[15] Tweib, et al., 2014
DOI:
10.5276/JSWTM.2014.136
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF
ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
WITH PALM OIL MILL SLUDGE (POMS)
Saleh Ali Tweib1, Rakmi Abd Rahman2, Mohd Sahaid Khalil3
1,2,3
Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment
University Kebangsaan Malaysia 43600 UKM Bangi Selangor Darul Ehsan Malaysia
ABSTRACT
Solid waste generated in Malaysia constitutes of large portion of organic material that can be
readily composted. Composting which dispose of the organic material, and at the same time
producing usable compost as the end product is thought to be a good option for organic wastes
disposal. A reactor is chosen to be utilised in this study since it is economical and is a simpler
alternative compared to other existing composting systems. Co-composting of palm oil mill
sludge (POMS) and solid waste (SW) can be regarded as an environmental-friendly approach if
compared to the current method whereby wastes are disposed in landfills. The compost starter
was estimated based on its efficiency. The objectives were to investigate the appropriate 1:2
SWK to POMS mixing ration compost maturity and quality. The potential of the composting
process is then found by using the proposed substrate and the possible usages of the compost
for agricultural activities. Besides that, the physicochemical changes that occurred during the
entire process of composting palm oil mill sludge together with solid waste were also studied.
The pH value also decreased along the process and the final pH recorded was 7.28.
Percentage of moisture content reduced from 64 to 55.32during the process. The highest
temperature achieved was about 34.23°C, and eventually dropped to 20°C in the stages which
followed. The model resulted in an experimental exponential equation. It also enable the
formulation of another linear equation there from, that eventually give in the value of Kl and K2
(whereby Kl is the process constant and K2 is the process variable of a compo sting system).
The model produced has a mathematical expression of y = 87.867 X-0.251 with R-square value of
0.943 , and gave in the value of Kl and K2 81.64 and 1.0301. The results showed that the model
is capable of describing the actual status of the kinetics composting
Keywords: Michaelis–Menten Model and Composting Kinetics
INTRODUCTION
Waste generation has become a significant management
problem in many developing countries. Special emphasis
should be given on the effective management of solid waste
treatment (Braun et al. 2003). Large amounts of organic
wastes are generated daily by households, agricultural
activities, markets and industry. Most of the solid wastes
from industries, markets and households all over the world
are deposited in landfills. This is an undesirable solution
because the esthetical value of the landscape is tarnished and
various harmful pollutants are released into the groundwater
(Ministry of Environment 1999). The issue of solid waste
_____________________________________
1,2,3
136
Correspondence author
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
disposal in Malaysia is becoming a force to be reckon with,
thus it has attracted the attention of the Malaysian
government and its citizens as well. With the decrease of
suitable sites and the increased cost of developing new sites,
effective efforts need to be exerted by the local authorities to
develop alternative methods to meet the ever-evolving
challenges of waste disposal [Surender et al. 2007]. In 2001,
the total solid waste generated in Peninsular Malaysia was
16,200 ton metric per day. This figure increased drastically to
19,100 ton metric a day in year 2005. This figure translates to
an average of 0.8 kilograms per capita per day [Anon 2006].
This rapid increase in solid waste generation can cripple
Malaysia’s capabilities of providing adequate waste
management services if not met with swift and proper actions
[Lens 2004]. The management options used currently
includes waste separation, burying, combustion or
incineration, biological treatment and re-usage in agricultural
and industrial activities [Lens 2004]. Over the last four
decades, the palm oil industry has grown by leaps and bounds
to become a very important agriculture based industry in
Malaysia. The total area under oil palm cultivation is about
4.05 million ha, with the total palm oil production of 16.8
million tonnes [Astimar 2006].Composting has been
presented as an environmental friendly solution and a
sustainable alternative approach to manage and recycle
organic solid wastes, with the aim of obtaining quality
organic products known as composts which can be used as an
organic amendment in agriculture (Haug 1993). When
compared to the available technologies to the recycling of
organic solid waste, composting is often presented as a lowtechnology and low-investment process to add value to
organic solid waste through conversion into an organic
fertilizer known as compost (Zucconi et al. 1987).
Composting is defined as a biological decomposition and
stabilization of organic substrates under conditions that allow
the development of thermophilic temperatures as a result of
biologically produced heat, to yield a final product that is
stable, free of pathogens, plant seed and can be beneficially
applied to land (Haug 1993). Composting is generally
achieved by converting solid wastes into stable humus like
materials via biodegradation of putrescible organic materials.
The microorganisms involved in solid waste composting
include facultative and strict aerobic bacteria, fungi, and
actinomycetes. In this study, the composting process of mixed
palm oil mill sludge with solid waste was studied as a
potential alternative to treat and recycle these wastes. This
study aims to identify the potential of composting process
using the proposed substrate and the possible usages of the
compost in agricultural activities. Besides that, the
physicochemical changes that occurred during the entire
process of composting palm oil mill sludge together with
solid waste were also studied and kinetic estimation
behaviour of co-composting process of palm oil mill sludge
(POMS) with solid waste (Kitchen Waste), by using
Michaelis-Menten model and a mathematical logarithm
method for calculating kinetic suggested based on
composting of solid waste with palm oil mill sludge (POMS)
using a rotary drum composter. The composting process was
conducted using a rotary composter reactor system as it is
economical compared to other existing composting system.
MATERIALS AND METHOD
In this study co-composting was used to produce
compost from solid waste and palm oil mill sludge. Solid
waste samples were collected from the wet market of Bandar
Baru Bangi, Malaysia. Solid waste generated in the wet
market included wastes such as green vegetables, cardboard
boxes, wooden packs, waste from meat, chicken, seafood and
etc. Based on an interview with Kajang Alam Flora Officers,
the daily amount of waste generated at this particular wet
market was around 8 tonnes. The respected wet market is
monitored by the Kajang Municipal Council (MPKJ) and the
contactor responsible for the waste collection is the Alam
Flora Company. There are two rorobins placed outside the
wet market which serves as a waste collection centre. Each
rorobin has a capacity of 4-5 tones. Alam Flora collects the
wastes from the rorobins at 10-11 a.m. daily. The wastes are
then sent to the Refuse Derived Fuel (RDF) station in
Semenyih to be sorted out for recycling and energy
production. The non-recyclable waste is sent to a landfill in
Sungai Kembong. Sludge from anaerobic digestion pond was
collected from Sri Ulu Langat Palm Oil Mill in Dengkil,
Selangor. The sludge is of high moisture content, low carbon
content and high nutrient value. It is usually dried to be used
as fertilizer. This study was carried out in the Laboratory of
Environmental
Engineering,
University
Kebangsaan
Malaysia. Solid wastes from market waste were combined
with dewatered palm oil mill sludge (POMS) from Sri Ulu
Langat Palm Oil Mill in Dengkil, Selangor with mixing ratios
of 1:2 using a rotator composter.
PHYSICAL CHARACTERISTICS OF RAW
MATERIALS
Parameters of raw palm oil mill sludge (POMS) and
solid waste (SW) are as shown in Table 1. The solid waste
was first collected from a wet market and then noncomposting materials such as metal, plastic and etc. were
removed. They were then chopped into smaller sizes and
placed in a small box of 35.5cm x 27cm x 30cm which was
later sent to the laboratory of Environmental Engineering,
University Kebangsaan Malaysia. The physical tests were
conducted on raw materials and solid waste before mixing it
with sludge .Sample sludge for anaerobic digestion was
collected from a pond in Sri Ulu Langat Palm Oil Mill in
Dengkil, Selangor. The sludge is of high moisture content,
low carbon content and high nutrient value. It is usually dried
to be used as fertilizer. The sludge was physically dried. The
initial parameters addition of solid waste with palm oil mil
sludge changed several parameters are given in Table 2.
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
137
TABLE 1
Physicochemical analyses of raw palm oil mill sludge (POMS) and Solid Waste (SW)
Parameters
POMS
Solid Waste
Recyclable compost
MC%
82.67
60.1
64.55
Wd kg/m3
950
510.48
536.24
pH
7.9
6.2
6.55
TOC%
33.48
45.16
41
P%
1.05
0.11
0.6
K%
2.05
1.237
1.43
C/N
8.65
34
13
TABLE 2
Parameters recorded for initial material mixture of ration 2:1 (POMS: SW)
Parameters
Initial material mixture
Ratio 2:1
Phosphorus (%)
1.14
Potassium (%)
0.42
pH
7.84
Nitrogen (%)
2.38
MC%
53.23
TOC%
46.23
C/N
32
ROTARY COMPOSTER DESIGN AND
DESCRIPTION USED IN THIS STUDY
Rotator composter reactor system as shown in Figure 1
consists of 3 main components. They are the rotator drum, air
compressor and gas absorber. Rotator drums are facilitated
with 3 phase motor. There are 8 inner blades with length of 5
cm each in order to enhance the mixing process in the reactor.
On the other hand, the air compressor functions to provide air
to the reactor. The gas absorber functions to absorb gas and
air resulting from the process inside the reactor. The absorber
used in this reactor is charcoal. The characteristic of each
components of the reactor is shown in Table 4. Mixing of
palm oil mill effluents sludge and solid waste were inserted
through the feeding part.
CHEMICAL AND PHYSICAL ANALYSIS
Sampling was done by taking 100g of compost from the
rotary drum composter. The temperature was measured using
138
a thermometer at the core of the reactor on days 0, 5, 10, 15,
20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95,
and 100. The pH value was determined using a method
described by [Carnes 1970] in which at least 10 grams sample
of the organic waste was poured into 500 ml distilled water
and stirred vigorously for 3 to 5 minutes. When the mixture
settles, the pH of the mixture was measured as Figure 3. The
determination of the total moisture content was done by
weighing a sample of waste and drying it in a conventional
oven at a temperature of 1050C for 24 hours and then the
sample is weighted again [Romeela and Mohee 2005] as
Figures 4 and 5. The percentage of moisture content was
calculated using the formula as shown below:
VOLATILE SOLIDS AND ASH CONTENT
Volatile solid has been widely accepted by composting
research as a mean to estimate the carbon fraction in compost
materials. The relationship between carbon fraction and
volatile solid content was first developed by New Zealand
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
FIGURE 1
Rotary composter reactor system
TABLE 3
Physicochemical analysis of final compost
Parameters
Final compost
Standards Values
Reference
MC%
50.32
40-65
( Rynk et al.,1992)
Wd kg/m3
480.12
500-600
( Goldstein , 2002)
pH′
7.02
5.5-9
( Rynk et al.,1992)
TOC%
40.21
30-48
(Navarro et al.,1990)
P%
1.27
> 0.5
( Nogueiraetal ., 1999)
K%
2.68
> 1.5
> 1.5 ( Nogueiraetal ., 1999)
C/N
19
19
20-41 ( Rynk et al.,1992)
engineers and published in their second interim report of the
inter departmental committee on Utilization of organic wastes
in 1951 and is shown as below:
% Carbon = 100- % Ash /1.8
= 1/1.8 (% Volatile Solids)
= 0.56 (% Volatile Solids)
Whereby,
100- % Ash (% Volatile Solids)
In another publication by World Health Organisation,
International Reference Centre for wastes disposal (1978),
carbon content was estimated by the following relationship
% Carbon = a x % volatile solids
Where a (refuse) = 0.42
A (compost) = 0.52
Undoubtedly, volatile solids are an indirect measure of
carbon fraction for composting materials. However, the
values of volatile solids will not be converted by the above
mentioned factor (i.e. 0.56 and 0.52) in this study. The dry
residue remained from the moisture content analysis was
heated to 500 ± 50 C0 in a muffle furnace for 2 hours. The
residue was allowed to cool down in a desecrator and
reweighted again. Volatile solids can be expressed as follows:
Volatile Solids = Initial Weight – Final Weight/ Initial Weight
X 100.
Non-volatile solid or ash content (in percentage) = 100 – VS
The volatile solids are often termed as the total organic
matter, but it is subjected to < 1% of error. This error is due to
the fact that organic fraction is not the only substance that
will be volatilized at 550 C0, but some of the inorganic
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
139
TABLE 4
Rotary composter reactor system specifications
Rotator Composter System
Material
Stainless steel
Length
3m
Diameter
0.6 m
Initial active volume
0.4 m3
Maximum rotation
2 rpm (rotation per minute)
Air Compressor
Model
SWAN DR 115
Ability
122 liter/min
Speed
1450 rpm (rotation per minute)
Cycle
50 Hz
Motor
Model
CHENTA, Taiwan
Volt
400 V
Current
9.1 A
Cycle
3.50 Hz
FIGURE 2
The flow of materials in the rotating drum reactor system
substance may get volatilized as well. (World Health
organization 1978).
FLOW CHART OF METHODOLOGY
RESULT AND DISCUSSION
pH
pH is an important parameter that can control the
depletion of nitrogen due to ammonia volatilization [Qiao, L.
140
& G. Ho. 1997]. A pH out of the 5-9 range would threaten any
biological activity. The pH level of the composting mass
typically varies with time during composting process. Along
the experimental period, solid waste and sludge were added
accordingly at the beginning in every batch. The initial pH
value is 8.01 and it was reached to levels as high as pH 8.83
when the process is in the second batch. pH was initially
recorded as 8.01 and then increased with time and reached a
peak of 8.83 around day 20 and this was followed by
reduction to 7.28 by the end of the process. Increase of pH
value at the beginning of the composting process maybe due
to the protein mineralization which affected to the increase in
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
FIGURE 3
Sample stirred
FIGURE 4
Mass of sample using digitalscales dumbing
ammonia generated by the biochemical reactions of nitrogencontaining materials and changing of amino acids and peptide
to the ammonia (Paredes et al. 2002; Crowford 1983; Liao et
al. 1994).Besides, the decreasing of pH values at the end of
the composting process agreed with the Sundberg et al.
(2004) that reported for fully developed composting, the pH
often rises to 8-9. In addition, the composting is dominated
by bacteria, which are generally not as acid tolerant. Figure 6
shows the graph of pH profile along the composting process.
Temperature Profile
Temperature is the most important indicator of the
efficiency of the composting process (Xiujin et al., 2008).
The palm oil mill sludge (POMS) used in this study contains
high level of organic matter, therefore when a highly organic
matter is added to a highly cellulosic material like solid waste
kitchen, waste heat is generated in the rotary composter as a
result of biodegradation (Miyatake & Iwabuchi. 2005). In this
study, the temperature reading taken at outlet section and the
initial temperature for experimental process measured was
26.23˚C. However, the temperature profile increased to
34.23˚C which reflects microbial decomposition activity in
the compost as shown in Figure 7. For 20 days, the
fermentation temperature was maintained around 26.2334.23°C indicating a thermophilic phase, on day 20 the
fermentation reached a maximum temperature of 34.23°C.
The temperature of rotary composter reduced gradually after
it reached the maturity phase on day 40 of the treatment. A
significant decrease in temperature to 20°C was noticed at the
end of the composting process, indicating microbial activities
had been reduced due to the depletion of biodegradable
substrates for their growth and survival. Most studies
reported that the optimum temperature range for effective
decomposition was 50-70˚C, with 60˚C being the most
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
141
FIGURE 5
Oven used for drying sample
FIGURE 6
Graph of pH versus Time
FIGURE 7
Temperature profile against time of composting
satisfactory level (Wong et al., 2001). However, for this
study, the temperature profile obtained was under the
optimum range for the sanitation and degradation process. In
order to increase the temperature of the compost process,
biodegradable carbon sources can be added. Stentiford (1996)
142
in his study stated that, maximum temperature of 55-65˚C is
necessary to destroy pathogens, but temperature of 45-55˚C
must be maintained for maximum degradation. Figure 3
shows the temperature profile for the experiment of
composting process.
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
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FIGURE 8
Moisture Content MC% profile against time of composting
FIGURE 9
Change of VS/R versus VS and their respective regression factors for series
Moisture Content MC%
COMPOSTING KINETICS
The moisture content level was the critical factor that
determined the decomposition rate in composting. For this
study, sludge (POMES) was added onto the compost
materials solid waste (SW) according to a ratio of 2:1. The
reactor was injected with air by using an air compressor in
order to reduce the moisture level of the compost product.
The moisture content of the compost materials was too high,
with a range of 64-70% and it then decreased over time to
reach 55.32% at the end of the treatment as Figure 8.
Moisture content of more than 70% can cause leaching in the
composting process (Lau, H.L.N., Y.M. Choo, A.N. Ma &
C.H. Chuah. 2008).
Kinetics is the study of rates of reactions. Composting
kinetics in particular deals with the rate of the composting
process, that is, the velocity at which biodegradable matter in
the composting materials is consumed. (Waksman et al.,
1939). The data was manipulated and best fitted based on the
procedure of Michaelis Menten model ( 1980).
CX* = CX / K1 ( 1 )
K1 = K1 + K2 / K1 ( 2 )
R= ( K2 C ) 1/ K1 + C ( 3)
Where
R = Consumption rate of substrate or reaction rate.
CX* = Activated substrate –organism complex.
C = Substrate , carbon.
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
143
X= Free organisms
K1 = System constant of composting system.
K2= System variable of composting system.
The data for experiment carried out at 34.230C was fitted by
an exponential function. The exponential functions were
derived. These functions and the corresponding regression
factors are included in Table 5 as exponential functions
describing the variation of volatile solids and their respective
regression factors.
solids were derived. The differential equation of the best –fit
exponential function is written as below:
Series
R1 =dy / dx= -22.0104 X-1.2380 (7)
R2=dy/dx= -18.4865 X-1.2133 (8)
R3=dy/dx = -19.5924 X-1.2273 (9)
For equation R1 = y = 87.867 X t.p.o - 0.251
By differentiating this equation
Best fit equation (volatile solid VS with time) in power
function for experimental results
R1 = y = 87.867 X t.p.o - 0.251 ( 4)
R2 = y= 85.437 X t.p.o -0.2123 (5)
R3 = y= 86.179 X t.p.o - 0.2268 (6)
Where
R = Consumption rate of substrate or reaction rate.
VS = Volatile solids.
By using Minitab Program
Scatterplot of VS/R vs. VS
The exponential functions were differentiated and the
differential equation denoting the rate of change of volatile
TABLE 5
The change of volatile solids for experimental model
Series
Exponential Function
Regression Factor
R1
Y= 87.867 X-0.251
0.8021
R2
Y= 85.437 X-0.2123
0.713
R3
Y= 86.179 X-0.2268
0.7612
TABLE 6
The changes of volatile solids (%Wt) over time for all the three series ( R 1, R2 and R3)
Analysis
144
The changes of volatile solids over time for all the three series
Sample/Day
R1
R2
R3
0
78.1252
79.123
77.11
1
70.7542
70.6926
71.69
2
72.1213
73.71
64.91
3
68.762
66.074
60.482
4
64.5354
61.6201
62.1300
5
56.9174
63.59
66.6052
6
54.137
63.31
50.1622
7
50.326
58.165
46.028
8
52.0722
43.0711
48.749
9
42.7222
62.041
43.1213
10
41.7414
51.63
46.042
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TABLE 7
Data Analysis for series 1 ( R1)
Time , day
VS
R
VS/R
0
79.12
22.0079
3.59
1
70.89
8.796
8.059
2
72.76
5.567
13.069
3
67.79
3.69
18.371
4
64.67
2.846
22.723
5
58.71
2.437
24.09
6
55.68
1.826
30.492
7
51.33
1.547
33.1803
8
54.072
1.511
35.7855
9
42.876
1.2304
34.847
10
43.86
1.41
31.106
TABLE 8
Intercept , Slope , K2 and K1 for series R1
Samples for series
Intercept
Slope
Regression Factor, RSq
K2
K1
59.5
-0.642
94.3%
1.0301
81.6406
R1
Regression Analysis: VS/R versus VS
The regression equation is
y = 59.5 - 0.642 x
Predictor CoefSECoef T P
Constant 59.463 4.642 12.81 0.000
x -0.64201 0.07893 -8.13 0.001
S = 2.06549 R-Sq = 94.3% R-Sq(adj) = 92.9%
Analysis of Variance
Source DF SS MS F P
Regression 1 282.27 282.27 66.16 0.001
Residual Error 4 17.06 4.27
Total 5 299.33
The value of volatiles solids (VS) and absolute value of
the rate of change (R) are calculated and manipulated in such
a way that the relationship between volatile solids (VS) can
be graphically described. The relationships were best–fitted
by linear equation as shown in figure. The linear equations
and their respective regression factors are as shown in Table
9.
From the value of regression factor for both the
exponential function and linear equation, series R1 has the
best fit. Corresponding data’s, the exponential model
generated in series R1 was opted as the kinetic model for
composting process. This model was compared to another set
of verification data to verify its appropriateness in describing
the actual composting process. The intercepts and slopes of
the linear equations were computed to give the value of K1
and K2. The K2 value was calculated by inverting the value of
slope, and K1 value was calculated by multiplying the value
of K2 and intercept. The value of slope intercept, K1, and K2
are summarized in Table 10.
K1 value is in the range of 80-83 whereas the K2 value is
within the range of 0.9-1.1. The mean for K1 and K2 are
81.3667 and 0.960 respectively.
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
145
TABLE 9
Linear equation of VS/R versus VS and their respective regression factors
Series
Linear Equation
Regression Factor
R1
Y= -0.642 x+59.5
0.943
R2
Y= -0.895 X + 87.2
0.686
R3
Y= -1.02 X + 86.8
0.672
TABLE 10
Value of Slope , Intercept, K1 and K2
Series
Slope
Intercept
K1
K2
R1
-0.642
59.5
81.6406
1.0301
R2
-0.895
87.2
82.67
0.867
R3
-1.02
86.8
80.6013
0.931
81.637
0.9427
Mean
TABLE 11
The comparison is shown in this table comparison of K1 and K2 value with other study
K1
Whang and Meenghan
K2
1.0623
1.2243
1.1362
0.0113
0.0325
0.0292
81.6406
82.67
80.6013
1.0301
0.867
0.931
(1980)
Experimental Results
The results show that the percentage error for K2 ranges
from 3.38 to 9.36 with an average of about 6.24. The
maximum percentage error for K1 is only 1.98 and the low
side reading for percentage error is 0.93. The percentage error
for K1 is 1.32 %, whereas the percentage error for K2 is 6.24
%. This shows that K2 is relatively more varied than K1. This
is consistent with the Michaelis-Menten model, whereby K1
is a dissociation constant that should be constant in a given
system. On the other hand, K2 is a system variable dependent
to microbial population in the composting materials. These
constants were also compared to the constant obtained by
Whang and Meenghan (1980)
Both the K1 and K2 for these two studies do not agree
well with each other, due to difference in the experimental
design for this study. Whang and Meenaghan (1980)
employed carbon fraction as their indicating parameter and
146
was expressed in weight of carbon (gram) per weight of ash
(gram), which eventually resulted in a dimensionless
indicator (g/g). However, the indicating parameters for this
study were volatile solids and were expressed in percentage
of volatile solids per total dry sample weight.
Besides
that, the K1value is corresponding to the initial concentration
of the indicating parameter. Therefore the magnitude of K1 for
this study are in two digits (expressed in percentage), while
the magnitude for K1 for Whang and Meenaghan (1980) was
only in one digit (expressed in fraction).
CONCLUSIONS
All in all, it can be concluded from this study that the
composting process is an option that has a talent in stabilising
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
and re-using waste for agricultural purposes. Solid waste
(SW) to palm oil mill sludge (POMS) mixing ratio of 1:2 is
more likeable than 1:3 because it contains more essential
nutrients for plant growth, such as N and C, and for building
soil organic matter content. The composting process kinetic
model developed for this project establishes the mathematical
expression
Y = 87.867 X-0.251
K1 value obtained from this study is 81.64, R2 is 0.943 and K2
is 1.0301. These values are acceptable for homogenized
carbon hydrate – based materials. In summary, the above
result proves that co-composting of solid waste with palm oil
mill sludge (POMS) can be used as an alternative method for
improving treatment of wastes and optimize kinetics by using
an economical and simple system,
ACKNOWLEDGMENT
The authors would like to thank Prof. Rakmi Abd
Rahman for the financial and technical support without which
this research could not be conducted and completed. We
would also like to thank the Majlis Perbandaran Kajang
(MPKJ) for allowing us to collect waste from the wet market
at Bandar Baru Bangi.
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
Anon. 2006.Ninth Malaysia Plan Sembilan. Bab 2 Promoting Environmental Care: pp. 481-482.
Astimar, A.A. & M.B. Wahid. 2006. Supply outlook of
oil palm biomass in Malaysia. Proceeding of the Seminar
on Ecomat Research and Promotion, 24-25 July: 13-26.
Baharuddin, A.S., M. Wakisaka, Y. Shirai, S. Abd-Aziz,
N.A. Abdul Rahman & M.A. Hassan 2009. “Cocomposting of Empty Fruit Bunch and Partially Treated
Palm Oil Mill Effluent in
Pilot Scale.” Intern. J.
Agric. Res. Volume 4: pp. 69-78.
Carnes, R.A.. 1970. “An Investigation of the pH
Characteristics of Compost.” Compos Science, Sept-Oct.
F. Zucconi, M. de Bertoldi,” Compost specifications for
the production and characterization of compost from
municipal solid waste.” In: deBertoldi, M., Ferranti,
M.P., L’Hermite, P., Zucconi, F. (Eds.), Compost:
Production, Quality and
Use. Elsevier, Barking,
pp. 30–50, 1987. Harilingen, Texas.
Gilmour, J.T., M.D. Clark, D. Mark and S.M. Daniel
1996. “Predicting long-term decomposition of biosolids
with a seven–day test.” Journal of Environmental
Quality, Volume 25(4): pp. 766-770.
Haug, R.T., 1993. The Practical Handbook of Compost
Engineering. Lewis Publishers, Boca Raton, Florida,
USA.
Haug, R.T. 1993.The practical Handbook of Compost
Engineering. Lewis publishers. Boca Raton. p. 717.
9.
Lau, H.L.N., Y.M. Choo, A.N. Ma & C.H. Chuah. 2008.
Journal of Food Engineering Selective extraction.
10. Lens, P., Bert Hamelers, H. Hoitink, & W. Bidlingmaier,
2004. “Resource Recovery and Reuse in Organic matrix
Ofpalm carotene and vitamin E from fresh palm-press
mesocarp fiber (Elaeisguineensis) using Supercritical
CO2.” Volume 84: pp. 289 -296.
11. Ministry of Environment (MOE). 1999. Korea
Government Report on Environment. Government
Printing Registered Number 11-1380000-000156-10,
Korea.
12. Miyatake, F. & K. Iwabuchi. 2005. “Effect of high
temperature on enzymatic activity and species diversity
of culturable bacteria in cattle manure compost.”
Bioresource Technology.,Volume 96: pp. 1821-1825
DOI: 10.1016/j.biortech.2005.01.005.
13. New Zealand engineering (1951) – “Second Interim
Report of the inter departmental
Committee on
Utilization of organic waste.” New Zealand Engineering.
Volume 6: pp. 11-12.
14. Pressel .F., and W. Bidlingmaier 1981. “Analyzing decay
rate of compost.” Biocycle. Volume 22(5): pp. 50-51.
15. Qiao, L. & G. Ho. 1997. “The effects of clay amendment
on composting of digested sludge.” Water Res., Volume
31: pp. 1054-1056.
16. Braun, R., E. Brachtl, M. Grasmug, “Codigestion of
protein aceous industrial waste,” 2003, Applied
Biochemistry and Biotechnology, Volume 109: pp. 139153.
17. Romeela, Mohee. &Ackmez Mudhoo. 2005. Analysis of
the physical properties of an in-vessel composting
Seminar on Ecomat Research and Promotion, 24-25 July:
13- 26. Solid Waste Management. United Kingdom:
IWA Publishing.
18. Surender, A. & A. Reddy, 2007. Composting feasibility
study and conceptual planning for the city of Tiquia,
S.M.N.F.Y. Tam and I.J. Hodgkiss (1996). Microbial
activities during composting of spent pig-manure
sawdust litter at different moisture content. Bioresource
Technology. Volume 55: pp. 201-206.
19. Thambirajah, J.J., M.D. Zulkifli, and M.A. Hashim,
1995. “Microbiology and biochemical changes during
the composting of oil palm empty fruit bunches. Effect of
nitrogen supplementation on the substrate.” Bioresour.
Technol. Volume 52: pp. 133-144.
20. Unit Operations in Resource Recovery Engineering
Prentice-Hall, Inc. V. Miikki, N. Senesi, K. Hanninen,
“Characterization of humicmaterial formed by
composting of domestic and industrial biowastes. Part
2.Spectroscopic evaluation
of humic acid structures,”
1997, Chemosphere, Volume 34, pp. 1639–1651.
21. Whang, D.S. and G.F. Meenaghan 1980. “Kinetic model
composting process compost Science/ Land Vtilization”
Volume 21 (3): pp. 44-46.
22. World Health organization (1978). Methods of Analysis
of sludge solid waste and compost. World Health
organization International Reference Centre for waste
disposal CH-8600 Dubendorf Switzerland.
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
147
10.5276/JSWTM.2014.136
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF
ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
WITH PALM OIL MILL SLUDGE (POMS)
Saleh Ali Tweib1, Rakmi Abd Rahman2, Mohd Sahaid Khalil3
1,2,3
Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment
University Kebangsaan Malaysia 43600 UKM Bangi Selangor Darul Ehsan Malaysia
ABSTRACT
Solid waste generated in Malaysia constitutes of large portion of organic material that can be
readily composted. Composting which dispose of the organic material, and at the same time
producing usable compost as the end product is thought to be a good option for organic wastes
disposal. A reactor is chosen to be utilised in this study since it is economical and is a simpler
alternative compared to other existing composting systems. Co-composting of palm oil mill
sludge (POMS) and solid waste (SW) can be regarded as an environmental-friendly approach if
compared to the current method whereby wastes are disposed in landfills. The compost starter
was estimated based on its efficiency. The objectives were to investigate the appropriate 1:2
SWK to POMS mixing ration compost maturity and quality. The potential of the composting
process is then found by using the proposed substrate and the possible usages of the compost
for agricultural activities. Besides that, the physicochemical changes that occurred during the
entire process of composting palm oil mill sludge together with solid waste were also studied.
The pH value also decreased along the process and the final pH recorded was 7.28.
Percentage of moisture content reduced from 64 to 55.32during the process. The highest
temperature achieved was about 34.23°C, and eventually dropped to 20°C in the stages which
followed. The model resulted in an experimental exponential equation. It also enable the
formulation of another linear equation there from, that eventually give in the value of Kl and K2
(whereby Kl is the process constant and K2 is the process variable of a compo sting system).
The model produced has a mathematical expression of y = 87.867 X-0.251 with R-square value of
0.943 , and gave in the value of Kl and K2 81.64 and 1.0301. The results showed that the model
is capable of describing the actual status of the kinetics composting
Keywords: Michaelis–Menten Model and Composting Kinetics
INTRODUCTION
Waste generation has become a significant management
problem in many developing countries. Special emphasis
should be given on the effective management of solid waste
treatment (Braun et al. 2003). Large amounts of organic
wastes are generated daily by households, agricultural
activities, markets and industry. Most of the solid wastes
from industries, markets and households all over the world
are deposited in landfills. This is an undesirable solution
because the esthetical value of the landscape is tarnished and
various harmful pollutants are released into the groundwater
(Ministry of Environment 1999). The issue of solid waste
_____________________________________
1,2,3
136
Correspondence author
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
disposal in Malaysia is becoming a force to be reckon with,
thus it has attracted the attention of the Malaysian
government and its citizens as well. With the decrease of
suitable sites and the increased cost of developing new sites,
effective efforts need to be exerted by the local authorities to
develop alternative methods to meet the ever-evolving
challenges of waste disposal [Surender et al. 2007]. In 2001,
the total solid waste generated in Peninsular Malaysia was
16,200 ton metric per day. This figure increased drastically to
19,100 ton metric a day in year 2005. This figure translates to
an average of 0.8 kilograms per capita per day [Anon 2006].
This rapid increase in solid waste generation can cripple
Malaysia’s capabilities of providing adequate waste
management services if not met with swift and proper actions
[Lens 2004]. The management options used currently
includes waste separation, burying, combustion or
incineration, biological treatment and re-usage in agricultural
and industrial activities [Lens 2004]. Over the last four
decades, the palm oil industry has grown by leaps and bounds
to become a very important agriculture based industry in
Malaysia. The total area under oil palm cultivation is about
4.05 million ha, with the total palm oil production of 16.8
million tonnes [Astimar 2006].Composting has been
presented as an environmental friendly solution and a
sustainable alternative approach to manage and recycle
organic solid wastes, with the aim of obtaining quality
organic products known as composts which can be used as an
organic amendment in agriculture (Haug 1993). When
compared to the available technologies to the recycling of
organic solid waste, composting is often presented as a lowtechnology and low-investment process to add value to
organic solid waste through conversion into an organic
fertilizer known as compost (Zucconi et al. 1987).
Composting is defined as a biological decomposition and
stabilization of organic substrates under conditions that allow
the development of thermophilic temperatures as a result of
biologically produced heat, to yield a final product that is
stable, free of pathogens, plant seed and can be beneficially
applied to land (Haug 1993). Composting is generally
achieved by converting solid wastes into stable humus like
materials via biodegradation of putrescible organic materials.
The microorganisms involved in solid waste composting
include facultative and strict aerobic bacteria, fungi, and
actinomycetes. In this study, the composting process of mixed
palm oil mill sludge with solid waste was studied as a
potential alternative to treat and recycle these wastes. This
study aims to identify the potential of composting process
using the proposed substrate and the possible usages of the
compost in agricultural activities. Besides that, the
physicochemical changes that occurred during the entire
process of composting palm oil mill sludge together with
solid waste were also studied and kinetic estimation
behaviour of co-composting process of palm oil mill sludge
(POMS) with solid waste (Kitchen Waste), by using
Michaelis-Menten model and a mathematical logarithm
method for calculating kinetic suggested based on
composting of solid waste with palm oil mill sludge (POMS)
using a rotary drum composter. The composting process was
conducted using a rotary composter reactor system as it is
economical compared to other existing composting system.
MATERIALS AND METHOD
In this study co-composting was used to produce
compost from solid waste and palm oil mill sludge. Solid
waste samples were collected from the wet market of Bandar
Baru Bangi, Malaysia. Solid waste generated in the wet
market included wastes such as green vegetables, cardboard
boxes, wooden packs, waste from meat, chicken, seafood and
etc. Based on an interview with Kajang Alam Flora Officers,
the daily amount of waste generated at this particular wet
market was around 8 tonnes. The respected wet market is
monitored by the Kajang Municipal Council (MPKJ) and the
contactor responsible for the waste collection is the Alam
Flora Company. There are two rorobins placed outside the
wet market which serves as a waste collection centre. Each
rorobin has a capacity of 4-5 tones. Alam Flora collects the
wastes from the rorobins at 10-11 a.m. daily. The wastes are
then sent to the Refuse Derived Fuel (RDF) station in
Semenyih to be sorted out for recycling and energy
production. The non-recyclable waste is sent to a landfill in
Sungai Kembong. Sludge from anaerobic digestion pond was
collected from Sri Ulu Langat Palm Oil Mill in Dengkil,
Selangor. The sludge is of high moisture content, low carbon
content and high nutrient value. It is usually dried to be used
as fertilizer. This study was carried out in the Laboratory of
Environmental
Engineering,
University
Kebangsaan
Malaysia. Solid wastes from market waste were combined
with dewatered palm oil mill sludge (POMS) from Sri Ulu
Langat Palm Oil Mill in Dengkil, Selangor with mixing ratios
of 1:2 using a rotator composter.
PHYSICAL CHARACTERISTICS OF RAW
MATERIALS
Parameters of raw palm oil mill sludge (POMS) and
solid waste (SW) are as shown in Table 1. The solid waste
was first collected from a wet market and then noncomposting materials such as metal, plastic and etc. were
removed. They were then chopped into smaller sizes and
placed in a small box of 35.5cm x 27cm x 30cm which was
later sent to the laboratory of Environmental Engineering,
University Kebangsaan Malaysia. The physical tests were
conducted on raw materials and solid waste before mixing it
with sludge .Sample sludge for anaerobic digestion was
collected from a pond in Sri Ulu Langat Palm Oil Mill in
Dengkil, Selangor. The sludge is of high moisture content,
low carbon content and high nutrient value. It is usually dried
to be used as fertilizer. The sludge was physically dried. The
initial parameters addition of solid waste with palm oil mil
sludge changed several parameters are given in Table 2.
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
137
TABLE 1
Physicochemical analyses of raw palm oil mill sludge (POMS) and Solid Waste (SW)
Parameters
POMS
Solid Waste
Recyclable compost
MC%
82.67
60.1
64.55
Wd kg/m3
950
510.48
536.24
pH
7.9
6.2
6.55
TOC%
33.48
45.16
41
P%
1.05
0.11
0.6
K%
2.05
1.237
1.43
C/N
8.65
34
13
TABLE 2
Parameters recorded for initial material mixture of ration 2:1 (POMS: SW)
Parameters
Initial material mixture
Ratio 2:1
Phosphorus (%)
1.14
Potassium (%)
0.42
pH
7.84
Nitrogen (%)
2.38
MC%
53.23
TOC%
46.23
C/N
32
ROTARY COMPOSTER DESIGN AND
DESCRIPTION USED IN THIS STUDY
Rotator composter reactor system as shown in Figure 1
consists of 3 main components. They are the rotator drum, air
compressor and gas absorber. Rotator drums are facilitated
with 3 phase motor. There are 8 inner blades with length of 5
cm each in order to enhance the mixing process in the reactor.
On the other hand, the air compressor functions to provide air
to the reactor. The gas absorber functions to absorb gas and
air resulting from the process inside the reactor. The absorber
used in this reactor is charcoal. The characteristic of each
components of the reactor is shown in Table 4. Mixing of
palm oil mill effluents sludge and solid waste were inserted
through the feeding part.
CHEMICAL AND PHYSICAL ANALYSIS
Sampling was done by taking 100g of compost from the
rotary drum composter. The temperature was measured using
138
a thermometer at the core of the reactor on days 0, 5, 10, 15,
20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95,
and 100. The pH value was determined using a method
described by [Carnes 1970] in which at least 10 grams sample
of the organic waste was poured into 500 ml distilled water
and stirred vigorously for 3 to 5 minutes. When the mixture
settles, the pH of the mixture was measured as Figure 3. The
determination of the total moisture content was done by
weighing a sample of waste and drying it in a conventional
oven at a temperature of 1050C for 24 hours and then the
sample is weighted again [Romeela and Mohee 2005] as
Figures 4 and 5. The percentage of moisture content was
calculated using the formula as shown below:
VOLATILE SOLIDS AND ASH CONTENT
Volatile solid has been widely accepted by composting
research as a mean to estimate the carbon fraction in compost
materials. The relationship between carbon fraction and
volatile solid content was first developed by New Zealand
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
FIGURE 1
Rotary composter reactor system
TABLE 3
Physicochemical analysis of final compost
Parameters
Final compost
Standards Values
Reference
MC%
50.32
40-65
( Rynk et al.,1992)
Wd kg/m3
480.12
500-600
( Goldstein , 2002)
pH′
7.02
5.5-9
( Rynk et al.,1992)
TOC%
40.21
30-48
(Navarro et al.,1990)
P%
1.27
> 0.5
( Nogueiraetal ., 1999)
K%
2.68
> 1.5
> 1.5 ( Nogueiraetal ., 1999)
C/N
19
19
20-41 ( Rynk et al.,1992)
engineers and published in their second interim report of the
inter departmental committee on Utilization of organic wastes
in 1951 and is shown as below:
% Carbon = 100- % Ash /1.8
= 1/1.8 (% Volatile Solids)
= 0.56 (% Volatile Solids)
Whereby,
100- % Ash (% Volatile Solids)
In another publication by World Health Organisation,
International Reference Centre for wastes disposal (1978),
carbon content was estimated by the following relationship
% Carbon = a x % volatile solids
Where a (refuse) = 0.42
A (compost) = 0.52
Undoubtedly, volatile solids are an indirect measure of
carbon fraction for composting materials. However, the
values of volatile solids will not be converted by the above
mentioned factor (i.e. 0.56 and 0.52) in this study. The dry
residue remained from the moisture content analysis was
heated to 500 ± 50 C0 in a muffle furnace for 2 hours. The
residue was allowed to cool down in a desecrator and
reweighted again. Volatile solids can be expressed as follows:
Volatile Solids = Initial Weight – Final Weight/ Initial Weight
X 100.
Non-volatile solid or ash content (in percentage) = 100 – VS
The volatile solids are often termed as the total organic
matter, but it is subjected to < 1% of error. This error is due to
the fact that organic fraction is not the only substance that
will be volatilized at 550 C0, but some of the inorganic
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
139
TABLE 4
Rotary composter reactor system specifications
Rotator Composter System
Material
Stainless steel
Length
3m
Diameter
0.6 m
Initial active volume
0.4 m3
Maximum rotation
2 rpm (rotation per minute)
Air Compressor
Model
SWAN DR 115
Ability
122 liter/min
Speed
1450 rpm (rotation per minute)
Cycle
50 Hz
Motor
Model
CHENTA, Taiwan
Volt
400 V
Current
9.1 A
Cycle
3.50 Hz
FIGURE 2
The flow of materials in the rotating drum reactor system
substance may get volatilized as well. (World Health
organization 1978).
FLOW CHART OF METHODOLOGY
RESULT AND DISCUSSION
pH
pH is an important parameter that can control the
depletion of nitrogen due to ammonia volatilization [Qiao, L.
140
& G. Ho. 1997]. A pH out of the 5-9 range would threaten any
biological activity. The pH level of the composting mass
typically varies with time during composting process. Along
the experimental period, solid waste and sludge were added
accordingly at the beginning in every batch. The initial pH
value is 8.01 and it was reached to levels as high as pH 8.83
when the process is in the second batch. pH was initially
recorded as 8.01 and then increased with time and reached a
peak of 8.83 around day 20 and this was followed by
reduction to 7.28 by the end of the process. Increase of pH
value at the beginning of the composting process maybe due
to the protein mineralization which affected to the increase in
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
FIGURE 3
Sample stirred
FIGURE 4
Mass of sample using digitalscales dumbing
ammonia generated by the biochemical reactions of nitrogencontaining materials and changing of amino acids and peptide
to the ammonia (Paredes et al. 2002; Crowford 1983; Liao et
al. 1994).Besides, the decreasing of pH values at the end of
the composting process agreed with the Sundberg et al.
(2004) that reported for fully developed composting, the pH
often rises to 8-9. In addition, the composting is dominated
by bacteria, which are generally not as acid tolerant. Figure 6
shows the graph of pH profile along the composting process.
Temperature Profile
Temperature is the most important indicator of the
efficiency of the composting process (Xiujin et al., 2008).
The palm oil mill sludge (POMS) used in this study contains
high level of organic matter, therefore when a highly organic
matter is added to a highly cellulosic material like solid waste
kitchen, waste heat is generated in the rotary composter as a
result of biodegradation (Miyatake & Iwabuchi. 2005). In this
study, the temperature reading taken at outlet section and the
initial temperature for experimental process measured was
26.23˚C. However, the temperature profile increased to
34.23˚C which reflects microbial decomposition activity in
the compost as shown in Figure 7. For 20 days, the
fermentation temperature was maintained around 26.2334.23°C indicating a thermophilic phase, on day 20 the
fermentation reached a maximum temperature of 34.23°C.
The temperature of rotary composter reduced gradually after
it reached the maturity phase on day 40 of the treatment. A
significant decrease in temperature to 20°C was noticed at the
end of the composting process, indicating microbial activities
had been reduced due to the depletion of biodegradable
substrates for their growth and survival. Most studies
reported that the optimum temperature range for effective
decomposition was 50-70˚C, with 60˚C being the most
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
141
FIGURE 5
Oven used for drying sample
FIGURE 6
Graph of pH versus Time
FIGURE 7
Temperature profile against time of composting
satisfactory level (Wong et al., 2001). However, for this
study, the temperature profile obtained was under the
optimum range for the sanitation and degradation process. In
order to increase the temperature of the compost process,
biodegradable carbon sources can be added. Stentiford (1996)
142
in his study stated that, maximum temperature of 55-65˚C is
necessary to destroy pathogens, but temperature of 45-55˚C
must be maintained for maximum degradation. Figure 3
shows the temperature profile for the experiment of
composting process.
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
FIGURE 8
Moisture Content MC% profile against time of composting
FIGURE 9
Change of VS/R versus VS and their respective regression factors for series
Moisture Content MC%
COMPOSTING KINETICS
The moisture content level was the critical factor that
determined the decomposition rate in composting. For this
study, sludge (POMES) was added onto the compost
materials solid waste (SW) according to a ratio of 2:1. The
reactor was injected with air by using an air compressor in
order to reduce the moisture level of the compost product.
The moisture content of the compost materials was too high,
with a range of 64-70% and it then decreased over time to
reach 55.32% at the end of the treatment as Figure 8.
Moisture content of more than 70% can cause leaching in the
composting process (Lau, H.L.N., Y.M. Choo, A.N. Ma &
C.H. Chuah. 2008).
Kinetics is the study of rates of reactions. Composting
kinetics in particular deals with the rate of the composting
process, that is, the velocity at which biodegradable matter in
the composting materials is consumed. (Waksman et al.,
1939). The data was manipulated and best fitted based on the
procedure of Michaelis Menten model ( 1980).
CX* = CX / K1 ( 1 )
K1 = K1 + K2 / K1 ( 2 )
R= ( K2 C ) 1/ K1 + C ( 3)
Where
R = Consumption rate of substrate or reaction rate.
CX* = Activated substrate –organism complex.
C = Substrate , carbon.
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
143
X= Free organisms
K1 = System constant of composting system.
K2= System variable of composting system.
The data for experiment carried out at 34.230C was fitted by
an exponential function. The exponential functions were
derived. These functions and the corresponding regression
factors are included in Table 5 as exponential functions
describing the variation of volatile solids and their respective
regression factors.
solids were derived. The differential equation of the best –fit
exponential function is written as below:
Series
R1 =dy / dx= -22.0104 X-1.2380 (7)
R2=dy/dx= -18.4865 X-1.2133 (8)
R3=dy/dx = -19.5924 X-1.2273 (9)
For equation R1 = y = 87.867 X t.p.o - 0.251
By differentiating this equation
Best fit equation (volatile solid VS with time) in power
function for experimental results
R1 = y = 87.867 X t.p.o - 0.251 ( 4)
R2 = y= 85.437 X t.p.o -0.2123 (5)
R3 = y= 86.179 X t.p.o - 0.2268 (6)
Where
R = Consumption rate of substrate or reaction rate.
VS = Volatile solids.
By using Minitab Program
Scatterplot of VS/R vs. VS
The exponential functions were differentiated and the
differential equation denoting the rate of change of volatile
TABLE 5
The change of volatile solids for experimental model
Series
Exponential Function
Regression Factor
R1
Y= 87.867 X-0.251
0.8021
R2
Y= 85.437 X-0.2123
0.713
R3
Y= 86.179 X-0.2268
0.7612
TABLE 6
The changes of volatile solids (%Wt) over time for all the three series ( R 1, R2 and R3)
Analysis
144
The changes of volatile solids over time for all the three series
Sample/Day
R1
R2
R3
0
78.1252
79.123
77.11
1
70.7542
70.6926
71.69
2
72.1213
73.71
64.91
3
68.762
66.074
60.482
4
64.5354
61.6201
62.1300
5
56.9174
63.59
66.6052
6
54.137
63.31
50.1622
7
50.326
58.165
46.028
8
52.0722
43.0711
48.749
9
42.7222
62.041
43.1213
10
41.7414
51.63
46.042
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
TABLE 7
Data Analysis for series 1 ( R1)
Time , day
VS
R
VS/R
0
79.12
22.0079
3.59
1
70.89
8.796
8.059
2
72.76
5.567
13.069
3
67.79
3.69
18.371
4
64.67
2.846
22.723
5
58.71
2.437
24.09
6
55.68
1.826
30.492
7
51.33
1.547
33.1803
8
54.072
1.511
35.7855
9
42.876
1.2304
34.847
10
43.86
1.41
31.106
TABLE 8
Intercept , Slope , K2 and K1 for series R1
Samples for series
Intercept
Slope
Regression Factor, RSq
K2
K1
59.5
-0.642
94.3%
1.0301
81.6406
R1
Regression Analysis: VS/R versus VS
The regression equation is
y = 59.5 - 0.642 x
Predictor CoefSECoef T P
Constant 59.463 4.642 12.81 0.000
x -0.64201 0.07893 -8.13 0.001
S = 2.06549 R-Sq = 94.3% R-Sq(adj) = 92.9%
Analysis of Variance
Source DF SS MS F P
Regression 1 282.27 282.27 66.16 0.001
Residual Error 4 17.06 4.27
Total 5 299.33
The value of volatiles solids (VS) and absolute value of
the rate of change (R) are calculated and manipulated in such
a way that the relationship between volatile solids (VS) can
be graphically described. The relationships were best–fitted
by linear equation as shown in figure. The linear equations
and their respective regression factors are as shown in Table
9.
From the value of regression factor for both the
exponential function and linear equation, series R1 has the
best fit. Corresponding data’s, the exponential model
generated in series R1 was opted as the kinetic model for
composting process. This model was compared to another set
of verification data to verify its appropriateness in describing
the actual composting process. The intercepts and slopes of
the linear equations were computed to give the value of K1
and K2. The K2 value was calculated by inverting the value of
slope, and K1 value was calculated by multiplying the value
of K2 and intercept. The value of slope intercept, K1, and K2
are summarized in Table 10.
K1 value is in the range of 80-83 whereas the K2 value is
within the range of 0.9-1.1. The mean for K1 and K2 are
81.3667 and 0.960 respectively.
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
145
TABLE 9
Linear equation of VS/R versus VS and their respective regression factors
Series
Linear Equation
Regression Factor
R1
Y= -0.642 x+59.5
0.943
R2
Y= -0.895 X + 87.2
0.686
R3
Y= -1.02 X + 86.8
0.672
TABLE 10
Value of Slope , Intercept, K1 and K2
Series
Slope
Intercept
K1
K2
R1
-0.642
59.5
81.6406
1.0301
R2
-0.895
87.2
82.67
0.867
R3
-1.02
86.8
80.6013
0.931
81.637
0.9427
Mean
TABLE 11
The comparison is shown in this table comparison of K1 and K2 value with other study
K1
Whang and Meenghan
K2
1.0623
1.2243
1.1362
0.0113
0.0325
0.0292
81.6406
82.67
80.6013
1.0301
0.867
0.931
(1980)
Experimental Results
The results show that the percentage error for K2 ranges
from 3.38 to 9.36 with an average of about 6.24. The
maximum percentage error for K1 is only 1.98 and the low
side reading for percentage error is 0.93. The percentage error
for K1 is 1.32 %, whereas the percentage error for K2 is 6.24
%. This shows that K2 is relatively more varied than K1. This
is consistent with the Michaelis-Menten model, whereby K1
is a dissociation constant that should be constant in a given
system. On the other hand, K2 is a system variable dependent
to microbial population in the composting materials. These
constants were also compared to the constant obtained by
Whang and Meenghan (1980)
Both the K1 and K2 for these two studies do not agree
well with each other, due to difference in the experimental
design for this study. Whang and Meenaghan (1980)
employed carbon fraction as their indicating parameter and
146
was expressed in weight of carbon (gram) per weight of ash
(gram), which eventually resulted in a dimensionless
indicator (g/g). However, the indicating parameters for this
study were volatile solids and were expressed in percentage
of volatile solids per total dry sample weight.
Besides
that, the K1value is corresponding to the initial concentration
of the indicating parameter. Therefore the magnitude of K1 for
this study are in two digits (expressed in percentage), while
the magnitude for K1 for Whang and Meenaghan (1980) was
only in one digit (expressed in fraction).
CONCLUSIONS
All in all, it can be concluded from this study that the
composting process is an option that has a talent in stabilising
JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT
VOLUME 40, NO. 2
MAY 2014
and re-using waste for agricultural purposes. Solid waste
(SW) to palm oil mill sludge (POMS) mixing ratio of 1:2 is
more likeable than 1:3 because it contains more essential
nutrients for plant growth, such as N and C, and for building
soil organic matter content. The composting process kinetic
model developed for this project establishes the mathematical
expression
Y = 87.867 X-0.251
K1 value obtained from this study is 81.64, R2 is 0.943 and K2
is 1.0301. These values are acceptable for homogenized
carbon hydrate – based materials. In summary, the above
result proves that co-composting of solid waste with palm oil
mill sludge (POMS) can be used as an alternative method for
improving treatment of wastes and optimize kinetics by using
an economical and simple system,
ACKNOWLEDGMENT
The authors would like to thank Prof. Rakmi Abd
Rahman for the financial and technical support without which
this research could not be conducted and completed. We
would also like to thank the Majlis Perbandaran Kajang
(MPKJ) for allowing us to collect waste from the wet market
at Bandar Baru Bangi.
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
Anon. 2006.Ninth Malaysia Plan Sembilan. Bab 2 Promoting Environmental Care: pp. 481-482.
Astimar, A.A. & M.B. Wahid. 2006. Supply outlook of
oil palm biomass in Malaysia. Proceeding of the Seminar
on Ecomat Research and Promotion, 24-25 July: 13-26.
Baharuddin, A.S., M. Wakisaka, Y. Shirai, S. Abd-Aziz,
N.A. Abdul Rahman & M.A. Hassan 2009. “Cocomposting of Empty Fruit Bunch and Partially Treated
Palm Oil Mill Effluent in
Pilot Scale.” Intern. J.
Agric. Res. Volume 4: pp. 69-78.
Carnes, R.A.. 1970. “An Investigation of the pH
Characteristics of Compost.” Compos Science, Sept-Oct.
F. Zucconi, M. de Bertoldi,” Compost specifications for
the production and characterization of compost from
municipal solid waste.” In: deBertoldi, M., Ferranti,
M.P., L’Hermite, P., Zucconi, F. (Eds.), Compost:
Production, Quality and
Use. Elsevier, Barking,
pp. 30–50, 1987. Harilingen, Texas.
Gilmour, J.T., M.D. Clark, D. Mark and S.M. Daniel
1996. “Predicting long-term decomposition of biosolids
with a seven–day test.” Journal of Environmental
Quality, Volume 25(4): pp. 766-770.
Haug, R.T., 1993. The Practical Handbook of Compost
Engineering. Lewis Publishers, Boca Raton, Florida,
USA.
Haug, R.T. 1993.The practical Handbook of Compost
Engineering. Lewis publishers. Boca Raton. p. 717.
9.
Lau, H.L.N., Y.M. Choo, A.N. Ma & C.H. Chuah. 2008.
Journal of Food Engineering Selective extraction.
10. Lens, P., Bert Hamelers, H. Hoitink, & W. Bidlingmaier,
2004. “Resource Recovery and Reuse in Organic matrix
Ofpalm carotene and vitamin E from fresh palm-press
mesocarp fiber (Elaeisguineensis) using Supercritical
CO2.” Volume 84: pp. 289 -296.
11. Ministry of Environment (MOE). 1999. Korea
Government Report on Environment. Government
Printing Registered Number 11-1380000-000156-10,
Korea.
12. Miyatake, F. & K. Iwabuchi. 2005. “Effect of high
temperature on enzymatic activity and species diversity
of culturable bacteria in cattle manure compost.”
Bioresource Technology.,Volume 96: pp. 1821-1825
DOI: 10.1016/j.biortech.2005.01.005.
13. New Zealand engineering (1951) – “Second Interim
Report of the inter departmental
Committee on
Utilization of organic waste.” New Zealand Engineering.
Volume 6: pp. 11-12.
14. Pressel .F., and W. Bidlingmaier 1981. “Analyzing decay
rate of compost.” Biocycle. Volume 22(5): pp. 50-51.
15. Qiao, L. & G. Ho. 1997. “The effects of clay amendment
on composting of digested sludge.” Water Res., Volume
31: pp. 1054-1056.
16. Braun, R., E. Brachtl, M. Grasmug, “Codigestion of
protein aceous industrial waste,” 2003, Applied
Biochemistry and Biotechnology, Volume 109: pp. 139153.
17. Romeela, Mohee. &Ackmez Mudhoo. 2005. Analysis of
the physical properties of an in-vessel composting
Seminar on Ecomat Research and Promotion, 24-25 July:
13- 26. Solid Waste Management. United Kingdom:
IWA Publishing.
18. Surender, A. & A. Reddy, 2007. Composting feasibility
study and conceptual planning for the city of Tiquia,
S.M.N.F.Y. Tam and I.J. Hodgkiss (1996). Microbial
activities during composting of spent pig-manure
sawdust litter at different moisture content. Bioresource
Technology. Volume 55: pp. 201-206.
19. Thambirajah, J.J., M.D. Zulkifli, and M.A. Hashim,
1995. “Microbiology and biochemical changes during
the composting of oil palm empty fruit bunches. Effect of
nitrogen supplementation on the substrate.” Bioresour.
Technol. Volume 52: pp. 133-144.
20. Unit Operations in Resource Recovery Engineering
Prentice-Hall, Inc. V. Miikki, N. Senesi, K. Hanninen,
“Characterization of humicmaterial formed by
composting of domestic and industrial biowastes. Part
2.Spectroscopic evaluation
of humic acid structures,”
1997, Chemosphere, Volume 34, pp. 1639–1651.
21. Whang, D.S. and G.F. Meenaghan 1980. “Kinetic model
composting process compost Science/ Land Vtilization”
Volume 21 (3): pp. 44-46.
22. World Health organization (1978). Methods of Analysis
of sludge solid waste and compost. World Health
organization International Reference Centre for waste
disposal CH-8600 Dubendorf Switzerland.
DETERMINATION OF KINETICS FOR CO-COMPOSTING OF ORGANIC FRACTION OF MUNICIPAL SOLID WASTE
147