Characterization of Municipal Solid waste’s Potential for Power Generation at Mekelle City as a Waste Minimisation strategy

ABSTRACT

Waste to energy concept is one of the best methods, which not only consider the environment but also generate energy from municipal solid waste (MSW). Generation of MSWs at Mekelle city, Ethiopia, has grown steadily mainly due to migration of people from rural areas. However, the waste has not been managed and utilised as a useful resource due to lack of awareness and proper technology in the city. The objective of this study was to measure the heat content of solid waste generated in the city. Measurement of heating value was performed on collected samples using bomb calorimeter and Dulong’s formula. The average heating values obtained from the experimental analysis were 17,001 kJ/kg. The energy content obtained from the elemental composition of waste using Dulong’s formula was 16,853 kJ/kg. These results indicate that it could be possible to generate 8.7 MW of power from the solid waste composition represented by the sample; it is a good potential to alleviating the power shortage and interruption problems in the city. The results of this study could be used for design considerations in the selection and establishment of waste to energy technology in Mekelle city.

KEYWORDS:

• Municipal solid waste
• ultimate analysis
• proximate analysis
• heat

1. Introduction

The rapid urbanisation that has been taking place during the twentieth century virtually transformed the world into communities of cities and towns facing similar challenges on environmental issues in which most of them have to be addressed at international level (Nagabooshnam and Ababa 2011).

Among those environmental issues, solid waste management (SWM) is a critical one because as long as humans are living in settled communities, solid waste generation is an unavoidable issue in both developed and developing nations. As a result, SWM became a worldwide agenda at United Nation Conference on Environment and Development in Rio de janeiro in 1992 with a great emphasis on reducing wastes and maximising environmentally sound waste reuse and recycling at first step in waste management (Khateeb, Al-Junidi, and Sawalha 2011).

Municipal solid waste (MSW) is a term usually applied to a heterogeneous collection of wastes produced in urban areas in either solid or semi-solid form (Hoornweg and Bhada-Tata 2012), the nature of which varies from region to region. It consists of household waste, construction and demolition debris, sanitation residue, waste from streets and so forth. The term MSW describes the stream of solid waste (trash or garbage) generated by households and apartments, commercial establishments, industries and institutions. The characteristics and quantity of the solid waste generated in a region are not only a function of the living standard and lifestyle of the region’s inhabitants but also of the abundance and type of the region’s natural resources. Urban wastes can be subdivided into two major components as organic and inorganic. In general, the organic components of urban solid waste can be classified into three broad categories: putrescible, fermentable and non-fermentable. Putrescible wastes tend to decompose rapidly and unless carefully controlled, decompose with the production of objectionable odours and visual unpleasantness. Fermentable wastes tend to decompose rapidly, but without the unpleasant accompaniments of putrefaction. Non-fermentable wastes tend to resist decomposition and, therefore, break down very slowly. A major source of putrescible waste is food preparation and consumption. As such, its nature varies with lifestyle, standard of living and seasonality of foods. Fermentable wastes are typified by crop and market debris (Ziraba, Haregu, and Mberu 2016; Henry, Yongsheng, and Jun 2006).

Waste generation in sub-Saharan Africa is approximately 62 million tons per year. Waste generation per capita is generally low in this region, but oscillates from 0.09 to 3.0 kg per person per day, with an average of approximately 0.65 kg/capital/day (AkhtarI 2014). Ethiopia is one of the sub-Saharan countries facing rapid urbanisation consequently producing a huge amount of wastes in the major cities such as Addis Ababa, Mekelle and Bahir dar. Most of the generated wastes end up in landfills without any economic value.

The focus of this study was Mekelle city where there is rapid economic development, high rate of urbanisation and an improved living standards. With these developments, the generation of MSW is constantly increasing. This causes environmental pollution and potentially affects people’s health, preventing the sustained development of Mekelle city and drawing serious public concern. The continuously generated wastes take up the limited land resources, pollute water and air, and consequently lead to serious environmental issues. Without effective handling and recovery, MSW may seriously threaten people’s health, improvement of environment and man’s sustainable development (Manaf, Samah, and Zukki 2009). Proper waste management is, therefore, an urgent and important task for the continued development of Mekelle city.

One of the waste management strategies being exercised around the world is re-using of waste in different forms with the generation of energy being an important and critical path for waste utilisation for economic value (Cheng and Yuanan 2010; Kathirvale et al. 2004; Murphy and Eamon 2004; Cheng et al. 2007; Ruth 1998; Consonni, Giugliano, and Grosso 2005). However, the characteristics such as the heating value of the MSW differ from region to region, from city to city because of the aforementioned reasons. Therefore, characterisation of the types of waste in terms of their potential in the generation of heat is crucial in order to understand the potential of power generation from the specific place. The heating value of waste is a measure of the energy released when it is burned. It can be estimated by combusting samples in a boiler and measuring the heat output using lab-scale bomb calorimeter or ultimate analysis. The most common methods currently being practised to evaluate the heating value of MSW are using the equation derived by Dulong or experimentally using the bomb calorimeter (Kumar, Venkata Subbaiah, and Rao 2010; Tadesse 2012).

Therefore, the main aim of this study was to measure the heat content of solid waste generated in Mekelle city. It also aims at creating a databank for the city with the systematic characterisation of the MSW, which has been barely reported before. The results can provide a huge opportunity for investors to be involved in power generation from municipal waste and consequently improve the city’s environment while economically benefiting themselves and the country at large.

2. Methodology and materials

2.1. Materials and instruments

The materials and instruments used during the study include Balance, Bomb calorimeter with model IKA calorimeter C-4000 adiabatic, Crucibles,Digital Camera, VECSTAR furnace up to 1200 ${ }^{0}C$, CARBOLTE furnace up to 1400 ${ }^{0}C$, oven with model number (XmtB −8000) up to 110 ${ }^{0}C$, plastic bags, plastic sheets, Thread, Chromium wire, Thermometer stopwatch and EA1112 thermo flash gas analyser.

2.2. Methods

The methodology employed for this study involves a review of related literatures, establishment and training of teams for the sorting and measurement of MSW for composition determination, proximate analysis and heating value evaluation of MSW. Here are the major methodological steps followed during the study.

2.3. Sources of data

In this study, both primary and secondary data sources were utilised. For gathering primary data, questionnaires, interviews and field observations were employed. The primary data have been the main resources of information for this study including experimental data. The secondary data sources included books, published articles both from internet and journals, various research papers that are published or unpublished and government publications.

2.4. Sorting of waste

The characterisation of solid waste into different categories is depending on region and waste sources. The solid waste generation is usually categorised from 15 to 24 different categories. There are food waste, paper (mixed), newsprint, plastic(film), plastic(rigid), plastic(foam), textile, rubber/leather, wood waste, yard waste, high grade paper, corrugated paper, glass(clear), glass(colored), metal(Aluminium), batteries/hazardous, diaper, fruit waste, metal(ferrous), metal (non-ferrous), inorganic, organics, and others.

In this study, manual sorting was performed with safety devices like hand protective plastic gloves, mouth and nose mask, and six related categories were prepared from the collected solid waste, which includes mixed plastics, cardboards, mixed dry leaves, textile products, mixed paper excluding diaper, shoe, bones for laboratory test as shown in Figure 1. Based on literature information, diaper, shoe, bones are not suitable for the recovery of energy by combustion because they need excess heating and release high emission gases when they are combusted.

Figure 1. Samples ready for laboratory analysis.

2.5. Proximate analysis

Proximate analysis consists of moisture content, ash content, volatile matter and fixed carbon determined by putting the selected sample to different ranges of the temperature between 110 ${ }^0$C and 950 ${ }^0$C with a sample of 1 g each. Care was taken to make the samples are well mixed, and each waste component was taken and chopped manually to reduce the size. The well-mixed sample was finally taken for laboratory analysis. The laboratory methods for measuring the proximate analysis of samples in this research were carried out based on the American Society of Testing and Materials (ASTM) standard (ASTM 2004). This standard determines the condition of laboratory analysis such as moisture, volatile and ash content. The per cent moisture of the MSW samples was determined by weighing the samples into a pre-weighed dish and drying the samples in an oven at 110 ${ }^0$C to a constant weight. The per cent moisture content was calculated as a percentage loss in weight before and after drying. The composite samples of MSW material used in the moisture content determination were weighed and placed in a furnace for 7 min at 950 ${ }^0$C. After combustion, the samples were weighed to determine the ash dry weight, with volatile solids being the difference between the dried solids and the ash.

Volatile matter is a portion of the waste, which is converted into the gas during combustion. Fixed carbon is the carbon remaining after all volatile matter been evolved from the particles. High volatile matter content indicates easy ignition of fuel. Ash content of waste is the non-combustible residue left after waste is burnt, which represents the natural substances after carbon, oxygen, sulphur and water. Analysis for the volatile matter of the samples was carried out at a temperature of 750 ${ }^0$C for 1 h. The ash content is important in the design of the furnace grate, combustion volume, pollution control equipment and ash handling systems of a furnace. Fixed carbon defined by carbon found in the material which is left after volatile test. Fixed carbon is determined by removing the mass of volatile from the original mass of the sample. Fixed carbon acts as a main heat generator during burning. Waste with high fixed carbon requires a longer detention time on the surface of the furnace to achieve complete combustion than the waste with a low fixed carbon load (Menikpura et al. 2007).

2.6. Ultimate analysis

The carbon (C), hydrogen (H), oxygen (O), sulphur (S) and nitrogen (N) determination in biomass represents the so-called ultimate analysis. These elements were detected by EA1112 thermo flash gas analyser except oxygen. Oxygen was determined by difference based on other element determination. About 10 mg of sample was burned at 900 ${ }^0$C in an oxygen atmosphere, so the C is converted into CO2, H in H2O, S into SO2 and the N into N2. The first three compounds were detected quantitatively by an IR detector, while N2 is determined by a thermal conductivity detector.

The weight of water, hydrogen and oxygen can be calculated using Equations (1–3) respectively.

(1) $Weigh{t}_{H2O}=Weigh{t}_{WH2O}-Weigh{t}_{WOH2O}$(1)

where $Weigh{t}_{H2O}$ is weight of water,$Weigh{t}_{WH2O}$ is weight with water, $Weigh{t}_{WOH2O}$ is weight without water.

(2) $Weigh{t}_H=\frac{Weigh{t}_{H2O}\times MolarWeigh{t}_H}{MolarWeigh{t}_{H2O}}$(2)

where $Weigh{t}_H$ is weight of hydrogen, $MolarWeigh{t}_H$ is molar weight of hydrogen, $MolarWeigh{t}_{H2O}$ is molar weight of water.

(3) $Weigh{t}_O=\frac{Weigh{t}_{H2O}\times MolarWeigh{t}_O}{MolarWeigh{t}_{H2O}}$(3)

where $Weigh{t}_O$ is weight of oxygen, $MolarWeigh{t}_O$ is molar weight of oxygen.

2.7. Experimental testing of the heating value

In this work, oxygen bomb calorimeter is used to determine the heating value of wastes. A sample of 0.5 g was used for the testing. Samples were chopped, well mixed manually and fed into the bomb calorimeter. The samples were then ignited in excess oxygen at 30 bars using an electric arc where the rise in temperature due to combustion of the sample was noted and the heating values of MSW were determined.

The heating value represents the amount of chemical energy in a given waste component, which depends on its carbon, moisture and hydrogen contents of the waste. If MSW can be handled with incineration and pyrolysis, heating value serves as an important parameter in deciding for incineration plant. Removing particular materials from MSW prior to incineration (e.g. through source separation) can affect combustibility. For example, removing other wastes and inorganic recyclables such as glass and metals can reduce moisture and increase average Higher Heating Value (HHV) as investigated in this study. In contrast, removing paper and plastics lowers HHV and increases moisture content. The net effect depends on exactly what is removed.

2.8. Numerical data organisation and analysis

The primary data obtained from sample households through direct measurement (solid waste generated), solid waste composition, proximate analysis, energy content, questionnaire and focus group discussion were analysed basically using averages, ratios and percentages as a major summarising tool. MS Excel and Long range energy Alternative Planning System (LEAP) program was used for the analyses of data obtained from solid waste measurements and questionnaire. LEAP is a widely used software tool for energy policy analysis and climate change mitigation assessment developed by the Stockholm Environment Institute (Heaps 2012). This software is a free license for educational purposes and used this kind of license for analysis of the data in this work. The outputs from the software used for analysis and interpretation of the collected data were presented in the form of tables and figures.

3. Results and discussions

3.1. Segregation and compositions of wastes

Characterisation of solid waste into different categories depends on region and waste sources. The solid waste generation is usually categorised between 15 and 24 different categories: food waste, paper (mixed), newsprint, plastic(film), plastic(rigid), plastic(foam), textile, rubber/leather, wood waste, yard waste, high grade paper, corrugated paper, glass(clear), glass (colored), metal(aluminium), batteries/hazardous, diaper, fruit waste, metal(ferrous), metal (non-ferrous), inorganic organics and others.

Sometimes countries use different categories for the physical characterisation of solid waste; the categories are usually distinguished in the various waste characterisation studies. MSW composition could vary from place to place according to the social background, location, population density, wealth, and culture and society consumption pattern (Zurbrugg 2002).

The physical composition by per cent of residential solid wastes of Mekelle city extracted from the sample households is summarised in Table 1. The percentage composition of each residential solid waste component generated from sample households was determined by dividing the total amount of a particular solid waste component type collected with the total amount of solid waste of all components. The quantity of each individual component of the MSW at sample households is also given in Table 1. From the waste collected, between 16 and 21 different categories were found at sample households during. The composition was then categorised into eight major categories: food, mixed papers, mixed plastics, textile product, cardboard, mixed dry leaves, diaper and other waste (shoe, bones and mobile card).

Table 1. Waste composition category.

Waste type	% of total
Humid waste (food waste, fruit, etc.)	71.43
Mixed plastics	13.59
Cardboards	8.8
Mixed dry leaves	1.99
Textile products	1.77
Mixed paper	1.61
Diaper	0.61
Others (shoe, bones and mobile card)	0.19
Total weight	100

The main purpose of sorting wastes was to estimate the quantity of solid waste component which was generated at sample households for proximate, heating value and energy potential determination in the laboratory based on the composition result. The results showed that food and fruit wastes constitute 71.43% of the total sample household wastes by weight in Mekelle city. Studies also showed that a large portion of wastes of developing countries is food wastes (Tegegn 2008).

Mixed plastics constitute for 13.59% by weight (Cardboards accounted for 8.80% and mixed dry leaves accounts for 1.99%). The least solid wastes in terms of weight are textile products, mixed paper, diaper and others (shoe, bones and mobile card) constituting only 1.77%, 1.61%, 0.61% and 0.19% in weight, respectively.

The segregated composition of MSW collected from the selected households in Mekelle city is shown in Table 2. The result indicates that the highest fraction contains more than 47% which is mixed plastics and 31% were cardboard which indicates that waste to energy technology has very high potential as the waste dominates by plastics, cardboard and dry leaves. The increase of plastic items disposed is due to the change of lifestyle of urban people preferring the use of processed food which most of them are packed and distributed by plastic materials. Most communities thus use plastics for carrying products during marketing. Cardboard was abundant next to plastics for the same reason. However, mixed dry leaves, textile products and mixed paper have relatively smaller because they are used as energy resources for cooking by mixing with woods and other sources of energy. The amount of diaper found mixed in the solid waste was around 0.68%, though this item was expected to be in the wet waste. Others (shoe, bones and mobile card) were also found with smaller amounts. Mobile cards are high in numbers but very small in weight.

Table 2. Segregated solid waste composition.

Waste type	% of total
Mixed plastics	47.58
Cardboards	30.81
Mixed dry leaves	6.98
Textile products	6.19
Mixed paper	5.63
Diaper	2.14
Others (shoe, bones and mobile card)	0.68
Total weight	100

3.2. Proximate analysis

The moisture content is a measure of the amount of water lost from materials upon drying to a constant weight. It is directly affected by physical and chemical properties of the material which enable it to absorb the exiting water in the environment. The complete proximate analysis of six types of waste in Mekelle city is shown in Table 3.

Table 3. Proximate analysis results of individual MSW.

Type of waste	Ash content (%)	Volatility (%)	Moisture (%)	Fixed carbon (%)
Plastic	0.12	94.44	0.24	5.2
Grass	10.55	69.58	5.61	14.26
Cardboard	6.23	77.81	5.76	10.2
Textile	0.49	90.17	0.85	8.49
Dry leave	16.81	61.19	6.76	15.24
Paper	13.73	75.23	2.99	8.05

Based on the laboratory analysis result, the waste characteristics of Mekelle city show that mixed dry leaves have high percentages of fixed carbon (15.24%) because these materials required longer detention time in the furnace to achieve complete combustion compared to other materials with a low percentage of carbon like plastic and mixed papers. The mixed dry leaves have 61.19% volatile matter, meaning the higher the volatile matter, the higher the reactivity of the materials. The advantage of high volatile and low fixed carbon is rapid burning of a solid waste residue based on the observation during the testing of the waste. Plastics have 0.12% ash content, 94.44% volatility and 5.2% fixed carbon. Solid wastes which have low ash content do not hinder the combustion of char, and allow easier diffusion of oxygen into the char. The useful range of volatile matter and fixed carbon for technical viability of energy recovery is greater than 40% and is less than 15%, respectively. Therefore, this investigation shows that most of the MSWs in Mekelle city shown in Table 3 have the capacity to generate more flue gases for heating.

The moisture content analysis of each individual waste constitutes mixed dry leaves with 6.76%, mixed plastic with 0.24%, mixed paper with 2.99%, textile product with 0.85%, cardboard with 5.76% and grass 5.61%. From this analysis, mixed dry leaves were found with highest moisture content and mixed plastics were found with lowest moisture content. The average moisture content of samples was found to be 6%. The desirable range of moisture content for technical viability of energy recovery is less than 45% (Kuleape et al. 2014). Therefore, the characteristics of the MSW in Mekelle city are within the desirable range of moisture content for technical viability of energy recovery by incineration. Wastes with different moisture contents have different drying characteristics. Those with higher moisture content require a longer drying time and much more heat energy, causing a lower temperature in the furnace, and vice versa. If the moisture content is too high, the furnace temperature will be too low for combustion, such that auxiliary fuel is needed to raise the furnace temperature and to ensure normal combustion.

3.3. Ultimate analysis

The oxygen value is calculated by subtracting the other components, including ash and moisture, from 100%. This analysis is used to characterise the chemical composition of the organic fraction of the waste as shown in Table 4, which, in turn, is useful in assessing the stability of the waste as a fuel and in predicting releases from combustion (Amber, Kulla, and Gukop 2012).

Table 4. Elemental analysis of selected components.

Type of wastes	N (%)	C(%)	H(%)	S (%)	O (%)
Grass	0.089	38.3	6.0055	0	55.6075
Dry leaves	0.311	37.53	5.666	0	56.497
Paper	0	36.51	5.314	0	58.1805
Plastics	0	63.91	5.331	0.045	30.7185
Cardboard	0	39.32	7.2015	0	53.482
Textile	0	43.421	3.526	0	53.0535

The composition of waste components with moisture and dry are given in Table 5.

Table 5. Composition of components with moisture content and dry base.

Type of waste	Unit wet Weight (kg)	% of Moisture	Dry weight (kg)	C	H	O	N	S
Plastic	1	1	0.99	0.63	0.05	0.23	0	0
Leaves and grasses	1	8	0.92	0.35	0.05	0.52	0	0
Cardboard	1	5	0.95	0.37	0.07	0.51	0	0
Textile	1	10	0.90	0.50	0.06	0.28	0.04	0
Paper	1	6	0.94	0.34	0.05	0.55	0	0

Using Equations (1–3), the weight of water, hydrogen and oxygen is 0.30, 0.03 and 0.27 kg, respectively. The mass of elements with and without moisture is given in Table 6.

Table 6. Weight of elements with and without water.

Element	Weight without water (kg)	Weight with water (kg)
C	2.19	2.19
H	0.28	0.32
O	2.08	2.34
N	0.04	0.04
S	0	0

The molar composition of the elements are also calculated by dividing each component by its respective molar weight as shown in Table 7

Table 7. Molar composition of elements.

Element	Atomic weight	Weight without water (kg)	Weight with water (kg)
C	12	0.18	0.18
H	1	0.28	0.32
O	16	0.13	0.15
N	14	0.00	0.00
S	32	0.0001	0.0001

The mole ratio is calculated by dividing the number of moles of each element by the lowest number of moles (Sulphur in this case) and the mole ratio for each element is given in Table 8.

Table 8. Mole ratio of Elements.

Element	Mole ratio without water (kg)	Mole ratio with water (kg)
C	2605	2605
H	4050	4525
O	1850	2088
N	44	44
S	1	1

Therefore, the chemical formula for this particular solid waste sample is $C_{2605}{H}_{4050}{O}_{1850}{N}_{44}S$ without water & $C_{2605}{H}_{4525}{O}_{2088}{N}_{44}S$ with water. Excluding the chemical formula with water, the energy content of the dry weight can be found using modified Dulong formula (Omari 2013):

(4) $\begin{array}{rl}& HV(kJ/kg)=337(C)+1419(H_2-0.125O_2)\\ & +93(S)+23(N)+93(S)+23(N)\end{array}$(4)

In order to calculate the energy content of the dry weight, it is important to know the % by weight of each element as given in Table 9.

Table 9. Weight (%) of elements.

Element	Number of moles	Atomic weight	Weight	Weight %
C	2605	12	31260	47.68
H	4050	1	4050	6.18
O	1850	16	29600	45.15
N	44	14	616	0.94
S	1	32	32	0.05
Total			65558	100

Using Equation (4), the theoretical energy values of the MSW are 16,853 kJ/kg having a deviation of 0.87% with experimentally measured values (17,001 kJ/kg) shown in Table 10, which is not a big difference.

Table 10. Energy content of samples.

Type of samples	Heating value (kJ/kg)
Plastic	23,192.6
Grass	16,424.7
Cardboard	16,217.4
Textile	20,495.2
Dry leave	13,502.7
Paper	12,173.6
Average	17,001.0

Table 10 shows the HHV of individual MSW collected from the selected households. The result showed that plastic waste has the highest HHV of 23,192.6 kJ/kg among all tested wastes, textile products with a value of 20,495.2 kJ/kg and the least HHV was found to be paper with 12,173.6 kJ/kg.

3.4. Net heating value of MSW

The net energy and recovery potential of MSW was estimated by correlating the result of high heating value, moisture content and 1 Kg MSW sample. When waste is combusted, energy is given out as heat. Some of the energy is consumed in drying of the waste (to evaporate the water in the waste) and the rest (net) is available for conversion into useful work or power generation. The water in the waste is represented by the moisture content. Hence,

(5) $N_e=G_{te}-E_d,$(5)

where $N_e$ is Net energy, $G_{te}$ is gross total annual energy, $E_d$ is the energy required in drying the waste. However, the energy required for drying MSW to a constant weight (Ed) is given by the sum of the energy required to raise the temperature of the water in waste from its initial temperature to a vaporisation temperature of 100 ${ }^0$C (HI) and the energy required to completely vaporise the water in the waste at 100 ${ }^0$C or heat of vaporisation (HV). This means the energy required in drying the waste is:

(6) $E_d=HI+HV$(6)

where:

(7) $HI=m_m\times c\times \mathrm{\Delta }T,HV=m_m\times L_{hv}$(7)

where $m_m$ is mass of moisture of the waste, $L_{hv}$ is latent heat of vaporisation, $c$ is heat capacity of water in MSW and $\mathrm{\Delta }T$ is change in temperature. Substituting Equations (6) and (7) into (5), the net energy can be calculated as:

(8) $N_e=(m-m_m)\times Cv-(m_m\times c\times \mathrm{\Delta }T+m_m\times L_{hv})$(8)

where $m$ is mass of the sample MSW, $Cv$ is heating value of dry MSW, $L_{hv}$ is latent heat of vaporisation. Considering the following assumptions:

• Values to assume are average temperature of Mekelle city (18 ${ }^0$C).

• Boiling point of water in terms of moisture content (100 ${ }^0$C)

• Heat capacity (c) of water in MSW (4.2 kJ/kg-K)

• One kilogram of MSW

• Determined average heating value (17 MJ/kg)

• Average moisture content (6%)

• Latent heat of vaporisation (2260 kJ/kg)

By substituting all the parameters in Equation (8), the net energy is 13,532 kJ/kg. World Bank guide on the incineration of MSW recommends that a Lower heating Value (LHV) of minimum 6000 kJ/Kg during all the seasons is required for sustained combustion for adopting the thermal treatment process (Kumar, Venkata Subbaiah, and Rao 2010; Johari et al. 2012). This shows that the heating value of MSW found in Mekelle city is feasible for power generation. The efficiency of heat recovery may be obtained as:

(9) $\eta =\frac{N_e}{G_{te}}$(9)

where $\eta$ is the energy efficiency. By substituting the values in Equation (9), the efficiency of the energy recovery from MSW is 79.6%.

3.5. Power generation

The power that can be recovered from MSW of the households in Mekelle cities can be calculated by daily net energy divided by the hours of the day in terms of seconds. With the minimum daily generation of waste in Mekelle estimated to be around 55,329 kg, the power generation capacity can be estimated as:

(10) $P=N_e\times \frac{M_{waste}}{day}\times \frac{day}{24hours}\times \frac{hours}{3600s}$(10)

where $P$ is the estimated power generation, $M_{waste}$ is the total mass of waste generated. Substituting all the known values in Equation (10), the estimated power generation with the minimum waste generation in Mekelle city is 8665 kW.

3.6. Optimisation of heat contents of wastes

The burning characteristics of MSW are affected by various interacting parameters that need to be optimizsed. Moisture content is one of the most important properties of MSW in determining the burning characteristics. The higher the level of moisture, the longer it will take the material to burn and also it affects the useful energy obtainable from the waste. In addition, ash content and ash composition have an impact on smooth running of a combustor. Slagging and clinker formation can be caused by melting and agglomeration of ashes in the reactor (Gebreegziabher et al. 2012). The ultimate and proximate analysis was used for the determination of constraint compositions in each waste stream. The steps followed in this analysis can be employed in any waste to energy process containing so many waste streams and accordingly an exact moisture reduction level and the corresponding heating value of the composite can be predicted as indicated in Table 11.

Table 11. Heating value and proximate analysis result for composite samples.

Type of samples	Heating value (Kcal/Kg)	Ash (%)	Volatility (%)	Moisture (%)	Carbon (%)
Plastic + Grass	20,388.34	3.64	81.94	2.54	11.88
Plastic + Card board + Grass	18,796.47	4.22	81.41	3.26	11.13
Plastic + Card board + Paper	19,889.12	4.75	86.71	1.51	7.03
Plastic + Dry Leave + Paper	18,890.67	6.59	83.25	2.14	8.02
Plastic + Textile	22,441.76	0.12	93.39	1.43	5.06
Plastic + Paper	21,028.88	5.54	86.5	1.18	6.78
Plastic + Dry Leave	21,405.64	3.71	86.77	3.71	7.84
Plastic + Card board	20,416.94	3.76	93.09	1.43	1.72
Plastic + Card board + Textile	19,587.7	3.09	86.80	1.85	8.26
Grass + Dry Leave + Card board	14,906.24	9.34	74.22	5.16	11.28
Paper + Textile + Dry Leave	14,105.57	10.26	77.11	4.69	7.94

The ash content, volatility matter, moisture content and fixed carbon were evaluated using furnace at different temperature ranges. From the measured values of wastes, the lower value of moisture content was found on plastic, textile and paper, and the lower value of ash content was found on plastic, textile and cardboard. The remaining wastes have a high amount of ash and moisture content. Therefore, with these results into consideration, optimisation was done by mixing the high and low values of ash and moisture contents. The result shows that a mixture of plastic and textile has 22,441.76 kJ/kg energy value, which is higher than the average measured value of the individual wastes ((23,192.6 kJ/kg + 20,495.2 kJ/kg)/2 = 21,843.9 kJ/kg). This shows that the utilisation of waste with an optimal energy release by mixing a few waste types rather than using all types of waste would be more economical.

4. Conclusion

This paper presents findings related to MSW, waste compositions, proximate analysis of wastes and energy values of wastes at Mekelle city. The generation rate of waste in Mekelle city was studied as part of this work and it showed 0.33 kg/capital/day. The wastes collected from selected households were also sorted into their respective categories. Among these categories, food and fruit wastes constitute 71.43% by weight. Next to food and fruit wastes, mixed plastics constitutes 13.59% by weight in Mekelle city.

Based on the laboratory analysis result, the moisture content analysis of each individual waste constitutes mixed dry leaves with 6.76%, mixed plastic with 0.24%, mixed paper with 2.99%, textile product with 0.85%, cardboard with 5.76% and grass 5.61%. From this analysis, mixed dry leaves were found with highest moisture content and mixed plastics was found with lower moisture content. The average moisture content of samples was found to be 6%. This result fulfils the desirable range of moisture content for technical viability of energy recovery by incineration.

From the MSW disposed in Mekelle city, plastic waste presents the highest HHV among all tested wastes, with a value of 23,192.6 kJ/kg, textile products with a value of 20,495.2 kJ/kg and the least HHV was found to be Paper with a value of 12,173.6 KJ/kg. An average HHV of 17,001 kJ/kg and LHV 13,532 kJ/Kg with a moisture content of 6% was obtained, and this can generate 8.7 MW of power. Furthermore, the energy content obtained from the elemental composition of waste using Dulong’s formula was 16,853 kJ/kg. Based on the results obtained, it is feasible to implement the waste to energy technology for Mekelle city. The methodologies applied in this work and the outcome will be helpful to support in characterising the waste generated in other cities. This will also promote waste to energy investment and subsequently provide significant co-benefits by improving MSW management and at the same time ensuring energy security.

Acknowledgments

We would like to thank Mekelle University for providing the funding under project number CRPO/EiT-M/Large/Recurrent/001/2010 to conduct this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Mekelle University [CRPO/EiT-M/Large/Recurrent/001/2010].

Notes on contributors

Mulualem G. Gebreslassie

Dr. Mulualem G. Gebreslassie is Associate Professor of Renewable Energy at the Ethiopian Institute of Technology-Mekelle (EiT-M). He holds a PhD in Engineering from University of Exeter, joint MSc from UPM, IMN, and KTH as well as a BSc from Mekelle University. He has wide-range of research experiences and was and is being involved in international large scale projects as a principal investigator and a member with more than 15 international Universities. He has published 18 articles, book chapters, and conference proceedings and is a member of professional bodies such as the European Energy center, international solar energy society, and American energy society. His research interests include: Technology development, on-grid and off-grid energy systems, energy conservation and management, energy policy etc.

Hailu B. Gebreyesus

Mr. Hailu B. Gebreyesus received MSc in Energy Technology from Mekelle University, BSc Chemical Engineering. He is currently a lecturer at Adigrat University. He has been participating in various researches and consultancy services.

Mulubrhan T. Gebretsadik

Mr. Mulubrhan T. Gebretsadik holds MSc in process Engineering from Karlsruhe Institute of Technology, BSc in Mechanical Engineering from Bahir Dar University, and he is currently completing his PhD study from Karlsruhe Institute of Technology. He is involved in different research and community service activities and has published five articles and conference proceedings. His research interest includes thermal processes, energy etc.

Solomon T. Bahta

Solomon T. Bahta received a joint masters in Renewable Energy from IST, Portugal, Lisbon and KTH, Sweden, Stockholm in 2013. He is a senior lecturer of Renewable Energy in Mekelle University, EiT-M. He has been participating in various researches and consultancy services related to Renewable Energy. His Research focuses on; Innovative technology development for converting and recovering municipal solid waste to energy; Renewable energy resources assessment, studying energy provision systems to developing countries; Alternative fuel development for industries and energy efficiency enhancement.

Seyoum Eshetu Birkie

Dr. Seyoum Eshetu Birkie, PhD in Industrial Engineering and Management from KTH Royal Institute of Technology, Sweden and Politecnico di Milano, Italy, is an assistant professor at KTH. He has several years of teaching and research experience. His areas of current research interest include sustainable production management as well as supply chain and operations risk management.