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Effects of bulking agents on food waste composting
Bioresource Technology 101 (2010) 5917–5924
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Effects of bulking agents on food waste composting James I. Chang *, Y.J. Chen Department of Safety, Health and Environmental Engineering, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, ROC
a r t i c l e
i n f o
Article history: Received 21 December 2009 Received in revised form 7 February 2010 Accepted 8 February 2010 Available online 10 April 2010 Keywords: Mixture design of bulking agents Regression analysis Water absorption capacity of composting mixture Free air space of composting mixture
a b s t r a c t The effects of rice husk, sawdust and rice bran on the composting process of food waste were studied in a 180-L laboratory composter based on a mixture experimental design. Linear and quadratic models of seven important process characteristics (composting and acidification times, lowest and final pH values, highest temperature, the water-soluble organic carbon to water-soluble organic nitrogen (COW/NOW ratio), and the water-soluble organic carbon to total organic nitrogen (COW/NOT) ratio) in terms of fractional compositions of bulking agents as well as the water absorption capacity and the free air space of the composting matrix were developed. Ó 2010 Published by Elsevier Ltd.
1. Introduction Aerobic composting is the decomposition of organic substrates in the presence of oxygen (Haug, 1993). The composting process occurs more efficiently, when the carbon-to-nitrogen (C/N) ratio is between 30 and 40, and the moisture content between 50% and 65% (Agnew and Leonard, 2003). Recently, several studies (Huang et al., 2004; Zhu, 2007; Kumar et al., 2010) found that composting can also be effective at C/N ratios lower than 20. Numerous bulking agents including wood chips, wheat straw, sawdust, rice husk, rice bran, chopped hay, wood shavings, and peanut shells have been mixed with waste materials to adjust the moisture content, N-content, C/N ratio, and void spaces between particles in the past (Haug, 1993; Gea et al., 2007; Kim et al., 2008; Adhikari et al., 2009; Iqbal et al., 2010), since most waste materials such as sewage sludge, food waste and animal manure have too high moisture contents and too low C/N ratios for efficient composting. It is well known that different bulking agents not only modify physical properties of the composting feedstock, but also change the biodegradation kinetics and composting performance (Haug, 1993; Barrington et al., 2002; Das et al., 2003; Kulcu and Yaldiz, 2007; Adhikari et al., 2008; Kim et al., 2008; Yanez et al., 2009); however, quantitative relationships between the physical properties of the composting material and the performance of composting process have not been tried and developed yet.
The objective of this work was to develop quantitative relationships between physical properties of the composting material and the performance of composting process. To achieve the goal, the effects of bulking agents on the composting process of food waste were first studied. Multi-variate regression analysis was applied to relate physical properties of the composting mixture to the experimental results. Human and animal foods were used as raw materials for preparing uniform synthetic food waste for all experiments. Sawdust, rice husk and rice bran used extensively in Asia waste composting studies (Amano and Atoni, 2002; Tajima and Yoshimura, 2003; Nakasaki and Nagasaki, 2004; Huang, 2005; Kamolmanit and Reunghang, 2006; Chang et al., 2006; Kato and Miura, 2008) were used as the bulking agents, because they were not only readily available, but also could be mixed together with synthetic food waste to produce composting materials of a wide range of C/N ratios from 21 to 40, water absorption capacities from 150 to 296, and free air spaces from 53 to 91. The statistical experimental design was based on the principles of mixture experimental design (Cornell, 1990). Linear and quadratic equations of the process characteristics (composting time, lowest and final pH values, highest temperature etc.) and corresponding response curves in terms of weight fractions of bulking agents and physical properties and were also provided. 2. Methods 2.1. Composting material and bulking agents
* Corresponding author. Tel.: +886 7 601 1000; fax: +886 7 601 1061. E-mail address: [email protected] (J.I. Chang). 0960-8524/$ - see front matter Ó 2010 Published by Elsevier Ltd. doi:10.1016/j.biortech.2010.02.042
Human and animal foods such as steamed rice, wheat meal, fishmeal, meat and soy meals, and lard were mixed to make the
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synthetic food waste with a desired composition of carbohydrate, protein and fat that reflects the foods consumed by the Taiwanese people. The uniformity of a synthetic food waste made this way offered a distinct advantage over restaurant or cafeteria waste in controlling the variability of substrate characteristics (Schwab et al., 1994). Rice and lard were bought from the local grocery store. Wheat meal, fishmeal, meat and soy meals were provided by the Dachan Great Wall Enterprise Company, Tainan, Taiwan. Rice husk, sawdust and rice bran were mixed with synthetic food waste to adjust the moisture content and the C/N ratio, and to maintain air spaces. Physical and chemical properties of raw materials used to make synthetic food waste and bulking agents were presented in Table 1. 2.2. Experimental apparatus A specially designed laboratory-scale reactor (180-L in volume) was used in this study. The reactor system consisted of an insulated cylindrical vessel made of stainless steel, an induced fan, and a condenser. A double helix ribbon mixer driven by a high-torque, low-speed electric motor operated intermittently was installed in the reactor for mixing. 2.3. Physico-chemical monitoring and analyses 2.3.1. Gas analyses CO2 and O2 concentrations in the reactor outlet gas were continuously measured by a CO2 analyzer (ABB EasyLiner IR) and an O2 analyzer (ABB magneto-mechanical analyzer) placed after the induced fan. Temperatures at different heights in the reactor were continuously monitored by thermocouples connected to temperature recorders. An automated sampling and data acquisition system ran on IBM PC was also used to acquire and store the data of gas compositions and temperature data. The air rate sucked into the reactor by the induced fan was set at 1.6 L gas kg1 DM min1. 2.3.2. Solid analyses Solid samples were taken every 4 h in first 2 days and every 8 h afterwards. Water was added to maintain the moisture content in the reactor at 55% whenever solid samples were taken. Important physico-chemical parameters of the samples such as the pH value, moisture, ash, carbon, nitrogen, total organic carbon (TOC), and total Kjeldahl nitrogen were analyzed according to the standard methods (APHA, AWWA, WPCF, 1998). Three replicate samples were analyzed.
The wet samples taken from the reactor were extracted with deionized water (1:5 (v/v) sample to water ratio). After the solution had been allowed to equilibrate for 30 min with occasional stirring, a pH meter was used to measure the pH value. Carbon, hydrogen and nitrogen in the solid samples were measured using an element analyzer (EA1110, ThermoQuest, Italia SPA). Oxygen concentration was calculated by difference. Aqueous compost extracts were prepared by placing 20 g of compost sample (dry weight) in a 250 mL beaker filled with 200 mL of distilled water, and shaking (125 rpm) for 2 h at room temperature. The suspension was then centrifuged and the supernatant filtered through a 0.45 lm membrane filter (Chefetz et al., 1998). The concentration of dissolved organic carbon was measured using a total organic carbon analyzer (ShimadzuTOC-5000A). Total Kjeldahl nitrogen was determined by digesting the samples with sulfuric acid and 50% hydrogen peroxide at 500 °C for 15 min and measuring the resulting NH3–N-content at a pH of 13, using a NH3 sensitive electrode (APHA, WPCF, AWWA, 1998). Bulk density defined as the weight per unit volume of material was measured according to ASTM D1895–96 (ASTM, 2003). The solid sample was first filled a known volume using a graduated cylinder and the mass of that particular volume was measured using an analytical balance. The free air space (FAS) was determined from the bulk density (wet/dry) and particle density. The wet particle density was determined by placing the 5 g of material in a graduated cylinder and submerged with kerosene. The particle density was calculated after verifying the density of kerosene (0.79 kg/m3) and determining the mass of kerosene added (Barrington et al., 2002). This wet particle density was used to compute the FAS (Iqbal et al., 2010).
FASð%Þ ¼ 100 ð1 BD=PDÞ
ð1Þ
where FAS = free air space (%), BD = bulk density (kg/m3), and PD = particle density (kg/m3). The water absorption capacity (WAC) value of a solid sample was determined using a method described in Adhikari et al. (2008). The sample was soaked in distilled water for 24 h and the wet sample was then dried at 105 °C for 24 h after the gravitational water was drained off under cover. The total water absorbed was the difference between the weight of the soaked sample and the weight of the dried sample.
WACð%Þ ¼ 100ðW AB W DRY Þ=W DRY
ð2Þ
where, WAB = weight of the soaked sample (g) and WDRY = weight of dried sample (g).
Table 1 Compositions of synthetic food waste and bulking agents.
a b c
Composition
Steamed rice meal
Wheat meal
Fish powder
Meat powder
Soy meal
Lard
Soy oil
Synthetic food wasteb
Rice husk
Sawdust
Rice bran
Kitchen wastec
Moisture (%) Ash (%) Carbohydrate (%)a Protein (%)a Fat (%)a Carbon (%)a Hydrogen (%)a Nitrogen (%)a Oxygen (%)a C/N ratio Bulk density (kg/m3) Free air space (%) Water absorption capacity (%)
57–60 0.6–1.0 87–89 8–9 0.6–0.8 41–43 3–5 1.6–2.0 52–55 21–26 – – –
7–8 1.6–2.0 88–91 11–12 1.4–1.6 47–50 7–9 1.8–2.2 37–46 24–29 – – –
6–7 26–27 4–5 78–82 15–17 56–58 7–8 13–14 21–23 4–5 – – –
4–5 33–34 4–5 79–83 14–16 41–46 5–7 7–11 38–46 4–6 – – –
8–9 7–8 49–51 48–50 1.8–2.2 42–52 5–7 7–9 42–48 5–7 – – –
0.5–1.0 0 0 0 100 35–37 5–7 0 54–60 – – – –
0.3–0.7 0 0 0 100 40–44 4–6 0 52–58 – – – –
80.5 1.0 62.6 16.0 21.3 44.5 5.1 3.3 47.0 13.3 – – –
9–11 18–21 – – – 37–40 5–6 0.6–0.7 47–50 60–70 110–120 90–100 330–340
10–12 2–3 – – – 43–46 4–6 0.2–0.4 48–51 140–160 190–210 85–95 430–450
10–14 8–11 – – – 48–52 7–9 1.6–2.0 38–42 25–30 710–730 50–60 145–155
65–80 3–5 52–65 12–20 15–35 50–52 6–7 3–4 36–38 13–18 – – –
Dry and ash free base. 16.2% Steamed rice, 6.22% wheat meat, 1.10% fish powder, 1.2% meat powder, 1.2% soy meal, 0.95% lard, 2.61% soy oil, and 70% water. Kitchen waste in Taiwan (Chang et al., 2006).
J.I. Chang, Y.J. Chen / Bioresource Technology 101 (2010) 5917–5924
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Fig. 1. Response surface of the composting time in terms of fractional compositions of bulking agents.
The final product at the end of each experiment was tested for maturity using a modified phytotoxicity test employing seed germination as suggested by Zucconi et al. (1985) and Wu and Ma (2001). The germination method was briefly described as follows: two pieces of No. 2 Whatman filter paper were placed inside a 15 mm 100 mm sterilized, disposable Petri dish. The filter paper was wetted with 9 mL of a compost–water extract solution (1:10) and 30 tomato (Lycopersicon esculentum L.) seeds were then placed on the paper. Deionized distilled water was used as a control. The Petri dishes were sealed with parafilm to minimize water loss while allowing air penetration and were kept at room temperature in the dark. At the end of 4 days, the percentage of seed germination in the compost extract was compared with that of the water control.
2.4. Experimental design The statistical experimental design was based on the principles of the mixture experimental design (Cornell, 1990). In the beginning, only the first 10 (A–J) were selected based on the simplexcentroid design (A–F and J) introduced by Scheff’e (1963) and the augmented design with interior points (G, H, and I) suggested by Khuri and Cornell (1987). The last two (K and L) were added after the composting experiment of the mixture C failed. A triangular system of coordinates as shown in Fig. 1 was used to represent the locations of points A–L. The corners of the triangle represented the pure components, A (100% rice husk), B (100% sawdust) and C (100% rice bran). The percentage of A was plotted along the sides AB and AC. Components B and C were plotted similarly (Atkins and de Paula, 2005). Their corresponding compositions were shown Table 2. The solid C/N ratios ranged from 21.5 to 39.2, WAC from 150 to 296, and FAS from 53 to 91. About 14 experiments were performed in this study. The last two experiments (mixtures J0 and J00 ) were duplicated experiments of mixture J, which was at the center of the ternary system. The temperature in the room that housed the reactor was kept between 25 and 30 °C.
2.5. Linear and quadratic models To predict the dependence of observed responses on the bulking agents, a mixture experimental design was developed (Cornell, 1990). The observed responses (Yi’s) could be approximated by a set of quadratic or linear functions listed as follows:
Y i ¼ a0 þ a1 x1 þ a2 x2
ð3Þ
or
Y i ¼ a0 þ a1 x1 þ a2 x2 þ a11 x21 þ a22 x22 þ a12 x1 x2
ð4Þ
where Yi’s were predicted responses that characterized the composting process were selected as dependent variables. x1, x2 were weight fractions of sawdust and rice bran in the mixture of bulking agents, respectively; a0 was the offset term; a1, a2, were linear coefficients, and a11, a22 were squared terms; and a12 the interaction coefficients. In fact, a0 was the predicted response of the control case, when rice husk was the only one bulking agent in the mixture (i.e. x1, x2 = 0). Seven experimental results, namely composting time, acidification time, final pH value, lowest pH value, highest temperature, the ratio of water-soluble organic carbon to water-soluble organic nitrogen (COW/NOW), and the ratio of the water-soluble organic carbon to total organic nitrogen (COW/NOT) in the solid) were chosen as the predicted responses characterizing the composting process. The reasons why those seven measurable process characteristics were chosen are explained below. The composting time are defined to be the time from the start to the time that the temperature stayed ambient and the pH value remained unchanged for 16 h. The acidification time was defined to be the time from the start to the time when the pH value reached its minimum. The composting time, the acidification time and the lowest pH value are closely related to the rate of composting. The composting process can be considered as consecutive reactions of a first-order hydrolysis of polymeric substrate into soluble organic matter and a first-order oxidation of monomeric substrate into carbon dioxide and water, and an aerobic decay reaction of
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Table 2 Feedstock compositions of experiments. Composition
A
B
C
D (A + C)/2
E (A + B)/2
F (B + C)/2
G (A + J)/2
H (B + J)/2
I (C + J)/2
J (A + B + C)/ 3
J0 (A + B + C)/ 3
Synthetic food waste (kg) Rice husk (kg) Sawdust (kg) Rice bran (kg) Total weight (kg) Bulk density (kg/m3) Moisture (%)
16.24
16.24
16.24
16.24
16.24
16.24
16.24
16.24
16.24
16.24
16.24
9.00 0.00 0.00 25.24 130 (5) 55.39 (1.03) 7.62 (0.26) 40.43 (0.41) 1.51 (0.03) 26.7 19.3 91 (5) 229 (10)
0.00 9.00 0.00 25.24 261 (4) 55.74 (0.89) 1.53 (0.04) 44.87 (0.51) 1.14 (0.01) 39.2 48.2 72 (3) 296 (12)
0.00 0.00 9.00 25.24 585 (15) 55.60 (0.89) 4.09 (0.09) 48.39
4.50 0.00 4.50 25.24 234 (5) 55.49 (1.27) 5.86 (0.12) 44.41
4.50 4.50 0.00 25.24 218 (6) 55.57 (0.94) 4.58 (0.12) 42.65
0.00 4.50 4.50 25.24 359 (18) 55.68 (1.69) 2.81 (0.09) 46.63
6.00 1.50 1.50 25.24 218 (9) 55.49 (1.56) 6.02 (0.12) 42.49
1.50 6.00 1.50 25.24 349 (24) 55.66 (1.37) 2.97 (0.03) 44.71
1.50 1.50 6.00 25.24 408 (25) 55.59 (1.23) 4.25 (0.17) 46.47
3.00 3.00 3.00 25.24 263 (15) 55.58 (1.74) 4.38 (0.08)
3.00 3.00 3.00 25.24 259 (16) 55.58 (1.21) 4.41 (0.12)
44.56
44.56
2.25 (0.02) 21.5 55.3 53 (2) 150 (9)
1.88 (0.02) 23.6 45.7 83 (3) 195 (6)
1.33 (0.02) 32.1 16.5 84 (4) 269 (10)
1.70 (0.01) 27.4 47.3 70 (3) 199 (8)
1.57 (0.03) 27.0 20.8 90 (6) 274 (15)
1.39 (0.01) 32.2 24.2 77 (4) 292 (13)
1.94 (0.02) 23.9 34.0 63 (3) 188 (11)
1.64 (0.02)
1.64 (0.02)
27.2 38.4 85 (4) 272 (14)
27.2 39.2 86 (4) 274 (14)
0.000 0.000 1.000
1.000 0.000 0.000
0.000 1.000 0.000
0.000 0.500 0.500
0.500 0.000 0.500
0.500 0.500 0.000
0.166 0.167 0.666
0.666 0.167 0.167
0.167 0.666 0.167
0.333 0.333 0.333
0.333 0.333 0.333
Ash (%) Carbon (%)a Nitrogen (%)a C/N ratio (solid) COW/NOWb Free air space (%) Water absorption capacity (%) x1 in coded unit x2 in coded unit x3 in coded unit a b
Dry and ash free base. COW and NOW are carbon and nitrogen contents of water-soluble organic fraction.
Table 3 Experimental results. Item
A
B
C
D
E
F
G
H
I
J
J0
Total weight (kg)
19.23 (0.12) 51.21 (1.02) 162 (6) 10.01 (0.41) 40.62 (0.55) 1.13 (0.04) 36.0 0.70 (0.04) 0.13 (0.004) 5.2 0.6 289 41
18.75 (0.15) 51.63 (1.21) 302 (7) 2.05 (0.62) 44.98 (0.41) 0.87 (0.03) 51.5 2.66 (0.11) 0.11 (0.002) 24.2 3.1 150 31
18.63 (0.16) 51.95 (1.13) – 5.55 (0.34) 49.33 (0.55) 1.59 (0.07) 31.0 –
19.62 (0.09) 53.33 (1.58) 262 (6) 7.54 (0.41) 44.98 (0.49) 1.38 (0.05) 32.7 6.98 (0.28) 0.28 (0.006) 24.8 5.1 345 101
20.11 (0.18) 54.15 (1.38) 247 (8) 5.74 (0.28) 42.80 (0.46) 1.01 (0.04) 42.6 0.81 (0.03) 0.14 (0.002) 5.8 0.8 233 42
18.92 (0.08) 53.39 (1.89) 421 (21) 3.75 (0.21) 47.13 (0.53) 1.26 (0.05) 37.3 4.94 (0.11) 0.24 (0.004) 20.6 3.9 312 31
21.23 (0.09) 56.38 (1.62) 278 (9) 7.16 (0.29) 42.80 (0.44) 1.17 (0.04) 36.4 2.37 (0.04) 0.13 (0.003) 18.3 2.0 264 41
19.00 (0.14) 52.11 (1.98) 394 (29) 3.95 (0.19) 44.97 (047) 1.05 (0.03) 42.8 2.20 (0.004) 0.13 (0.002) 17.1 2.1 192 24
17.83 (0.08) 49.32 (1.56) 442 (26) 6.02 (0.33) 47.14 (0.18) 1.42 (0.04) 33.3 5.76 (0.14) 0.22 (0.003) 26.2 4.1 376 62
19.93 (0.18) 54.21 (1.38) 302 (18) 5.59 (0.27) 44.97 (0.21) 1.22 (0.03) 36.9 4.87 (0.16) 0.21 (0.003) 22.4 4.0 232 41
19.18 (0.12) 52.38 (1.14) 291 (17) 5.81 (0.43) 44.97 (0.53) 1.22 (0.05) 36.9 5.16 (0.12) 0.22 (0.004) 23.1 4.2 230 40
8.91 (0.01) 4.51 (0.02) 60 (1)
8.6 (0.01)
–
6.09 (0.02) 50 (1)
4.18 (0.02) 35<
10.01 (0.01) 5.01 (0.01) 52 (1)
8.92 (0.02) 4.95 (0.01) 56 (1)
9.89 (0.01) 5.98 (0.02) 47 (1)
8.91 (0.01) 4.82 (0.01) 54 (1)
9.01 (0.02) 5.69 (0.01) 49 (1)
9.59 (0.02) 5.81 (0.02) 47 (1)
9.71 (0.01) 5.51 (0.02) 51 (1)
9.61 (0.01) 5.58 (0.02) 51 (1)
85
45.2
–
45.2
73.2
55.3
51.9
52.1
43.9
42.6
47.2
Moisture (%) Bulk density (kg/m3) Ash (%) Carbon (%) Nitrogen (%) C/N ratio (solid) Cow (%) Now (%) Cow/Now Cow/NOTa Composting time (h) Acidification times (h) Final pH value Lowest pH value Highest temperature (°C) Germination index (GI) (>60%) a
– – – – 160
NOT: total organic nitrogen content in solid (%).
biomass (Hamelers, 1993). In the beginning, the hydrolysis predominates and the pH value drops steadily. When the rate of the hydrolysis reaction is equal to the rate of the oxidation reaction, the pH reaches its minimum and the concentration of intermediate soluble organic matter reaches its maximum at
s ¼ lnðk1 =k2 Þ=ðk1 k2 Þ
ð5Þ
s is the time from the start to the minimum pH point defined as the acidification time, and k1 and k2 are rate constants of hydrolysis and the oxidation reactions, respectively (Atkins and de Paula, 2005). The oxidation reaction is the rate-determining step, since it is
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J.I. Chang, Y.J. Chen / Bioresource Technology 101 (2010) 5917–5924 Table 4 Empirical coefficients of regression models in terms of fractional compositions of bulking agentsa. Item
a0
a1
a2
a11
a22
a12
R2
F
Composting time (h)
294.6 254.6 42.2 46.9 8.9 8.9 4.4 4.3 60.3 59.6 5.9 7.9 0.7 1.00
162.7 135.1 14.6 39.9 0.1 0.2 1.2 1.6 11.0 9.5 5.1 10.5 1.5 1.4
194.0 231.0 70.2 58.1 2.3 1.6 1.8 1.9 22.4 17.3 64.7 27.0 12.3 5.6
27.7 – 5.5 – 0.7 – 0.6 – 0.4 – 21.7 – 3.6 –
575.4 – 44.5 – 1.6 – 0.4 – 4.6 – 43.4 – 8.4 –
27.1 – 219.6 – 1.8 – 0.9 – 8.7 – 22.3 – 1.5 –
0.965 0.827 0.930 0.638 0.830 0.659 0.849 0.803 0.938 0.926 0.887 0.732 0.876 0.786
38.88 23.94 18.45 8.80 6.84 9.66 7.89 20.34 21.08 63.00 11.04 13.69 9.90 18.30
Acidification time (h) Final pH value Lowest pH value Highest temperature (°C) COW/NOW COW/NOT a
Y = a0 + a1x1 + a2x2 + a11x12 + a22x22 + a12x1x2 or a0 + a1x1 + a2x2; x1, x2 are weight fractions of sawdust and rice bran, respectively.
slower than the hydrolysis reaction (k2 < k1). A higher value of k1/k2 leads to a longer acidification time and a lower value of the lowest pH, and vise versa.As the rate of aerobic oxidation is faster than that of hydrolysis, the pH value rises. As the pH rises above 7, ammonia will release as ammonia hydroxide. The pH continues to rise and eventually to a steady value over 8. In general, the more the nitrogen contents in the original mixture, the higher the final pH and water-soluble nitrogen values are. The COW/NOW values between 5 and 6, and COW/NOT values under 1.0 were suggested as the maturity indices by several researchers (Chanyasak et al., 1982; Hue and Liu, 1995; Bernal et al., 2009) because they were independent on the composting materials. These maturity indices were also compared with germination test and no phytoxic effects were found (Hue and Liu, 1995; Bernal et al., 2009). To predict the effects of the water absorption capacity and the free air space of the composting material on observed responses, a different set of quadratic or linear equations were developed:
Y 0i ¼ b0 þ b1 x3 þ b2 x4
ð6Þ
or
Y 0i ¼ b0 þ b1 x3 þ b2 x4 þ b11 x23 þ b22 x24 þ b12 x3 x4
ð7Þ
0
where Y is were predicted responses. The same seven parameters in Eqs. (3) and (4), were dependent variables; x3, x4 were water absorption capacity and free air space, respectively. b0 was the offset term; b1, b2, were linear coefficients; b11, b22 were squared terms; and b12 the interaction coefficients. 2.6. Statistical analysis The best values of Yi and Y0 i in. Eqs. (3), (4), (6), and (7), respectively, were evaluated using the ‘‘Solver” function in Microsoft Excel using Newtonian method. A maximum of 100 iterations was sufficient for the convergence of the errors of sum of the squares (SSE) between the experimental and estimated values to a minimum value. The initial values of parameters were estimated using a built-in visual procedure based on a limited fit algorithm (Wen et al., 1994). The statistical diagnosis of the above parameters was based on the approach reported by Wen et al. (1994). The response surface contour plots were constructed using the Windows software Statistica. A regression model is considered to be statistically significant if the calculated F value is larger than the value of Fl,m,a in the F table at a probability of a (Box et al., 1978), where l is the number of coefficients less 1, m is the degree of freedom, and a is a probability level. The degree of freedom (m) is defined to be the number of data less the number of coefficients. In Eqs. (3) and (6), there are 13 sets
of data and six coefficients (a0, a1, a2, a11, a22 and a12) to be evaluated by regression analysis. Therefore, l and m are equal to 5 and 7, respectively, and F5,7,0.05 is 3.97 at a = 0.05 (95% confidence level). In Eqs. (4) and (7), there are 13 sets of data and three coefficients (a0, a1, and a2) to be evaluated by regression analysis. Therefore, l and m are equal to 2 and 10, respectively, and F2,10,0.05 is 4.10 at a = 0.05. 3. Results and discussion 3.1. General description Physical properties and chemical compositions of final products of all 14 experiments are listed in Table 3. All mixtures except mixture C could be composted successfully. The experiment of mixture C was considered to be a failure. In the beginning of the experiment, the mixture C contained 9 kg of rice bran and 16.24 kg of synthetic food waste as shown in Table 2. The moisture content of the synthetic food waste was 80% (i.e. 13 kg). The 9 kg of rice bran could only absorb 5 kg of water due to its low water absorption capacity at 50–60%. The water between the particles was therefore higher than that in the particles. The high amount of water between particles made the mixture C more viscous and more compact than other mixtures, and prevented air to penetrate. The oxidation reaction never took off due to lack of enough air. As a result, the pH value dropped continuously to a value below 5, the CO2 concentration in the off gas was lower than 0.1%, and the highest temperature was below 35 °C after 3 weeks. The results were therefore not used in following regression analyses. As shown in Table 3, the seven process parameters of all mixtures studied were quite different. Both the acidification and composting times covered a wide range. The hydrolysis reaction rates for all mixtures could be assumed to be of the same magnitude, since each mixture had the same amount of food waste and the bulking agents were almost inert. The wide ranges of the composting and acidification times indicated that physical property changes caused by the bulking agents significantly affected the oxidation reaction rates of composting mixtures. The carbon contents in final products changed a little, but nitrogen contents reduced significantly resulting in higher C/N ratios. Same phenomenon was also found in our previous studies using the same composter. (Chang et al., 2006; Chang and Hsu, 2008). It was due to sufficient air supply and frequent agitation of the composter. The COW/NOW ratios of compost products ranged from 5 to 30, which were smaller than those in the initial mixtures. Among the 12 mixtures studied, only those using 100% rice husk (mixture A) and an equal-weighted mixture of rice husk and sawdust (mixture
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Table 5 Empirical coefficients of regression models in terms of water absorption capacity and free air spacea. Item
b0
b1
b2
b11
b22
b12
R2
F
Composting time (h)
3604.0 782.2 1430.0 171.8 7.6 10.4 0.6 8.6 29.7 22.7 155.2 56.6 1.7 9.1
3577.3 196.8 1353.4 50.5 7.6 0.9 3.7 0.5 129.5 0.4 288.1 1.6 53.0 0.1
1966.5 25.2 362.4 4.2 21.4 1.5 28.1 5.5 358.6 36.7 529.9 41.0 167.1 6.7
585.3 – 247.3 – 1.0 – 0.3 – 15.8 – 46.1 – 7.4 –
1751.8 – 252.9 – 22.6 – 25.9 – 342.5 – 445.6 – 134.0 –
701.8 – 143.3 – 3.9 – 3.0 – 67.4 – 80.0 – 19.6 –
0.978 0.880 0.893 0.598 0.883 0.407 0.877 0.786 0.905 0.661 0.586 0.349 0.635 0.205
36.91 36.79 11.72 7.43 10.57 3.43 10.03 18.31 13.36 9.74 1.99 2.68 2.44 1.28
Acidification time (h) Final pH value Lowest pH value Highest temperature (°C) COW/NOW COW/NOT a
Y0 = b0 + b1x3 + b2x4 + b11x3 + b22x4 + b12x3x4, or b0 + b1x3 + b2x4 capacity and free air space, respectively; x3, x4 are water absorption.
Fig. 2. Response surface of the highest temperature in terms of fractional compositions of bulking agents.
E) as bulking agents had a COW/NOW ratio within the maturity range of 5–6 as suggested by Chanyasak et al. (1982). Since the organics in the food waste stabilized at the end of the composting process, high COW/NOW and COW ratios indicated that sawdust and rice bran were in the process of dissociation. All final compost products except those from mixtures A and E failed the seed germination test as proposed by Zucconi et al. (1985). Only rice husk could be considered as a safe bulking agent that could be used alone. Sawdust could be mixed with rice husk, but the content should be controlled to less than 50%. Rice bran was not recommended as the bulking agent at all. 3.2. Derivation of empirical models By performing regression analyses of experimental data listed in Table 3, the coefficients of Eqs. (3), (4), (6), and (7) corresponding to each dependable variable were evaluated and listed in Tables 4 and 5, respectively. As shown in Table 4, all linear and the quadratic models in terms of weight frictions of bulking agents based on Eqs. (3) and
(4) were statistically significant, for the calculated F values were larger than F (2, 10, 0.05) value of 4.10 or F (5, 7, 0.05) value of 3.97 respectively. Most models of Eqs. (6) and (7) except the linear model for the lowest pH value and both models for COW/NOW and COW/NOT were also statistically significant as shown in Table 5. 3.3. Effects of bulking agents To examine the dependence of process characteristics on the compositions of bulking agents, it was convenient to use response surfaces on a ternary diagram. The quadratic models based on Eq. (4) were used, for the graphical illustration for the R2 and the calculated F values were in general larger than those of linear models. Figs. 1 and 2 showed the response surfaces of the predicted composting time and the highest temperature in terms of fractional compositions of bulking agents, respectively. Contour lines in Figs. 1 and 2 represent locations of equal values. The linear models also had a couple advantages due to their simplicity. First of all, linear models were easier to use; secondly, the linear coefficients of independent variables such as ai0 s or bi0 s
J.I. Chang, Y.J. Chen / Bioresource Technology 101 (2010) 5917–5924
of the linear models were actually first derivatives (ai = @Y/@xi), which could be interpreted as how dependent variables changed as independent variables varied. Therefore, by looking at the signs and the values of the linear coefficients, one would know the effects of the composition change of the bulking agent mixture on the process characteristics. As shown in Fig. 1, the composting time decreased rapidly as the sawdust fraction increased. When sawdust was the only agent used as in run B, only 150 h was needed. The addition of more sawdust in the mixture of bulking agents decreased the composting time, acidification time, final pH value, highest temperature, but increased the lowest pH value, and COW/NOW. Rice bran was the worst agent among the three. As the fraction of rice bran increased, the composting material became more compact and sticky due to its low water absorption capacity and free air space. All experimental results such as the composting time, pH values etc. except the highest temperature increased significantly. The linear coefficients of the water absorption capacity (b1’s) for most predicted responses except the lowest pH value in the linear models as shown in Table 5 were negative. Increasing the water absorption capacity of the composting mixture resulted in improving the composting rate, shorter composting and acidification time, lower highest temperature, and higher the lowest pH value. As the free air space of the composting matrix increased, composting rate accelerated and higher temperature reached due to more air flow through the composting matrix. This work was only the first step toward understanding the effects of bulking agents on the composting performance. Only sawdust, rice husk and rice bran were studied. Due to the limitation of the bulking agents selected, only the water absorption and the free air space were considered. The particle size, which significantly relates to the surface area of the solid, also affects mass transfers of water and air in the composting matrix and the composting performance (Eftoda and McCartney, 2004; Gea et al., 2007; Tremier et al., 2009). It will also be included in the future studies. 4. Conclusion This work demonstrates that physical properties of the composting feedstock significantly affect the composting process. The water absorption capacity of the composting mixture was the dominant physical property that affected the composting rate. More sawdust in the composting mixture resulted in the increases of the water absorption capacity and the composting rate, shorter composting and acidification times, and lower final pH value. As the free air space of the composting feedstock increased, composting rate accelerated and higher temperature reached due to more air flow through the particles. Among the 12 mixtures studied, only the products of a mixture prepared using 100% rice husk and another one using an equalweighted mixture of rice husk and sawdust as bulking agents passed the germination test. Therefore, rice husk was considered as a safe agent that could be used alone. Sawdust should be mixed with rice husk, but the content should be less than 50%. Rice bran was not recommended as a bulking agent. Acknowledgement The authors would like to express their gratitude to the Dachan Great Wall Enterprise Co. for providing wheat, soy, fish and meat meals. References Adhikari, B.K., Barrington, S., Martinez, J., King, S., 2008. Characterization of food wastes and bulking agents for composting. Waste Manage. 28 (5), 795–804.
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Adhikari, B.K., Barrington, S., Martinez, J., King, S., 2009. Effectiveness of three bulking agents for food waste composting. Waste Manage. 29 (1), 197–203. Agnew, J.M., Leonard, J.J., 2003. The physical properties of compost. Compost Sci. Util. 11 (3), 238–264. Amano, M., Atoni, T., 2002. Basic experiment in mixed composting of different organic wastes special reference to temperature with the addition of garbage. J. Agric. Sci. 46, 4265–4269. APHA, AWWA, WPCF, 1998. Standard Methods for the Examination of Water and Wastewater. American Public Health Association, American Water Works Association, and Water Pollution Control Federation, 20th ed. American Public Health Association, Washington, DC. ASTM, 2003. Standard Test Methods for Apparent Density, Bulk Factor, and Pourability of Plastic Materials. ASTM D1895–96. ASTM International, West Conshohocken. Atkins, P., de Paula, J., 2005. Elements of Physical Chemistry, fourth ed. Oxford University Press, Oxford. p. 262 (Chapter 11). Barrington, S., Choiniere, D., Trigui, M., Knight, W., 2002. Compost airflow resistance. Biosyst. Eng. 81 (4), 433–441. Bernal, M.P., Alburquerque, J.A., Moral, R., 2009. Composting of animal manures and chemical criteria fro compost maturity assessment. A review. Bioresour. Technol. 100 (22), 5444–5453. Box, G.E.P., Hunter, W.G., Hunter, J.S., 1978. Statistics for Experiments: An Introduction to Design, Data Analysis, and Model Building. John Wiley and Sons, New York. Chang, J.I., Hsu, T.E., 2008. Effects of composition on food waste composting. Bioresour. Technol. 99 (17), 8068–8074. Chang, J.I., Tsai, J.J., Wu, K.H., 2006. Thermophilic composting of food waste. Bioresour. Technol. 97 (1), 116–122. Chanyasak, V., Hirai, M., Kubota, H., 1982. Changes of chemical components and nitrogen transformation in water extracts during composting of garbage. J. Ferment. Technol. 60, 439–446. Chefetz, B., Hadar, Y., Chen, Y., 1998. Water extractable compounds released during composting of municipal solid waste. Acta Hortic. 469, 111–118. Cornell, J.A., 1990. Experiments with Mixtures. John Wiley and Sons, New York. Das, K.C., Tollner, E.W., Eiteman, M.A., 2003. Comparison of synthetic and natural bulking agents in food waste composting. Compost Sci. Util. 11 (10), 27–35. Eftoda, G., McCartney, D., 2004. Determination of the critical bulking agent requirement for municipal biosolids composting. Compost Sci. Util. 12 (3), 208–218. Gea, T., Barrena, R., Artola, A., Sanchez, A., 2007. Optimal bulking agent particle size and usage for heat retention and disinfection in domestic wastewater sludge composting. Waste Manage. 27 (9), 1108–1116. Hamelers, H.V.M., 1993. Theoretical model of composting kinetics. In: Hoitink, H.A.J., Keener, H.M. (Eds.), Science and Engineering of Composting. Renaissance Publications, Worthington, USA, pp. 36–58. Haug, R.T., 1993. The Principal Handbook of Compost Engineering. Lewis Publishers, Boca Raton, FL, USA. Huang, Y.M., 2005. Composting Methods. Food and Fertilizer Technology Center for the Asia and the Pacific Region, Taipei. Huang, G.F., Wong, J.W.C., Wu, Q.T., Nagar, B.B., 2004. Effect of C/N on composting of pig manure with sawdust. Waste Manage. 24 (8), 805–813. Hue, N.V., Liu, J., 1995. Predicting composting stability. Compost Sci. Util. 3 (2), 8– 15. Iqbal, M.K., Shafiq, T., Ahmed, K., 2010. Characterization of bulking agents and its effects on physical properties of compost. Bioresour. Technol. 101 (6), 1913– 1919. Kamolmanit, N., Reunghang, A., 2006. Effect of carbon to nitrogen ratio on the composting of Cassava pulp with swine manure. J. Water Environ. Technol. 4, 33–50. Kato, K., Miura, N., 2008. Effect of matured compost as a bulking and inoculating agent on the microbial community and maturity of cattle manure compost. Bioresour. Technol. 99 (9), 3372–3380. Khuri, A.I., Cornell, J.A., 1987. Response Surfaces Design and Analysis. Dekker, New York. Kim, K.Y., Kim, H.W., Han, S.K., Hwang, E.J., Lee, C.Y., Shin, H.S., 2008. Effect of granular porous media on the composting of swine manure. Waste Manage. 28 (11), 2336–2343. Kulcu, R., Yaldiz, O., 2007. Composting of goat manure and wheat straw using pine cones as a bulking agent. Bioresour. Technol. 98 (14), 2700–2704. Kumar, M., Ou, Y.L., Lin, J.G., 2010. Co-composting of green waste and food waste at low C/N ratio. Waste Manage. 30 (4), 602–609. Nakasaki, K., Nagasaki, K., 2004. Degradation of fats during thermophilic composting of organic waste. Waste Manage. Res. 22, 276–282. Scheff’e, H., 1963. The simplex-centroid design for experiments with mixtures. J. Roy. Stat. Soc. Ser. B (Methodol.) 25, 235–263. Schwab, B.S., Ritchie, C.J., Kain, D.J., Dobrin, G.D., King, L.W., Palmisano, A.C., 1994. Characterization of compost from a pilot plant-scale composting utilizing simulated solid waste. Waste Manage. Res. 123, 289–303. Tajima, K., Yoshimura, M., Kato, M., Tatsuno, J., Tamaki, K., 2003. Enhancement of composting process by the addition of chicken manure and rice bran to tree pruning chips. Jpn. J. Farm Work Res. 38 (4), 207–213. Tremier, A., Teglia, C., Barrington, S., 2009. Effect of initial physical characteristics on sludge compost performance. Bioresour. Technol. 100 (5), 3751–3758. Wen, T.C., Cheng, S.S., Lay, J.J., 1994. A kinetic model of a recirculated upflow anaerobic sludge blanket treating phenolic wastewater. Water Environ. Res. 66, 794–799.
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Wu, L., Ma, L.O., 2001. Effects of sample storage on biosolids compost stability and maturity evaluation. J. Environ. Qual. 30, 222–228. Yanez, R., Alonso, J.I., Diaz, M.J., 2009. Influence of bulking agent on sewage sludge composting process. Bioresour. Technol. 100 (23), 5827–5833.
Zhu, N., 2007. Effect of low initial ratio on aerobic composting of swine manure with rice straw. Bioresour. Technol. 98 (1), 9–13. Zucconi, F., Monaco, A., Forte, M., 1985. Phytotoxins during the stabilization of organic matter. In: Gasser, J.K.R. (Ed.), Composting of Agricultural and Other Wastes. Elsevier Applied Science Publications, England, pp. 73–85.
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Effects of bulking agents on food waste composting James I. Chang *, Y.J. Chen Department of Safety, Health and Environmental Engineering, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, ROC
a r t i c l e
i n f o
Article history: Received 21 December 2009 Received in revised form 7 February 2010 Accepted 8 February 2010 Available online 10 April 2010 Keywords: Mixture design of bulking agents Regression analysis Water absorption capacity of composting mixture Free air space of composting mixture
a b s t r a c t The effects of rice husk, sawdust and rice bran on the composting process of food waste were studied in a 180-L laboratory composter based on a mixture experimental design. Linear and quadratic models of seven important process characteristics (composting and acidification times, lowest and final pH values, highest temperature, the water-soluble organic carbon to water-soluble organic nitrogen (COW/NOW ratio), and the water-soluble organic carbon to total organic nitrogen (COW/NOT) ratio) in terms of fractional compositions of bulking agents as well as the water absorption capacity and the free air space of the composting matrix were developed. Ó 2010 Published by Elsevier Ltd.
1. Introduction Aerobic composting is the decomposition of organic substrates in the presence of oxygen (Haug, 1993). The composting process occurs more efficiently, when the carbon-to-nitrogen (C/N) ratio is between 30 and 40, and the moisture content between 50% and 65% (Agnew and Leonard, 2003). Recently, several studies (Huang et al., 2004; Zhu, 2007; Kumar et al., 2010) found that composting can also be effective at C/N ratios lower than 20. Numerous bulking agents including wood chips, wheat straw, sawdust, rice husk, rice bran, chopped hay, wood shavings, and peanut shells have been mixed with waste materials to adjust the moisture content, N-content, C/N ratio, and void spaces between particles in the past (Haug, 1993; Gea et al., 2007; Kim et al., 2008; Adhikari et al., 2009; Iqbal et al., 2010), since most waste materials such as sewage sludge, food waste and animal manure have too high moisture contents and too low C/N ratios for efficient composting. It is well known that different bulking agents not only modify physical properties of the composting feedstock, but also change the biodegradation kinetics and composting performance (Haug, 1993; Barrington et al., 2002; Das et al., 2003; Kulcu and Yaldiz, 2007; Adhikari et al., 2008; Kim et al., 2008; Yanez et al., 2009); however, quantitative relationships between the physical properties of the composting material and the performance of composting process have not been tried and developed yet.
The objective of this work was to develop quantitative relationships between physical properties of the composting material and the performance of composting process. To achieve the goal, the effects of bulking agents on the composting process of food waste were first studied. Multi-variate regression analysis was applied to relate physical properties of the composting mixture to the experimental results. Human and animal foods were used as raw materials for preparing uniform synthetic food waste for all experiments. Sawdust, rice husk and rice bran used extensively in Asia waste composting studies (Amano and Atoni, 2002; Tajima and Yoshimura, 2003; Nakasaki and Nagasaki, 2004; Huang, 2005; Kamolmanit and Reunghang, 2006; Chang et al., 2006; Kato and Miura, 2008) were used as the bulking agents, because they were not only readily available, but also could be mixed together with synthetic food waste to produce composting materials of a wide range of C/N ratios from 21 to 40, water absorption capacities from 150 to 296, and free air spaces from 53 to 91. The statistical experimental design was based on the principles of mixture experimental design (Cornell, 1990). Linear and quadratic equations of the process characteristics (composting time, lowest and final pH values, highest temperature etc.) and corresponding response curves in terms of weight fractions of bulking agents and physical properties and were also provided. 2. Methods 2.1. Composting material and bulking agents
* Corresponding author. Tel.: +886 7 601 1000; fax: +886 7 601 1061. E-mail address: [email protected] (J.I. Chang). 0960-8524/$ - see front matter Ó 2010 Published by Elsevier Ltd. doi:10.1016/j.biortech.2010.02.042
Human and animal foods such as steamed rice, wheat meal, fishmeal, meat and soy meals, and lard were mixed to make the
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synthetic food waste with a desired composition of carbohydrate, protein and fat that reflects the foods consumed by the Taiwanese people. The uniformity of a synthetic food waste made this way offered a distinct advantage over restaurant or cafeteria waste in controlling the variability of substrate characteristics (Schwab et al., 1994). Rice and lard were bought from the local grocery store. Wheat meal, fishmeal, meat and soy meals were provided by the Dachan Great Wall Enterprise Company, Tainan, Taiwan. Rice husk, sawdust and rice bran were mixed with synthetic food waste to adjust the moisture content and the C/N ratio, and to maintain air spaces. Physical and chemical properties of raw materials used to make synthetic food waste and bulking agents were presented in Table 1. 2.2. Experimental apparatus A specially designed laboratory-scale reactor (180-L in volume) was used in this study. The reactor system consisted of an insulated cylindrical vessel made of stainless steel, an induced fan, and a condenser. A double helix ribbon mixer driven by a high-torque, low-speed electric motor operated intermittently was installed in the reactor for mixing. 2.3. Physico-chemical monitoring and analyses 2.3.1. Gas analyses CO2 and O2 concentrations in the reactor outlet gas were continuously measured by a CO2 analyzer (ABB EasyLiner IR) and an O2 analyzer (ABB magneto-mechanical analyzer) placed after the induced fan. Temperatures at different heights in the reactor were continuously monitored by thermocouples connected to temperature recorders. An automated sampling and data acquisition system ran on IBM PC was also used to acquire and store the data of gas compositions and temperature data. The air rate sucked into the reactor by the induced fan was set at 1.6 L gas kg1 DM min1. 2.3.2. Solid analyses Solid samples were taken every 4 h in first 2 days and every 8 h afterwards. Water was added to maintain the moisture content in the reactor at 55% whenever solid samples were taken. Important physico-chemical parameters of the samples such as the pH value, moisture, ash, carbon, nitrogen, total organic carbon (TOC), and total Kjeldahl nitrogen were analyzed according to the standard methods (APHA, AWWA, WPCF, 1998). Three replicate samples were analyzed.
The wet samples taken from the reactor were extracted with deionized water (1:5 (v/v) sample to water ratio). After the solution had been allowed to equilibrate for 30 min with occasional stirring, a pH meter was used to measure the pH value. Carbon, hydrogen and nitrogen in the solid samples were measured using an element analyzer (EA1110, ThermoQuest, Italia SPA). Oxygen concentration was calculated by difference. Aqueous compost extracts were prepared by placing 20 g of compost sample (dry weight) in a 250 mL beaker filled with 200 mL of distilled water, and shaking (125 rpm) for 2 h at room temperature. The suspension was then centrifuged and the supernatant filtered through a 0.45 lm membrane filter (Chefetz et al., 1998). The concentration of dissolved organic carbon was measured using a total organic carbon analyzer (ShimadzuTOC-5000A). Total Kjeldahl nitrogen was determined by digesting the samples with sulfuric acid and 50% hydrogen peroxide at 500 °C for 15 min and measuring the resulting NH3–N-content at a pH of 13, using a NH3 sensitive electrode (APHA, WPCF, AWWA, 1998). Bulk density defined as the weight per unit volume of material was measured according to ASTM D1895–96 (ASTM, 2003). The solid sample was first filled a known volume using a graduated cylinder and the mass of that particular volume was measured using an analytical balance. The free air space (FAS) was determined from the bulk density (wet/dry) and particle density. The wet particle density was determined by placing the 5 g of material in a graduated cylinder and submerged with kerosene. The particle density was calculated after verifying the density of kerosene (0.79 kg/m3) and determining the mass of kerosene added (Barrington et al., 2002). This wet particle density was used to compute the FAS (Iqbal et al., 2010).
FASð%Þ ¼ 100 ð1 BD=PDÞ
ð1Þ
where FAS = free air space (%), BD = bulk density (kg/m3), and PD = particle density (kg/m3). The water absorption capacity (WAC) value of a solid sample was determined using a method described in Adhikari et al. (2008). The sample was soaked in distilled water for 24 h and the wet sample was then dried at 105 °C for 24 h after the gravitational water was drained off under cover. The total water absorbed was the difference between the weight of the soaked sample and the weight of the dried sample.
WACð%Þ ¼ 100ðW AB W DRY Þ=W DRY
ð2Þ
where, WAB = weight of the soaked sample (g) and WDRY = weight of dried sample (g).
Table 1 Compositions of synthetic food waste and bulking agents.
a b c
Composition
Steamed rice meal
Wheat meal
Fish powder
Meat powder
Soy meal
Lard
Soy oil
Synthetic food wasteb
Rice husk
Sawdust
Rice bran
Kitchen wastec
Moisture (%) Ash (%) Carbohydrate (%)a Protein (%)a Fat (%)a Carbon (%)a Hydrogen (%)a Nitrogen (%)a Oxygen (%)a C/N ratio Bulk density (kg/m3) Free air space (%) Water absorption capacity (%)
57–60 0.6–1.0 87–89 8–9 0.6–0.8 41–43 3–5 1.6–2.0 52–55 21–26 – – –
7–8 1.6–2.0 88–91 11–12 1.4–1.6 47–50 7–9 1.8–2.2 37–46 24–29 – – –
6–7 26–27 4–5 78–82 15–17 56–58 7–8 13–14 21–23 4–5 – – –
4–5 33–34 4–5 79–83 14–16 41–46 5–7 7–11 38–46 4–6 – – –
8–9 7–8 49–51 48–50 1.8–2.2 42–52 5–7 7–9 42–48 5–7 – – –
0.5–1.0 0 0 0 100 35–37 5–7 0 54–60 – – – –
0.3–0.7 0 0 0 100 40–44 4–6 0 52–58 – – – –
80.5 1.0 62.6 16.0 21.3 44.5 5.1 3.3 47.0 13.3 – – –
9–11 18–21 – – – 37–40 5–6 0.6–0.7 47–50 60–70 110–120 90–100 330–340
10–12 2–3 – – – 43–46 4–6 0.2–0.4 48–51 140–160 190–210 85–95 430–450
10–14 8–11 – – – 48–52 7–9 1.6–2.0 38–42 25–30 710–730 50–60 145–155
65–80 3–5 52–65 12–20 15–35 50–52 6–7 3–4 36–38 13–18 – – –
Dry and ash free base. 16.2% Steamed rice, 6.22% wheat meat, 1.10% fish powder, 1.2% meat powder, 1.2% soy meal, 0.95% lard, 2.61% soy oil, and 70% water. Kitchen waste in Taiwan (Chang et al., 2006).
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Fig. 1. Response surface of the composting time in terms of fractional compositions of bulking agents.
The final product at the end of each experiment was tested for maturity using a modified phytotoxicity test employing seed germination as suggested by Zucconi et al. (1985) and Wu and Ma (2001). The germination method was briefly described as follows: two pieces of No. 2 Whatman filter paper were placed inside a 15 mm 100 mm sterilized, disposable Petri dish. The filter paper was wetted with 9 mL of a compost–water extract solution (1:10) and 30 tomato (Lycopersicon esculentum L.) seeds were then placed on the paper. Deionized distilled water was used as a control. The Petri dishes were sealed with parafilm to minimize water loss while allowing air penetration and were kept at room temperature in the dark. At the end of 4 days, the percentage of seed germination in the compost extract was compared with that of the water control.
2.4. Experimental design The statistical experimental design was based on the principles of the mixture experimental design (Cornell, 1990). In the beginning, only the first 10 (A–J) were selected based on the simplexcentroid design (A–F and J) introduced by Scheff’e (1963) and the augmented design with interior points (G, H, and I) suggested by Khuri and Cornell (1987). The last two (K and L) were added after the composting experiment of the mixture C failed. A triangular system of coordinates as shown in Fig. 1 was used to represent the locations of points A–L. The corners of the triangle represented the pure components, A (100% rice husk), B (100% sawdust) and C (100% rice bran). The percentage of A was plotted along the sides AB and AC. Components B and C were plotted similarly (Atkins and de Paula, 2005). Their corresponding compositions were shown Table 2. The solid C/N ratios ranged from 21.5 to 39.2, WAC from 150 to 296, and FAS from 53 to 91. About 14 experiments were performed in this study. The last two experiments (mixtures J0 and J00 ) were duplicated experiments of mixture J, which was at the center of the ternary system. The temperature in the room that housed the reactor was kept between 25 and 30 °C.
2.5. Linear and quadratic models To predict the dependence of observed responses on the bulking agents, a mixture experimental design was developed (Cornell, 1990). The observed responses (Yi’s) could be approximated by a set of quadratic or linear functions listed as follows:
Y i ¼ a0 þ a1 x1 þ a2 x2
ð3Þ
or
Y i ¼ a0 þ a1 x1 þ a2 x2 þ a11 x21 þ a22 x22 þ a12 x1 x2
ð4Þ
where Yi’s were predicted responses that characterized the composting process were selected as dependent variables. x1, x2 were weight fractions of sawdust and rice bran in the mixture of bulking agents, respectively; a0 was the offset term; a1, a2, were linear coefficients, and a11, a22 were squared terms; and a12 the interaction coefficients. In fact, a0 was the predicted response of the control case, when rice husk was the only one bulking agent in the mixture (i.e. x1, x2 = 0). Seven experimental results, namely composting time, acidification time, final pH value, lowest pH value, highest temperature, the ratio of water-soluble organic carbon to water-soluble organic nitrogen (COW/NOW), and the ratio of the water-soluble organic carbon to total organic nitrogen (COW/NOT) in the solid) were chosen as the predicted responses characterizing the composting process. The reasons why those seven measurable process characteristics were chosen are explained below. The composting time are defined to be the time from the start to the time that the temperature stayed ambient and the pH value remained unchanged for 16 h. The acidification time was defined to be the time from the start to the time when the pH value reached its minimum. The composting time, the acidification time and the lowest pH value are closely related to the rate of composting. The composting process can be considered as consecutive reactions of a first-order hydrolysis of polymeric substrate into soluble organic matter and a first-order oxidation of monomeric substrate into carbon dioxide and water, and an aerobic decay reaction of
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Table 2 Feedstock compositions of experiments. Composition
A
B
C
D (A + C)/2
E (A + B)/2
F (B + C)/2
G (A + J)/2
H (B + J)/2
I (C + J)/2
J (A + B + C)/ 3
J0 (A + B + C)/ 3
Synthetic food waste (kg) Rice husk (kg) Sawdust (kg) Rice bran (kg) Total weight (kg) Bulk density (kg/m3) Moisture (%)
16.24
16.24
16.24
16.24
16.24
16.24
16.24
16.24
16.24
16.24
16.24
9.00 0.00 0.00 25.24 130 (5) 55.39 (1.03) 7.62 (0.26) 40.43 (0.41) 1.51 (0.03) 26.7 19.3 91 (5) 229 (10)
0.00 9.00 0.00 25.24 261 (4) 55.74 (0.89) 1.53 (0.04) 44.87 (0.51) 1.14 (0.01) 39.2 48.2 72 (3) 296 (12)
0.00 0.00 9.00 25.24 585 (15) 55.60 (0.89) 4.09 (0.09) 48.39
4.50 0.00 4.50 25.24 234 (5) 55.49 (1.27) 5.86 (0.12) 44.41
4.50 4.50 0.00 25.24 218 (6) 55.57 (0.94) 4.58 (0.12) 42.65
0.00 4.50 4.50 25.24 359 (18) 55.68 (1.69) 2.81 (0.09) 46.63
6.00 1.50 1.50 25.24 218 (9) 55.49 (1.56) 6.02 (0.12) 42.49
1.50 6.00 1.50 25.24 349 (24) 55.66 (1.37) 2.97 (0.03) 44.71
1.50 1.50 6.00 25.24 408 (25) 55.59 (1.23) 4.25 (0.17) 46.47
3.00 3.00 3.00 25.24 263 (15) 55.58 (1.74) 4.38 (0.08)
3.00 3.00 3.00 25.24 259 (16) 55.58 (1.21) 4.41 (0.12)
44.56
44.56
2.25 (0.02) 21.5 55.3 53 (2) 150 (9)
1.88 (0.02) 23.6 45.7 83 (3) 195 (6)
1.33 (0.02) 32.1 16.5 84 (4) 269 (10)
1.70 (0.01) 27.4 47.3 70 (3) 199 (8)
1.57 (0.03) 27.0 20.8 90 (6) 274 (15)
1.39 (0.01) 32.2 24.2 77 (4) 292 (13)
1.94 (0.02) 23.9 34.0 63 (3) 188 (11)
1.64 (0.02)
1.64 (0.02)
27.2 38.4 85 (4) 272 (14)
27.2 39.2 86 (4) 274 (14)
0.000 0.000 1.000
1.000 0.000 0.000
0.000 1.000 0.000
0.000 0.500 0.500
0.500 0.000 0.500
0.500 0.500 0.000
0.166 0.167 0.666
0.666 0.167 0.167
0.167 0.666 0.167
0.333 0.333 0.333
0.333 0.333 0.333
Ash (%) Carbon (%)a Nitrogen (%)a C/N ratio (solid) COW/NOWb Free air space (%) Water absorption capacity (%) x1 in coded unit x2 in coded unit x3 in coded unit a b
Dry and ash free base. COW and NOW are carbon and nitrogen contents of water-soluble organic fraction.
Table 3 Experimental results. Item
A
B
C
D
E
F
G
H
I
J
J0
Total weight (kg)
19.23 (0.12) 51.21 (1.02) 162 (6) 10.01 (0.41) 40.62 (0.55) 1.13 (0.04) 36.0 0.70 (0.04) 0.13 (0.004) 5.2 0.6 289 41
18.75 (0.15) 51.63 (1.21) 302 (7) 2.05 (0.62) 44.98 (0.41) 0.87 (0.03) 51.5 2.66 (0.11) 0.11 (0.002) 24.2 3.1 150 31
18.63 (0.16) 51.95 (1.13) – 5.55 (0.34) 49.33 (0.55) 1.59 (0.07) 31.0 –
19.62 (0.09) 53.33 (1.58) 262 (6) 7.54 (0.41) 44.98 (0.49) 1.38 (0.05) 32.7 6.98 (0.28) 0.28 (0.006) 24.8 5.1 345 101
20.11 (0.18) 54.15 (1.38) 247 (8) 5.74 (0.28) 42.80 (0.46) 1.01 (0.04) 42.6 0.81 (0.03) 0.14 (0.002) 5.8 0.8 233 42
18.92 (0.08) 53.39 (1.89) 421 (21) 3.75 (0.21) 47.13 (0.53) 1.26 (0.05) 37.3 4.94 (0.11) 0.24 (0.004) 20.6 3.9 312 31
21.23 (0.09) 56.38 (1.62) 278 (9) 7.16 (0.29) 42.80 (0.44) 1.17 (0.04) 36.4 2.37 (0.04) 0.13 (0.003) 18.3 2.0 264 41
19.00 (0.14) 52.11 (1.98) 394 (29) 3.95 (0.19) 44.97 (047) 1.05 (0.03) 42.8 2.20 (0.004) 0.13 (0.002) 17.1 2.1 192 24
17.83 (0.08) 49.32 (1.56) 442 (26) 6.02 (0.33) 47.14 (0.18) 1.42 (0.04) 33.3 5.76 (0.14) 0.22 (0.003) 26.2 4.1 376 62
19.93 (0.18) 54.21 (1.38) 302 (18) 5.59 (0.27) 44.97 (0.21) 1.22 (0.03) 36.9 4.87 (0.16) 0.21 (0.003) 22.4 4.0 232 41
19.18 (0.12) 52.38 (1.14) 291 (17) 5.81 (0.43) 44.97 (0.53) 1.22 (0.05) 36.9 5.16 (0.12) 0.22 (0.004) 23.1 4.2 230 40
8.91 (0.01) 4.51 (0.02) 60 (1)
8.6 (0.01)
–
6.09 (0.02) 50 (1)
4.18 (0.02) 35<
10.01 (0.01) 5.01 (0.01) 52 (1)
8.92 (0.02) 4.95 (0.01) 56 (1)
9.89 (0.01) 5.98 (0.02) 47 (1)
8.91 (0.01) 4.82 (0.01) 54 (1)
9.01 (0.02) 5.69 (0.01) 49 (1)
9.59 (0.02) 5.81 (0.02) 47 (1)
9.71 (0.01) 5.51 (0.02) 51 (1)
9.61 (0.01) 5.58 (0.02) 51 (1)
85
45.2
–
45.2
73.2
55.3
51.9
52.1
43.9
42.6
47.2
Moisture (%) Bulk density (kg/m3) Ash (%) Carbon (%) Nitrogen (%) C/N ratio (solid) Cow (%) Now (%) Cow/Now Cow/NOTa Composting time (h) Acidification times (h) Final pH value Lowest pH value Highest temperature (°C) Germination index (GI) (>60%) a
– – – – 160
NOT: total organic nitrogen content in solid (%).
biomass (Hamelers, 1993). In the beginning, the hydrolysis predominates and the pH value drops steadily. When the rate of the hydrolysis reaction is equal to the rate of the oxidation reaction, the pH reaches its minimum and the concentration of intermediate soluble organic matter reaches its maximum at
s ¼ lnðk1 =k2 Þ=ðk1 k2 Þ
ð5Þ
s is the time from the start to the minimum pH point defined as the acidification time, and k1 and k2 are rate constants of hydrolysis and the oxidation reactions, respectively (Atkins and de Paula, 2005). The oxidation reaction is the rate-determining step, since it is
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J.I. Chang, Y.J. Chen / Bioresource Technology 101 (2010) 5917–5924 Table 4 Empirical coefficients of regression models in terms of fractional compositions of bulking agentsa. Item
a0
a1
a2
a11
a22
a12
R2
F
Composting time (h)
294.6 254.6 42.2 46.9 8.9 8.9 4.4 4.3 60.3 59.6 5.9 7.9 0.7 1.00
162.7 135.1 14.6 39.9 0.1 0.2 1.2 1.6 11.0 9.5 5.1 10.5 1.5 1.4
194.0 231.0 70.2 58.1 2.3 1.6 1.8 1.9 22.4 17.3 64.7 27.0 12.3 5.6
27.7 – 5.5 – 0.7 – 0.6 – 0.4 – 21.7 – 3.6 –
575.4 – 44.5 – 1.6 – 0.4 – 4.6 – 43.4 – 8.4 –
27.1 – 219.6 – 1.8 – 0.9 – 8.7 – 22.3 – 1.5 –
0.965 0.827 0.930 0.638 0.830 0.659 0.849 0.803 0.938 0.926 0.887 0.732 0.876 0.786
38.88 23.94 18.45 8.80 6.84 9.66 7.89 20.34 21.08 63.00 11.04 13.69 9.90 18.30
Acidification time (h) Final pH value Lowest pH value Highest temperature (°C) COW/NOW COW/NOT a
Y = a0 + a1x1 + a2x2 + a11x12 + a22x22 + a12x1x2 or a0 + a1x1 + a2x2; x1, x2 are weight fractions of sawdust and rice bran, respectively.
slower than the hydrolysis reaction (k2 < k1). A higher value of k1/k2 leads to a longer acidification time and a lower value of the lowest pH, and vise versa.As the rate of aerobic oxidation is faster than that of hydrolysis, the pH value rises. As the pH rises above 7, ammonia will release as ammonia hydroxide. The pH continues to rise and eventually to a steady value over 8. In general, the more the nitrogen contents in the original mixture, the higher the final pH and water-soluble nitrogen values are. The COW/NOW values between 5 and 6, and COW/NOT values under 1.0 were suggested as the maturity indices by several researchers (Chanyasak et al., 1982; Hue and Liu, 1995; Bernal et al., 2009) because they were independent on the composting materials. These maturity indices were also compared with germination test and no phytoxic effects were found (Hue and Liu, 1995; Bernal et al., 2009). To predict the effects of the water absorption capacity and the free air space of the composting material on observed responses, a different set of quadratic or linear equations were developed:
Y 0i ¼ b0 þ b1 x3 þ b2 x4
ð6Þ
or
Y 0i ¼ b0 þ b1 x3 þ b2 x4 þ b11 x23 þ b22 x24 þ b12 x3 x4
ð7Þ
0
where Y is were predicted responses. The same seven parameters in Eqs. (3) and (4), were dependent variables; x3, x4 were water absorption capacity and free air space, respectively. b0 was the offset term; b1, b2, were linear coefficients; b11, b22 were squared terms; and b12 the interaction coefficients. 2.6. Statistical analysis The best values of Yi and Y0 i in. Eqs. (3), (4), (6), and (7), respectively, were evaluated using the ‘‘Solver” function in Microsoft Excel using Newtonian method. A maximum of 100 iterations was sufficient for the convergence of the errors of sum of the squares (SSE) between the experimental and estimated values to a minimum value. The initial values of parameters were estimated using a built-in visual procedure based on a limited fit algorithm (Wen et al., 1994). The statistical diagnosis of the above parameters was based on the approach reported by Wen et al. (1994). The response surface contour plots were constructed using the Windows software Statistica. A regression model is considered to be statistically significant if the calculated F value is larger than the value of Fl,m,a in the F table at a probability of a (Box et al., 1978), where l is the number of coefficients less 1, m is the degree of freedom, and a is a probability level. The degree of freedom (m) is defined to be the number of data less the number of coefficients. In Eqs. (3) and (6), there are 13 sets
of data and six coefficients (a0, a1, a2, a11, a22 and a12) to be evaluated by regression analysis. Therefore, l and m are equal to 5 and 7, respectively, and F5,7,0.05 is 3.97 at a = 0.05 (95% confidence level). In Eqs. (4) and (7), there are 13 sets of data and three coefficients (a0, a1, and a2) to be evaluated by regression analysis. Therefore, l and m are equal to 2 and 10, respectively, and F2,10,0.05 is 4.10 at a = 0.05. 3. Results and discussion 3.1. General description Physical properties and chemical compositions of final products of all 14 experiments are listed in Table 3. All mixtures except mixture C could be composted successfully. The experiment of mixture C was considered to be a failure. In the beginning of the experiment, the mixture C contained 9 kg of rice bran and 16.24 kg of synthetic food waste as shown in Table 2. The moisture content of the synthetic food waste was 80% (i.e. 13 kg). The 9 kg of rice bran could only absorb 5 kg of water due to its low water absorption capacity at 50–60%. The water between the particles was therefore higher than that in the particles. The high amount of water between particles made the mixture C more viscous and more compact than other mixtures, and prevented air to penetrate. The oxidation reaction never took off due to lack of enough air. As a result, the pH value dropped continuously to a value below 5, the CO2 concentration in the off gas was lower than 0.1%, and the highest temperature was below 35 °C after 3 weeks. The results were therefore not used in following regression analyses. As shown in Table 3, the seven process parameters of all mixtures studied were quite different. Both the acidification and composting times covered a wide range. The hydrolysis reaction rates for all mixtures could be assumed to be of the same magnitude, since each mixture had the same amount of food waste and the bulking agents were almost inert. The wide ranges of the composting and acidification times indicated that physical property changes caused by the bulking agents significantly affected the oxidation reaction rates of composting mixtures. The carbon contents in final products changed a little, but nitrogen contents reduced significantly resulting in higher C/N ratios. Same phenomenon was also found in our previous studies using the same composter. (Chang et al., 2006; Chang and Hsu, 2008). It was due to sufficient air supply and frequent agitation of the composter. The COW/NOW ratios of compost products ranged from 5 to 30, which were smaller than those in the initial mixtures. Among the 12 mixtures studied, only those using 100% rice husk (mixture A) and an equal-weighted mixture of rice husk and sawdust (mixture
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Table 5 Empirical coefficients of regression models in terms of water absorption capacity and free air spacea. Item
b0
b1
b2
b11
b22
b12
R2
F
Composting time (h)
3604.0 782.2 1430.0 171.8 7.6 10.4 0.6 8.6 29.7 22.7 155.2 56.6 1.7 9.1
3577.3 196.8 1353.4 50.5 7.6 0.9 3.7 0.5 129.5 0.4 288.1 1.6 53.0 0.1
1966.5 25.2 362.4 4.2 21.4 1.5 28.1 5.5 358.6 36.7 529.9 41.0 167.1 6.7
585.3 – 247.3 – 1.0 – 0.3 – 15.8 – 46.1 – 7.4 –
1751.8 – 252.9 – 22.6 – 25.9 – 342.5 – 445.6 – 134.0 –
701.8 – 143.3 – 3.9 – 3.0 – 67.4 – 80.0 – 19.6 –
0.978 0.880 0.893 0.598 0.883 0.407 0.877 0.786 0.905 0.661 0.586 0.349 0.635 0.205
36.91 36.79 11.72 7.43 10.57 3.43 10.03 18.31 13.36 9.74 1.99 2.68 2.44 1.28
Acidification time (h) Final pH value Lowest pH value Highest temperature (°C) COW/NOW COW/NOT a
Y0 = b0 + b1x3 + b2x4 + b11x3 + b22x4 + b12x3x4, or b0 + b1x3 + b2x4 capacity and free air space, respectively; x3, x4 are water absorption.
Fig. 2. Response surface of the highest temperature in terms of fractional compositions of bulking agents.
E) as bulking agents had a COW/NOW ratio within the maturity range of 5–6 as suggested by Chanyasak et al. (1982). Since the organics in the food waste stabilized at the end of the composting process, high COW/NOW and COW ratios indicated that sawdust and rice bran were in the process of dissociation. All final compost products except those from mixtures A and E failed the seed germination test as proposed by Zucconi et al. (1985). Only rice husk could be considered as a safe bulking agent that could be used alone. Sawdust could be mixed with rice husk, but the content should be controlled to less than 50%. Rice bran was not recommended as the bulking agent at all. 3.2. Derivation of empirical models By performing regression analyses of experimental data listed in Table 3, the coefficients of Eqs. (3), (4), (6), and (7) corresponding to each dependable variable were evaluated and listed in Tables 4 and 5, respectively. As shown in Table 4, all linear and the quadratic models in terms of weight frictions of bulking agents based on Eqs. (3) and
(4) were statistically significant, for the calculated F values were larger than F (2, 10, 0.05) value of 4.10 or F (5, 7, 0.05) value of 3.97 respectively. Most models of Eqs. (6) and (7) except the linear model for the lowest pH value and both models for COW/NOW and COW/NOT were also statistically significant as shown in Table 5. 3.3. Effects of bulking agents To examine the dependence of process characteristics on the compositions of bulking agents, it was convenient to use response surfaces on a ternary diagram. The quadratic models based on Eq. (4) were used, for the graphical illustration for the R2 and the calculated F values were in general larger than those of linear models. Figs. 1 and 2 showed the response surfaces of the predicted composting time and the highest temperature in terms of fractional compositions of bulking agents, respectively. Contour lines in Figs. 1 and 2 represent locations of equal values. The linear models also had a couple advantages due to their simplicity. First of all, linear models were easier to use; secondly, the linear coefficients of independent variables such as ai0 s or bi0 s
J.I. Chang, Y.J. Chen / Bioresource Technology 101 (2010) 5917–5924
of the linear models were actually first derivatives (ai = @Y/@xi), which could be interpreted as how dependent variables changed as independent variables varied. Therefore, by looking at the signs and the values of the linear coefficients, one would know the effects of the composition change of the bulking agent mixture on the process characteristics. As shown in Fig. 1, the composting time decreased rapidly as the sawdust fraction increased. When sawdust was the only agent used as in run B, only 150 h was needed. The addition of more sawdust in the mixture of bulking agents decreased the composting time, acidification time, final pH value, highest temperature, but increased the lowest pH value, and COW/NOW. Rice bran was the worst agent among the three. As the fraction of rice bran increased, the composting material became more compact and sticky due to its low water absorption capacity and free air space. All experimental results such as the composting time, pH values etc. except the highest temperature increased significantly. The linear coefficients of the water absorption capacity (b1’s) for most predicted responses except the lowest pH value in the linear models as shown in Table 5 were negative. Increasing the water absorption capacity of the composting mixture resulted in improving the composting rate, shorter composting and acidification time, lower highest temperature, and higher the lowest pH value. As the free air space of the composting matrix increased, composting rate accelerated and higher temperature reached due to more air flow through the composting matrix. This work was only the first step toward understanding the effects of bulking agents on the composting performance. Only sawdust, rice husk and rice bran were studied. Due to the limitation of the bulking agents selected, only the water absorption and the free air space were considered. The particle size, which significantly relates to the surface area of the solid, also affects mass transfers of water and air in the composting matrix and the composting performance (Eftoda and McCartney, 2004; Gea et al., 2007; Tremier et al., 2009). It will also be included in the future studies. 4. Conclusion This work demonstrates that physical properties of the composting feedstock significantly affect the composting process. The water absorption capacity of the composting mixture was the dominant physical property that affected the composting rate. More sawdust in the composting mixture resulted in the increases of the water absorption capacity and the composting rate, shorter composting and acidification times, and lower final pH value. As the free air space of the composting feedstock increased, composting rate accelerated and higher temperature reached due to more air flow through the particles. Among the 12 mixtures studied, only the products of a mixture prepared using 100% rice husk and another one using an equalweighted mixture of rice husk and sawdust as bulking agents passed the germination test. Therefore, rice husk was considered as a safe agent that could be used alone. Sawdust should be mixed with rice husk, but the content should be less than 50%. Rice bran was not recommended as a bulking agent. Acknowledgement The authors would like to express their gratitude to the Dachan Great Wall Enterprise Co. for providing wheat, soy, fish and meat meals. References Adhikari, B.K., Barrington, S., Martinez, J., King, S., 2008. Characterization of food wastes and bulking agents for composting. Waste Manage. 28 (5), 795–804.
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Adhikari, B.K., Barrington, S., Martinez, J., King, S., 2009. Effectiveness of three bulking agents for food waste composting. Waste Manage. 29 (1), 197–203. Agnew, J.M., Leonard, J.J., 2003. The physical properties of compost. Compost Sci. Util. 11 (3), 238–264. Amano, M., Atoni, T., 2002. Basic experiment in mixed composting of different organic wastes special reference to temperature with the addition of garbage. J. Agric. Sci. 46, 4265–4269. APHA, AWWA, WPCF, 1998. Standard Methods for the Examination of Water and Wastewater. American Public Health Association, American Water Works Association, and Water Pollution Control Federation, 20th ed. American Public Health Association, Washington, DC. ASTM, 2003. Standard Test Methods for Apparent Density, Bulk Factor, and Pourability of Plastic Materials. ASTM D1895–96. ASTM International, West Conshohocken. Atkins, P., de Paula, J., 2005. Elements of Physical Chemistry, fourth ed. Oxford University Press, Oxford. p. 262 (Chapter 11). Barrington, S., Choiniere, D., Trigui, M., Knight, W., 2002. Compost airflow resistance. Biosyst. Eng. 81 (4), 433–441. Bernal, M.P., Alburquerque, J.A., Moral, R., 2009. Composting of animal manures and chemical criteria fro compost maturity assessment. A review. Bioresour. Technol. 100 (22), 5444–5453. Box, G.E.P., Hunter, W.G., Hunter, J.S., 1978. Statistics for Experiments: An Introduction to Design, Data Analysis, and Model Building. John Wiley and Sons, New York. Chang, J.I., Hsu, T.E., 2008. Effects of composition on food waste composting. Bioresour. Technol. 99 (17), 8068–8074. Chang, J.I., Tsai, J.J., Wu, K.H., 2006. Thermophilic composting of food waste. Bioresour. Technol. 97 (1), 116–122. Chanyasak, V., Hirai, M., Kubota, H., 1982. Changes of chemical components and nitrogen transformation in water extracts during composting of garbage. J. Ferment. Technol. 60, 439–446. Chefetz, B., Hadar, Y., Chen, Y., 1998. Water extractable compounds released during composting of municipal solid waste. Acta Hortic. 469, 111–118. Cornell, J.A., 1990. Experiments with Mixtures. John Wiley and Sons, New York. Das, K.C., Tollner, E.W., Eiteman, M.A., 2003. Comparison of synthetic and natural bulking agents in food waste composting. Compost Sci. Util. 11 (10), 27–35. Eftoda, G., McCartney, D., 2004. Determination of the critical bulking agent requirement for municipal biosolids composting. Compost Sci. Util. 12 (3), 208–218. Gea, T., Barrena, R., Artola, A., Sanchez, A., 2007. Optimal bulking agent particle size and usage for heat retention and disinfection in domestic wastewater sludge composting. Waste Manage. 27 (9), 1108–1116. Hamelers, H.V.M., 1993. Theoretical model of composting kinetics. In: Hoitink, H.A.J., Keener, H.M. (Eds.), Science and Engineering of Composting. Renaissance Publications, Worthington, USA, pp. 36–58. Haug, R.T., 1993. The Principal Handbook of Compost Engineering. Lewis Publishers, Boca Raton, FL, USA. Huang, Y.M., 2005. Composting Methods. Food and Fertilizer Technology Center for the Asia and the Pacific Region, Taipei. Huang, G.F., Wong, J.W.C., Wu, Q.T., Nagar, B.B., 2004. Effect of C/N on composting of pig manure with sawdust. Waste Manage. 24 (8), 805–813. Hue, N.V., Liu, J., 1995. Predicting composting stability. Compost Sci. Util. 3 (2), 8– 15. Iqbal, M.K., Shafiq, T., Ahmed, K., 2010. Characterization of bulking agents and its effects on physical properties of compost. Bioresour. Technol. 101 (6), 1913– 1919. Kamolmanit, N., Reunghang, A., 2006. Effect of carbon to nitrogen ratio on the composting of Cassava pulp with swine manure. J. Water Environ. Technol. 4, 33–50. Kato, K., Miura, N., 2008. Effect of matured compost as a bulking and inoculating agent on the microbial community and maturity of cattle manure compost. Bioresour. Technol. 99 (9), 3372–3380. Khuri, A.I., Cornell, J.A., 1987. Response Surfaces Design and Analysis. Dekker, New York. Kim, K.Y., Kim, H.W., Han, S.K., Hwang, E.J., Lee, C.Y., Shin, H.S., 2008. Effect of granular porous media on the composting of swine manure. Waste Manage. 28 (11), 2336–2343. Kulcu, R., Yaldiz, O., 2007. Composting of goat manure and wheat straw using pine cones as a bulking agent. Bioresour. Technol. 98 (14), 2700–2704. Kumar, M., Ou, Y.L., Lin, J.G., 2010. Co-composting of green waste and food waste at low C/N ratio. Waste Manage. 30 (4), 602–609. Nakasaki, K., Nagasaki, K., 2004. Degradation of fats during thermophilic composting of organic waste. Waste Manage. Res. 22, 276–282. Scheff’e, H., 1963. The simplex-centroid design for experiments with mixtures. J. Roy. Stat. Soc. Ser. B (Methodol.) 25, 235–263. Schwab, B.S., Ritchie, C.J., Kain, D.J., Dobrin, G.D., King, L.W., Palmisano, A.C., 1994. Characterization of compost from a pilot plant-scale composting utilizing simulated solid waste. Waste Manage. Res. 123, 289–303. Tajima, K., Yoshimura, M., Kato, M., Tatsuno, J., Tamaki, K., 2003. Enhancement of composting process by the addition of chicken manure and rice bran to tree pruning chips. Jpn. J. Farm Work Res. 38 (4), 207–213. Tremier, A., Teglia, C., Barrington, S., 2009. Effect of initial physical characteristics on sludge compost performance. Bioresour. Technol. 100 (5), 3751–3758. Wen, T.C., Cheng, S.S., Lay, J.J., 1994. A kinetic model of a recirculated upflow anaerobic sludge blanket treating phenolic wastewater. Water Environ. Res. 66, 794–799.
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J.I. Chang, Y.J. Chen / Bioresource Technology 101 (2010) 5917–5924
Wu, L., Ma, L.O., 2001. Effects of sample storage on biosolids compost stability and maturity evaluation. J. Environ. Qual. 30, 222–228. Yanez, R., Alonso, J.I., Diaz, M.J., 2009. Influence of bulking agent on sewage sludge composting process. Bioresour. Technol. 100 (23), 5827–5833.
Zhu, N., 2007. Effect of low initial ratio on aerobic composting of swine manure with rice straw. Bioresour. Technol. 98 (1), 9–13. Zucconi, F., Monaco, A., Forte, M., 1985. Phytotoxins during the stabilization of organic matter. In: Gasser, J.K.R. (Ed.), Composting of Agricultural and Other Wastes. Elsevier Applied Science Publications, England, pp. 73–85.