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Taraksh Journal of Physical Education / Page No. 8 / Volume 1 Issue 1
Design a Computational Model for Solid Waste
Management in Metropolitan Area
Vijay Tiwari1, Pradeep Kumar Sahu2, Rakesh Kumar Sahu3
Department of Mechanical Engineering,
Parthivi College of Engineering & Management, Bhilai-3, C.G., India.
1
[email protected]
[email protected]
3
[email protected]
2
Abstract- This paper reviews the past and present research in the
area of solid waste management (SWM). In this paper various
developed techniques used for SWM to cope with its
environment. A model for solid waste management is described
as a network flow problem and a special purpose algorithm is
developed and designed. The model is applied to waste
management and facility sitting decisions in the Metropolitan
area.
Keywords— solid waste management, network flow problems,
metropolitan area planning.
I. INTRODUCTION
Solid waste management today is made difficult and costly
by the increasing volumes of waste produced. The need to
control - what are now recognized as potential serious
environmental and health effects of disposal. The lack of land
in urban areas for disposal purposes. Waste management.
Once strictly a local and private sector matter, now involves
regional, state and federal authorities [1]. The two primary
reasons to have solid waste management (1) economies of
scale' as declining average cost as scale increases and (2)
empirically establish the existence of economies of scale for
transfer station, incineration and landfill operation. [2].
Some of the cities have no adequate place to landfill
disposal of their so they waste time and money for
transporting and disposal of solid waste management. With
increasing emphasis on resource recovery and the scarcity of
land for disposal in recent years, many cities are turning
towards incineration with power or steam generation, recovery
of useful commodities from the waste (recycling), utilization
of waste as a substitute for fuel in power plants[3].The
algorithm presented here being a modification of that of
Marks [ 5-7].
The type of models developed here and their
application to a specific regional waste management system in
the Chhattisgarh should not be misjudged as an ultimate
decision maker, but, rather, be used as an effective tool for
efficient management.
© 2014 Taraksh. All Rights Reserved
Fig. 1.1 MSW disposal methods practiced in study countries [8]
Looking at the most common disposal methods in the study
countries indicate the share of open dumping to be 90% in
India, 85% in Sri Lanka, 65% in Thailand and 50% in China.
II. LITERATURE REVIEW
Ojha et al. [8] have discussed the problem of solid waste
management in developing countries and mountains create
serious threats to both flora and fauna. They have developed
plasma arc gasification, in which pyrolysis of solid waste
takes place at very high temperature thus ensuring syngases as
the output of gasifier [8]. Aprila et al. [9] have discussed the
use of household wastage followed by recyclable inorganic
wastes such as plastic, paper and card board which will then
be disposed at the landfill and improve the toxic of household
waste[9]. Acosta et al.[10] have developed The Strategy
consists of ten (10) components, namely: Bridging policy gaps
and harmonizing policies, Capacity development, social
marketing and advocacy, Sustainable financing, Creating
economic opportunities, Knowledge management on
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Taraksh Journal of Physical Education / Page No. 9 / Volume 1 Issue 1
technologies and innovation for solid waste management [10].
Altuncu et al.[11] have presented the Control and
management of solid waste materials created during/after
building/demolition and renewal of a structure is one of the
most important problems of modern societies today[11]. They
have presented, exploring and evaluating qualitative data
obtained from two case studies and framing the evidence
through five strands of themes integral to facilitate social
learning to fill the gap [12]. Omar et al. [13] have presented a
cross-sectional comparative study carried out to determine the
variations and similarities in the activities of clinical waste
management practices within three district hospitals located in
Johor, Perakand Kelantan [13]. Gottinger has developed a
case study with a computational model for metropolitan area
for west Germany [14].
Subject to:
(2)
(3)
(4)
(5)
Problem 2 :
III. PROBLEM STATEMENTS AND MATHEMATICAL
MODELS
The general problem of the static model of the waste
management system can be described as follows: given the
potential locations of intermediate processing facilities and
landfills, the locations and capacities of existing facilities, the
cost structures--transportation, processing and fixed costs--and
the quantities of waste generated at the sources, determine
which facilities should be built and how the wastes should be
routed, processed and disposed of, so that the overall cost of
the system is minimized. A mathematical formulation of the
problem is given below. The aim of this paper to minimize the
transportation cost and improves annual revenue of plant.
(6)
Subject to:
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
IV.METHOODLOGY
Fig.1 Model of Fuzzy logic controller for transportation cost
Problem1:
(1)
© 2014 Taraksh. All Rights Reserved
The term fuzzy logic has been used in two different senses. It
is thus important to clarify the distinctions between these two
different usages of the term. In other words fuzzy logic
represent classical two valued logic for uncertainty .fuzzy
logic refers to all of the theories and technologies that employ
fuzzy sets, which are classes with unsharp boundaries.
In those situations, fuzzy set theory, introduced by
Zadeh in 1965[15], can be an effective means of dealing with
such linguistically specified objectives. Linguistic terms such
as small and large may be defined as fuzzy sets. A fuzzy set is
characterized by a membership function that assigns to each
object in a given class a grade of membership ranging
between zero class a grade of membership ranging between
zero and one. Therefore empirical or heuristic knowledge may
be used as a basis for logical inference. Moreover, linguistic
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Taraksh Journal of Physical Education / Page No. 10 / Volume 1 Issue 1
rules may be used for specification of control rules may be
used for specification of control laws in control problems [16].
1. Fuzzy logic controller (FLC) for SWM
In the proposed fuzzy logic controller consists of five major
units, a fuzzifier which convert the crisp set into fuzzy set,
an inference engine, which performs if then rules, a data
base, which provides the inference engine with the
membership functions of fuzzy sets used in rule base, a rule
base, which provides the inference engine with the control
rules and a defuzzifier, which changes the outcome of
fuzzy inference from fuzzy set into crisp set i.e. real value.
The desired transportation cost and actual transportation
cost has been taken as input of the desired FLC which has
been observed in the form of numerical value. Then this
value has been converted into fuzzy data/value by taking a
suitable member. Triangular membership function has been
taken for the conversion of numeric data into fuzzy data
due to its simplicity and area as well as centroid of the
triangle can be calculated easily .Three triangle has been
used to design FLC and the range of the triangular member
has been taken as Negative (N), Zero (Z), and Positive
(P).Now the design of fuzzy controller triangular
membership function has chosen for easy calculation such
as area and cancroids. Here two input and one output signal
error has taken such as actual transportation cost and actual
transportation cost and three membership functions
negative, zero and positive. A set of rule is designed by
using table 1.The „IF‟ part has been taken as desired and
actual joint velocity and „THEN‟ part is used for the zero
signal error. The rule base is form by the perception of the
zero error.
error is -0.8
Rule2: If Actual transportation cost is 1.8 and desired
transportation cost is -0.8 Then
error is -2.6
Rule3: If Actual transportation cost is -1.6 and desired
transportation cost is -0.2 Then
error is -1.2
Defuzzification: To get numeric value/crisp set the
following center of Area method has been used as:
COA ( A ) =
y =
(16)
Where
Y=output value after defuzzified
Table I
Rules of transportation cost error
Where
As per the above equation rule can be formed as follows for
error output as:
First sampling
Rule1: If Actual transportation cost is -1.4 and
desired transportation cost is -0.2 Then
error is 1.2
Rule2: If Actual transportation cost is -1.2 and desired
transportation cost is -0.6 Then
error is -1.8
Rule3: If Actual transportation cost is 1.6 and desired
transportation cost is -0.4 Then
error is -2
Second sampling
Rule1: If Actual transportation cost is 1.2 and desired
transportation cost is 0.4 Then
© 2014 Taraksh. All Rights Reserved
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Taraksh Journal of Physical Education / Page No. 11 / Volume 1 Issue 1
V. RESULT AND DISCUSSION
The sensor sense the input values and sends to the designed
triangular member where these numeric inputs are fuzzified
and then these fuzzy sets are inferred by the rule base i.e. IfThen rules. These rule base inferred by the inference engine
where and/or operation is imposed. Finally the output fuzzy
function is defuzzified and again fuzzy sets are converted into
crisp sets where signal error is obtained. Fig. 5.1 shows the
input actual transportation cost signal where range has taken
between -2 to 2 and fig.5.2 shows the range of desired
transportation cost signal where range has taken between -1 to
1 similarly fig.5.3 shows output transportation error where
range has taken between -3 to 3.Fig.5.4 shows the rule viewer
of error where get different –different surface view change the
range of inputs.fig5.5 shows the surface view of output
transportation error and fig 5.6 The results shows the efficient
and minimum error of transport and blue lines shows the
authors result and green line shows our result.
Fig. 5.4 Rule viewer transportation cost of solid waste
management
Fig.5.1 FIS diagram for actual transportation Cost
Fig. 5.5 Surface view for transportation cost
Fig.5.2 FIS diagram for desired transportation cost
Fig. 5.6 shows the graph between transportation cost and time
Fig.5.3 FIS diagram for transportation error
© 2014 Taraksh. All Rights Reserved
In this paper we represent the application of fuzzy logic
control theory elements with FIS diagram and generate
surface view of transportation cost with minimum error and
fig. 5.6 shows the graph between transportation cost and time
for solid waste management. Fig. 5.7 shows the result of
authors and fig. 5.8 shows the result of ours data’s are
obtained from BALDEV POWER PLANT, Raipur.
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Taraksh Journal of Physical Education / Page No. 12 / Volume 1 Issue 1
TableII
Results of annual revenue
ANNUAL
ECONOMICS[ours]
Revenue
Electricity Producttion
$61,240,000
Tipping fees
$9,187,500
Recycling Sales
$654,230
Salg Sales
$300,000
COMPARISON OF
Carbon monoxide(CO)
POLLUTION
PERFORMANCE
MSW IGCC
MODELLED
[ours]
MSW
COMBUSTION
PERMIT LIMIT
45
150PPM
Sulphur / HCL sales
$1,550
Sulphur Oxides( SOx)
12
30 PPM or 80%
Sulphur removed
whichever is less
stringer
Total
$71,383,280
Nitrogen Oxides
(NOx)
30
180 PPM-Ist Yr
150 PPM after Ist Yr
Particulate matter
8.2
24 Mg/Nm3(day) and
<10 opacity
(6-minute average)
Dioxin and Furan
EXPENSES
0.01
30 mg/Nm3(day)
Operating Expenses
$9,528,330
Hydrogen chloride
(HCL)
<1
25ppm reduction
whichever is less
stringent
Debt Payment
$12,407,225
Mercury (Hg)
0.0004
0.08 mg/Nm3
Taxes
$0
Lead (Pb)
0.006
.2 mg /Nm3 (day)
Total
$219,355,555
Cadmium(Cd)
0.003
0.02mg/Nm3
ANNUAL CASH FLOW
$49,447,725
© 2014 Taraksh. All Rights Reserved
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Taraksh Journal of Physical Education / Page No. 13 / Volume 1 Issue 1
Table III
Results of ours
VII.CONCLUSIONS
VIII. REFERENCES
On the basis of theoretical and simulation analysis it can be
concluded that the developed methodology is suitable to
implement into solid waste management for transportation
error. A rule base is proposed to minimize the transportation
cost. The design of fuzzy logic controller for transportation
cost and it verifies the mathematical equation for SWM
modeling. The model can be interpreted as a technology
choice model in which various waste treatment technologies,
in combination with transfer stations and land filling, can be
substituted for each other to provide an overall optimal
solution. Further We can optimize transportation cost for
annual revenue.
[1] Clark, R. M. and Gillean, J. I., “Analysis of solid waste
management in Cleveland. Ohio: a case study”, Interfaces, 6,
32-42,(1975).
© 2014 Taraksh. All Rights Reserved
[2] Dee, N. and Liehman, J. C. ,”Optimal location of public
facilities', Nay. Res. Logist. Q. “,19,753-759,(1972).
[3] Kemper, P. and Quigley, J. M. ,“The economics of refuse
collection”, Ballinger, Cambridge, Massachusetts, (1976)
[4] Liebman, J. "Models of solid waste management', Chapter
5 in 'Mathematical models in government planning', (S. Goss,
Ed.), Princeton. New Jersey, 139-164,(1975).
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Taraksh Journal of Physical Education / Page No. 14 / Volume 1 Issue 1
[5] Marks, D. H. and Liebman, J. C., “Mathematical analysis
of solid waste collections”, Public Health Service PubL No.
2065, MS Dept. of Health, Education and Welfare, (1970).
[6] OECD “Economic instruments
management”, Paris ,(1981).
of
solid
waste
[7] Walker, W., Aquiline, M. and Schur, D. "Development
and use of a fixed charge programming model for regional
solid waste planning”. Rand Corp. Paper, p. 5307, Santa
Monica, California,(1974).
[8] A. Ojha, A. C.t Reuben, D. Sharma ,”Solid Waste
Management in Developing Countries through Plasma Arc
Gasification- An Alternative Approach”, APCBEE Procedia,
1193 – 198, ( 2012 ) .
[9] A. Aprilia, T. Tezuka, G. Spaargaren, “Inorganic and
hazardous solid waste management: Current status and
challenges for Indonesia”, Procedia Environmental Sciences
17, 640 – 647,(2013).
[10] V. Acosta, J. Paua, C. Lao, E. Aguinaldo, ”Maria Delia
Cristina Valdezb Development of the Philippines National
Solid Waste Management Strategy 2012-2016 “,The 7th
International Conference on Waste Management and
© 2014 Taraksh. All Rights Reserved
Technology, Procedia Environmental Sciences, 16 ,9 –
16,(2012).
[11] D. Altuncu, M.A. Kasapseçkin, “Management and
recycling of constructional solid wastein Turkey”,
201International Conference on Green Buildings and
Sustainable Cities, Procedia Engineering ,21, 1072 –
1077,(2011).
[12] S. M. Kamaruddin, E. Pawson, S. Kingham,” Facilitating
Social Learning in Sustainable Waste Management: Case
study of NGOs involvement in selangor,Malaysia”, Procedia Social and Behavioral Sciences, 105,325 – 332,(2013).
[13] D. Omar, S. N. Nazli, S. A/L Karuppannan, “Clinical
Waste Management in District Hospitals of Tumpat, Batu
Pahat and Taiping” ,Procedia - Social and Behavioral
Sciences, 68 ,134 – 145,(2012).
[14] H.W. Gottinger,” A computational model for Solid waste
management
with
applications”,
Applied
math
modelling,10,330-338,(1986).
[15] L.A Zadeh,” fuzzy sets,” informs. Control, 1.8, 3,338353, (1965).
[16] L.J. Huang and M. Tomizuka, A self-paced fuzzy
tracking controller for two- dimensional motion control, IEEE
Transaction on systems, Man and cybernetics, 20, 5,(1990).
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