEFB bio-char, and urea beads as independent process variables. The design was carried out using a 2factorial with six axial points (α = 0.5) and six replicate center points, according to the CCD.

To optimize the effective process parameters, the CCD method chosen as the experimental design was appropriate for fitting a quadratic surface with a minimum number of experiments and helped analyze the interaction between the parameters (Arami-Niya et al. 2012).

Table 1. Experimental Design Matrix and Response Results

Table 1 shows the designed level and range of the variables investigated in this study. The quadratic equation model for predicting the optimal point is expressed by Equation 1.

where Yis the response (dependent variable); is constant coefficient; x2x3, and x4 are linear coefficients; x5x6, and x7 are interactive coefficients; x8x9, and x10 are quadratic coefficients; and A, B, and C are code-independent variables. Design Expert software (Version 7.0, Stat-Ease Inc., Minneapolis, MN, USA) was used for regression and graphical analysis of the data obtained. An analysis of variance (ANOVA) was used to estimate the statistical parameters. RSM was chosen as the method to calculate the optimum value. The variability in dependent variables was explained by the multiple coefficients of determination (R2), while the model equation was used to predict the optimum values (Amouzgar et al. 2010).

Scanning Electron Microscopy

The morphological structure of the materials (bio-char, POME anaerobic sludge, and POMEBS aerobic sludge) was analyzed by scanning electron microscopy (SEM) (S-3400N, Hitachi, Japan). SEM images of all the samples were taken at 1000× and 10000× magnifications.

Fourier Transform Infrared Analysis

Fourier transform infrared spectroscopy (FTIR) was used to evaluate the changes between chemical bonds in functional groups of POME anaerobic sludge and POMEBS aerobic sludge. This was carried out using a Perkin Elmer GX2000R infrared spectrophotometer by subjecting the sample to wave numbers within the range of 500 to 4000 cm-1 at a resolution of 4 cm-1.

DGGE Analysis of Partial 16S rDNA Genes

Microbe DNA was extracted from approximately 2.0 mL of POMEBS aerobic sludge in optimal conditions. The extraction was replicated twice. The sludge sample was poured into 10 mL extraction buffer (100 mM Tris-HCl pH 8.0, 100 mM sodium EDTA pH 8.0 and 1.5 M NaCl). About 0.5 g of 2-mm glass beads were consumed and then vigorous vortex mixing was applied for 2 min to disrupt the microbe’s cell wall.

The DNA samples were diluted with sterilized ultra-pure water to minimize the inhibition effects of co-extracted contaminants. The 16S rDNA was amplified by using a primer set, consisting of forward primer (341f) with a 40 bp GC clamp (First Base Laboratory, Malaysia), 5’-CGC CCG CCG CGC GCG GGC GGG GCG GGG GCA CGG GGG GCC TAC GGG AGG CAG CAG-3’ and reverse primer (518r), 5’-ATT ACC GCG GCT GCT GG-3’. PCR amplifications were carried out in 25 µL of PCR mixture and diluted to 25 mL with sterilized ultra-pure water. The PCR cycling for 16S rDNA using 341f and 518r primers was performed with a PCR Thermal Cycler (MasterEP Gradient, Eppendorf, Germany) (Ahmad et al. 2011).

The DGGE was performed according to Muyzer and Smalla (1998). The 16S rDNA PCR products were separated in 1.0 mM of 6% (w/w) polyacrylamide with a denaturing gradient of 30 to 70% (100% denaturing gradient correspondence to 7 M urea and 40% (v/v) deionized formamide). The gel was allowed to polymerize for at least 2 h. Five microliters of PCR product was loaded into each individual well. The DGGE was performed in 1× TAE buffer at 60 °C under a constant volume of 200 V for 5 h. After electrophoresis, the DGGE gel was stained using SYBR nucleic acid gel stain for 30 min and then rinsed with distilled water and photographed on a UV transillumination table (Labnet, USA). The DNA bands from the DGGE gel were excised with Pasteur pipettes and placed in 1.5-mL Eppendorf tubes. The band DNA was eluted in 50 µL of TE buffer, and the tubes were incubated overnight at -20 °C to extrude the DNA. Then, the DNA was frozen and thawed three times. Approximately 5 µL of the supernatant was used as the template to re-amplify the DNA. The re-amplified PCR product was further purified using QIAprep spin columns (Qiagen Inc., Valencia, CA).

The PCR products were sent for sequencing. The Gen-Bank database (www.ncbi.nlm.nih.gov) with BLAST (basic local alignment search tool) was used as a reference to identify the nearest relatives of partially sequenced 16S rDNA genes and the excised dominant bands.

Thermogravimetric Analysis (TGA)

Compost samples from day 1 and day 40 (dried at 60 °C for 24 h, then ground (0.25 mm particle size) using a universal cutting mill Pulverisette 19 (Fritsch, Germany)) were thermogravimetrically analyzed. TGA was carried out with a Mettler TG20 Thermobalance, TA3000 system. The following conditions were used for all TGA analyses: heating rate 10 °C/min from 25 to 500 °C, under a constant nitrogen flow of 10 mL/min, and sample weight of about 10 mg. Measurements were repeated twice.

RESULTS AND DISCUSSIONS

Regression Analysis

Optimization of the POMEBS aerobic sludge was achieved with CCD. Data were analyzed using the Design Expert software to yield analysis of variance (ANOVA), regression coefficients, and regression equations. The polynomial equations describing the TS, TSS, and VSS as simultaneous functions of bio-char (A), urea (B), and temperature (C) are presented as Eqs. 2, 3, and 4.

For TS, the amount of bio-char (A), urea (B), and two-level interactions of AB, BC, A2B, and AB2were the significant terms reduced from insignificant parameters. For TSS, it was found that the significant terms were the amount of bio-char (A), urea (B), temperature (C), and two-level interactions AB, AC, BC, A2, C2, ABC, A2B, A2C, and AB2. The quadratic model of VSS showed the less significant terms involved, which were the amount of bio-char (A) and two-level interactions A2, B2, and AB2. These regressions were statistically significant at 93.45%, 98.39%, and 84.49% for TS, TSS, and VSS, respectively. The impact of significant terms was defined by R2; an increase in the significance terms led to the models being more accurate. The model can give a predicted value that is near the actual value of the response when the regression coefficient value (R2) is close to 1 (Arami-Niya et al. 2012), while a high R2 value shows that the model obtained is able to give a good estimate of the response of the system within the range of study (Kang et al. 2012). Therefore, from the statistical results obtained, it can be verified that the models are accurate enough to predict the optimum conditions for producing POMEBS aerobic sludge.

Model Analysis

The three-dimensional (3-D) response surfaces using Eqs. 2, 3, and 4 are shown in Fig. 1. These 3-D graphs illustrate the relationship between factors and their effects in order to find the optimum point for each response. In order to show the interactive effects of independent variables on responses, two variables were well distributed in certain ranges while another one was kept constant. The 3-D response surfaces in Fig. 1 (a) and (b) show the effects of bio-char and urea on TS and VSS; Figs. 1 (c) and (d) show the effects of temperature and bio-char on TS and VSS; and Figs. 1 (e) and (f) show the effects of temperature and urea toward TS and VSS. In Figs. 1 (a) and (b), the optimum points for TS and VSS are at bio-char contents of 10.0 to 10.6 g/L and urea contents of 10.2 to 10.6 g/L. In Figs. 1 (c) and (d), the optimum points of TS and VSS are at temperature of 50.0 to 50.6 °C and bio-char contents at the range of 11.4 to 12.2 g/L for TS and 9.8 to 10.6 g/L for TSS. In Figs. 1 (e) and (f), the optimum points are at 50 °C and 10 g/L urea for both TS and VSS.

Table 2. Analysis of Variance (ANOVA) for Regression Equation Developed for TS, TSS, and VSS