Table of Contents 1.0 BACKGROUND ................................................................................................................................. 1

1.1 Problem definition and Importance of Study .................................................................................. 3

1.2 Aim of Study ................................................................................................................................... 3

1.3 Hypothesis ....................................................................................................................................... 4

1.4 Objectives of Study ......................................................................................................................... 4

1.5 Research Questions.......................................................................................................................... 4

2.0 LITERATURE REVIEW .................................................................................................................... 5

2.1 Available gas estimation Models ..................................................................................................... 5

2.1.1 Zero Order Model ......................................................................................................................... 6

2.1.2 First Order Model ......................................................................................................................... 6

3.0 METHODOLOGY .............................................................................................................................. 6

3.1 Structure of Dissertation .................................................................................................................. 7

3.2 Dissertation Time Line .................................................................................................................... 7

BIBLIOGRAPHY ..................................................................................................................................... A

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1.0 BACKGROUND

Zambia’s population is growing rapidly at an average rate of 2.7 percent per annum. The country’s total

population is projected to grow from 13.7 million recorded in 2011 to 17.9 million in 2020 and further

rise to 26.9 million by 2035 (Central Statistics Office [CSO], 2013). The urbanization rate is gradually

increasing and in the next 25 years, the percent of the total population living in urban areas will rise

from 40.6 percent recorded in 2011 to 46.1 percent by 2035 (Central Statistics Office [CSO], 2013).

Urbanization and population increase have inflicted pressure on available resources and services such

as municipal solid waste management services, increased demand for health care, water supply and

electricity. The growth rate of electricity demand has been estimated at 5.7 percent per annum until the

year 2020 and 4.4 percent per annum between 2020 and 2030 (Department of Energy [DoE], 2010).

Exclusively the demand for electricity in Zambia’s capital, Lusaka, has been increasing at a rate of 10

percent per annum since 1994 with an overall increase of over 100 percent between 1994 to 2004 (LCC

and ECZ, 2008, p. 3).

With the current levels of industrialization and urbanization, there is need to explore the different

renewable energy options in order to meet the current and future energy needs of the country in a

sustainable, environmentally friendly and cost effective manner. Deployment of biomass for electrical

energy production is one of the available options that can be explored since biomass is widely available

in the country including; industrial/municipal organic wastes, agricultural waste, forestry waste, energy

crops and products and animal waste (Department of Energy [DoE], 2010). In most urban centres in

Zambia, tonnes of waste are produced each year with the majority coming from agricultural, mining

and domestic industries. Approximately 15% of this waste is produced in Lusaka alone and disposed

off by dumping or incineration as these are the most prominent waste disposal methods in Zambia

(Auditor General, 2010). Municipal Solid Waste (MSW) at a global level has become increasingly

acknowledged as an essential negative contributor to the local environment and human health. A

typical MSW content is assumed to include all wastes that are generated in a community with the

exception of industrial wastes and agricultural solid wastes (Tchobanoglous, et al., 1993, p. 40).

Managing high quantities of wastes from multiple sources is a challenge in developing countries, where

20% to 50% of the available budget for municipalities is spent on solid waste management (Scarlet, et

al., 2015, p. 1270). Numerous suggestions have been made on methods of managing MSW from

simple methods such as dumping to more complex solutions such as sending waste into space.

Overtime different waste management methods have been applied and only a few solutions remain

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viable, including landfilling, incineration and recycling. These solutions to waste management are

being utilized to different extents.

Chen et al (2003) presented that sanitary landfilling is the common method for the disposal of solid

waste and Kamalan (2009) recognized it as an imperative source of methane gas which is the major

element of greenhouse gases. Likewise, landfilling is the most favorable solution worldwide and is the

broadly used waste disposal alternative owing to its economic advantages (Amini, 2011and Surroop &

Mohee, 2011). However, landfills continue to be key distresses for environmental regulating and

protecting organizations due to their impending probability to generate odours, leachate, and landfill

gas (Amini, 2011).

Landfills are significant in this context as Landfill Gas (LFG) is emitted from decomposing organic

wastes. LFG is produced in landfills through anaerobic degradation of organic matter and is comprised

of roughly 50 % methane (CH4) and 50 % carbon dioxide (CO2) (Willumsen, 1990). The fact that

methane and carbon dioxide are two major greenhouse gases (GHGs) with Global Warming Potential

(GWP) enhances the importance of studying LFG. On a mass basis, methane gas has 21 times the

global warming potential as compared to carbon dioxide over a 100 year time frame (Shariatmadari, et

al., 2003). On this basis, regulatory bodies have been formed worldwide to manage, estimate and

reduce the landfill methane gas such as the Kyoto Protol and Protocol on Pollutants Release and

Transfer Registers (PRTRs) (also known as Kiev Protocol) (Scharff & Jacobs, 2006). In addition, in

2006, Sabour and Kamalan (2006) it is established that methane gas has a great amount of energy and

encourages scientists and decision makers to estimate and turn the liability into an asset. In addition,

while being a threat to the environment as the major air pollutant, LFG if managed correctly is a

valuable energy resource, with an energy value of 18-22 Mega Joules per cubic meter (MJm-3) due to

the methane content (Spokas, et al., 2006).

Above mentioned matters have commanded the development and upgrading of landfill gas estimation

models. Monod Equation and the First Order decay equations have been used to develop models such

as, EPER, IPCC, TNO, LandGEM, Gassim, LFGREEN and Afvalzorg. Halvadakis model predicts

methane gas from landfills based on the sequential biological growth (Nastev, 1998). Ozakaya et al

(2006) and Sharitmadari et al (2007) have presented the weighted residual and neural network

numerical models as newly developed landfill gas estimation models.

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Engineers, businessmen, scientists and entrepreneurs are under international and global pressure to

reduce the greenhouse gas emissions in an attempt to curb the effects of global warming by seeking

feasible and innovative solutions that will not only solve the problem of gaseous emissions but provide

a form of energy that is worthy of their investment.

1.1 Problem definition and Importance of Study

Landfills produce methane gas which is released to the atmosphere and possess as a global warming

potential, if not correctly recovered for subsequent utilization. The traces of the gas in the atmosphere

and around the landfill premises may result in odour, nuisance, explosive danger and health hazards to

the environment. In Zambia there is currently no Landfill Gas to Energy (LFGTE) project or

utilization of LFG and little information is available on the potential of electricity generation from the

gas. Therefore, an estimation of the quantity of gas emissions at Chunga Lanfill and the electrical

energy potential in terms of Kilowatts (KW) is necessary in order to environmentally benefit the city by

reduction of gas emissions and economically benefit the residents from increased power supply.

Carbon credit trading markets have recently been rising and trading platforms in the United States,

Europe, India and China have been created. Trading in carbon emissions has provided financial

benefits for LFGTE projects and trends suggest that collecting LFG can has a major economic benefit

for Landfill owners. In this regard, landfill owners and operators can benefit from every ton of

emissions that is captured and used to create another form of energy or flare the dangerous emissions.

The study of landfill gas estimation models is essential in reducing GHG emissions and creating

alternative resource energy by quantification the probable amount of electrical energy that can be

produced from the landfill.

1.2 Aim of Study

The aim of this study is to investigate the energy potential of Chunga landfill by applying appropriate

theoretical gas estimation models based on the site conditions. The study also estimates the

environmental impact from Chunga landfill with respect to methane emissions, although this is not the

focus of this study.

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1.3 Hypothesis

a. The first order decay equation and subsequent landfill gas estimation models will be the most

applicable to Chunga landfill with regards to available waste data and site conditions.

b. The methane gas recovery and conversion to energy from Chunga landfill is worth considering

as a worthy investment with the ability of supplying power to over 500 households in Lusaka

city and is more beneficial as compared to the cost of installation and operations.

1.4 Objectives of Study

a. To review theoretical gas emission models and evaluate which models are highly applicable to

Chunga landfill gas estimation based on site conditions and available waste data.

b. To estimate the quantity of landfill gas production and electrical energy that can be produced

from the landfill.

c. To investigate costs and benefits of emitting against collecting landfill gas emissions with

regards to operation strategies and regulations.

d. To estimate the potential greenhouse gas reduction from then landfill.

1.5 Research Questions

a. Describe the prominent gas emissions models currently used in estimating landfill gas and give

the limitations with regards to certainty of the results.

b. Are there any necessary changes to be made to the models for application to Chunga landfill

based on site conditions and available waste data.

c. What parameters can be manipulated, removed or introduced to the theoretical models to reduce

errors in quantifying gas emissions from Chunga landfill.

d. What is the cost of construction and operations against the rate of return on the investment of

methane recovery plant and electrical energy production.

e. What are the environmental impacts of gas emissions from Chunga landfill and estimate the

potential of greenhouse gas reduction if a gas recovery plant is built.

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2.0 LITERATURE REVIEW

2.1 Available gas estimation Models

According to Kamalan, et al (2011 p. 80) three approaches are used for the mathematical presentation

of the gas production rate: (1) a simple empirical function represents the gas production rate or the gas

production rate is given as a combination of functions of an overall kinetic parameter, (2) gas

production rate is given as a complex of mathematical functions representing the individual kinetics of

the considered physioco-chemical processes occuring during refuse biodegradation and (3) numerical

models which interpret gas production in digits.

The overall kinetic parameter works with some models and it is the most common type of models

encountered in literature (Chereminoff & Morresi, 1976; Findikakis & Leckie, 1979; Hartz & Ham,

1982) (EMCON Associates, 1982; Gardner & Probert, 1993; Van Heut, 1986). The derivation of these

relatively simple models is a theoretical one and based on the the general kinetic expression for the

biodegradation process known as Monod’s equation (Kamalan & Sabour, 2011). It is named after

Jacques Monod, a scientist who related the microbial growth rates in an acqueous environment to the

concentration of the limititng nutrient and has the mathematical form given by equation below.

Where is the remaining concentration of subtrate at time t, such as organic mattere, organic carbon

(mass of carbon per unit volume/mass of refuse). is the concentration of microorganinsms (kilogram,

microorganisms per cubic meter refuse), is the maximum rate of substrate utilization per kilogram of

microorganisms, isrefuse concentration at which the rate is one half the maximum rat of substrate

utilization.

The zero and first order reactions can be used to approximate Monod’s equation by the functions in

two extreme cases: (1) zero order reaction with respect to substrate concentration: for large C, the

subtrate utilisation rate

is constant if the concentration of the microorganism, , remains constant

and (2) first order reaction with respect to the subtrate concentration: for small C and assuming again

constatnt concentration of microorganisms, , remains constant, the subtrate utilization rate is then a

linear function of the subtrare concentartion.

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2.1.1 Zero Order Model

This model assumes that the boigas generated from landfills remains steady against time and ultimately

the age and type of the was in the landfill has no effect on the gas production. This model is fairly

extensively used in the estimation of landfill gas by and required adjusting of parameters to fit field

data in order to optimize the results (SCS Engineers, 1997).

2.1.2 First Order Model

According to Kamalan & Sabour (2011), just about all the available models used to predict biogas from

landfills are based on the first order decay models. This model consider the quality of the waste in

terms of the mositure content, carbon content, age of waste and the capability of waste to be digested.

Apart from the quality, waste quantity and condition of landfill in terms of climate, temeperature and

precipitation are considered in this model. In other words, the effect of depletion of carbon in the waste

with time is accounted for in the first order model (Ozakaya, et al., 2006).

3.0 METHODOLOGY

This study will be a qualitative research prepared from secondary data sources comprising published

textbooks, Internet materials, and scholarly journals, articles and reports. The major strength and

advantage of utilizing the qualitative method in this study will be the unique opportunity of accessing

the many and different sources of quality data and scholarly information on landfill gas estimation

models and the different methods of methane recovery as an energy potential.

Waste data for the Chunga landfill will be collected and used to aid in the selection of landfill gas

estimations models to apply to the landfill for estimating methane production. From these results the

environmental impact of methane gas will be established in terms of global warming potential and

benefits of capturing this harmful gas will be outlined.

Furthermore, an assessment of the equivalent kilowatts of energy that can be obtained from the landfill

methane gas will be estimated and an approximation of the number of households that can be powered

by this energy will be given based on the current average electrical power demands. To determine

whether installation of a methane recovery plant and it’s conversion to electrical energy is worth

considering, an approximate cost of construction and operations will be compared against the financial

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inflows from billing consumers and carbon credits that can be obtained from every ton of landfill gas

captured based on the international trading rates.

3.1 Structure of Dissertation

In order to understand the complexity of landfill gas production as well as the difficulties in its

estimation, Chapter I of this study presents the Introduction by giving some basic theory on landfill gas

estimation models and degradation processes in landfills. Chapter II provides a review of the literature

on various topics related to landfill gas creation, LFGTE projects, and the mathematical interpretation

of microbial degradation in landfills as well as how this is used in gas estimation models. Chapter III

takes the reader through a step-by-step process describing the methodology used in this analysis along

with any assumptions used. Chapter IV will go into greater detail about the analysis and provide

specific models and alterations to these models to establish an estimate of the of energy potential from

the Chunga landfill as a case study. Chapter V will describe the costs that would be incurred from each

of the various options as well as the findings from the analysis of the benefits and costs. All

conclusions and recommendations will be reported in Chapter VI.

3.2 Dissertation Time Line

Stage of the dissertation writing process

Number of

days/weeks

needed

Start date End date

STAGE ONE: Reading and research

a) Seek to identify an original, manageable

topic 2 week May-5-2016 May-20-2016

b) Reading and research into chosen topic 20 weeks May-29-2016 Oct-25-2016

STAGE TWO: The detailed plan

a) Construct a detailed plan of the

dissertation 13 weeks Oct-27-2016 Nov-03-2016

STAGE THREE: Initial writing

a) Draft the various sections of the

dissertation 12 weeks Nov-05-2016 Jan-08-2017

b) Undertake additional research where

necessary 8 weeks Jan-12-2017 Mar-11-2017

STAGE FOUR: The first draft

a) Compile and collate sections into first

draft of dissertation 4 weeks Mar-13-2017 Apr-12-2017

b) check the flow of the dissertation 2 weeks Apr-14-2017 Apr-29-2017

c) Check the length of the dissertation 3 days May-02-2017 May-05-2017

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d) Undertake any additional editing and

research 4 weeks May-06-2017 Jun-06-2017

STAGE FIVE: Final draft

a) Check for errors 1 week Jun-07-2017 Jun-14-2017

b) Prepare for submission 2 week Jun-20-2017 Jul-04-2017

c) Final proof-read and final editing 1 week Jul-12-2017 Jul-19-2017

d) Compile bibliography 3 days Jul-25-2017 Jul-28-2015

e) Get the dissertation bound 1 day Jul-29-2017 Jul-29-2017

f) Submit your dissertation 1 day Jul-30-2017 Jul-30-2017

BIBLIOGRAPHY

Amini, H. R., 2011. Landfill Gas to Energy: Incentives and Benefits, Florida: s.n.

Auditor General, 2010. Solid Waste Management, Lusaka: s.n.

Central Statistics Office [CSO], 2013. Population and Demographic Projection 2011-2035.

Chereminoff, P. N. & Morresi, A. G., 1976. Energy from Solid Wastes.

Department of Energy [DoE], 2010. Draft Renewable Energy Strategy for Zambia, Lusaka:

Department of Energy.

EMCON Associates, 1982. Methane Generation and Recovery from Landfills. Michigan: Ann Arbor

Science.

Findikakis, A. N. & Leckie, J. O., 1979. Numerical Simulation of Pas Flow in Sanitary Landfills. J.

Environ. Eng, Issue 115, pp. 927-945.

Gardner, N. & Probert, S. D., 1993. Forecasting Landfill Gas Yields. s.l.:Science Publishers Ltd.

Hartz, K. E. & Ham, R. K., 1982. Gas Generation Rates of Landfill Samples. s.l.:Conservation

Recycling.

Kamalan, H. & Sabour, M. S. N., 2011. A Review on Available Landfill Gas Models.

LCC and ECZ, 2008. Lusaka City State of Environment Outlook, Lusaka: LCC.

Ozakaya, B., A, D. & Bigili, M. B., 2006. Neural Network Prediction Model for Methane Fraction in

Biogas from Field Scale Landfill Bioreactors. s.l.:Environmental Modeling Software.

Scarlet, N. et al., 2015. Evaluation of Energy Potential of Municipa Solid Waste from African Urban

Areas. June.p. 1270.

Scharff, H. & Jacobs, J., 2006. Applying Guidance for Methane Emission Estimation for Landfills.

s.l.:s.n.

SCS Engineers, 1997. Comparison of Models for Predicting Landfill Maethane Rocovery ], California:

Institute for Environmental Management.

Shariatmadari, N., Sabour, H., Kamalan, H. M. A. & Ablofazlzade, M., 2003. Applying Simple

Numerical Models to Predict Methane Emission from Landfill. J. Applied Science, Issue 7, pp. 1511-

1515.

Spokas, K. et al., 2006. Methane mass balance at three landfill site: What is the efficiency of capture by

gas collection systems. 26(Waste Management), pp. 512-525.

Surroop, D. & Mohee, R., 2011. Power Ggeneration fromLandfil Gas. 17(2nd International Conference

on Environmental Engineering and Applications).

Tchobanoglous, G., Theisen, H. & Vigil, S., 1993. Intergrated Soild Waste Management. New York:

McGraw-Hill.

Van, H. & R, E., 1986. Estimating Landfill Gas Yields. California, s.n., pp. 92-120.

Willumsen, H., 1990. Landfill gas, Resources, Conservation and Recycling. pp. 121-133.