Residue Derived Fuels as an Alternative Fuel for the Hellenic Power Generation Sector and their Potential for Emissions ReductionConstantinos S. Psomopoulos

Constantinos S. Psomopoulos; Constantinos S. Psomopoulos

doi:10.3934/energy.2014.3.321

AIMS Energy

2014, Volume 2, Issue 3: 321-341. doi: 10.3934/energy.2014.3.321

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Research article Special Issues

Residue Derived Fuels as an Alternative Fuel for the Hellenic Power Generation Sector and their Potential for Emissions ReductionConstantinos S. Psomopoulos

Constantinos S. Psomopoulos ^,

Department of Electrical Engineering, Technological Education Institute of Piraeus, P. Ralli & Thivon 250, Egaleo, 122 44, Greece

Received: 01 July 2014 Accepted: 25 August 2014 Published: 02 September 2014

The European Union Landfill Directive (1999/31 EC) promotes more environmental friendly waste management options, by reducing the amount of wastes and more specific of biodegradable wastes, disposed of in landfills. The EU member states are adopting the mechanical-biological treatment process for municipal solid waste and non-hazardous industrial wastes to comply with the abovementioned Directive's targets on landfill diversion, and produce waste derived fuels such as refuse derived fuel and solid recovered fuel. Waste derived fuels present high calorific values depending on their synthesis and are being used both in dedicated waste-to-energy plants and as fuel substitutes in industrial processes. In this paper the refuse derived fuel and solid recovered fuel production and utilisation options in European Union are presented, and the possibilities in Greece based on the waste production and National Plan for Waste Management of the Ministry of Environment is attempted. The existing and ongoing studies on co-combustion and co-gasification with brown coal support the use of refuse derived fuel and solid recovered fuel as fuel on Hellenic Power Sector, adopting in the existing lignite power plants adequate Air Pollution Control systems. If the co-combustion or co-gasification of these alternative fuels is adopted from the Hellenic Power Sector a reduction on emissions is expected that cannot be neglected.

Keywords:

Citation: Constantinos S. Psomopoulos. Residue Derived Fuels as an Alternative Fuel for the Hellenic Power Generation Sector and their Potential for Emissions ReductionConstantinos S. Psomopoulos[J]. AIMS Energy, 2014, 2(3): 321-341. doi: 10.3934/energy.2014.3.321

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Abstract

1. Introduction

The mathematical models in epidemiology have been used to understand the temporal dynamics of infectious diseases. The first model used to study the spread of infectious diseases was given by Kermack and Mckendrick [1] in 1927. Practically, this model is based on a system of ordinary differential equations and has been widely investigated with several modifications, in [2]–[7] and references therein. The distinct variables to formulate the individuals compartments are susceptible (S), exposed (E), infected (I) and recovered (or removed, R). The classical SEIR model has been widely studied, for instance, see [8]–[13]. It is shown that the asymptotic behavior depends on the basic reproduction number ℜ₀ (the expected number of secondary cases produced by an infective person in a completely susceptible population). It is described as a threshold value that indicates whether or not the initial outbreak occurs. That is, if ℜ₀ < 1, then the infective population tends to decrease and there is no outbreak, whereas if ℜ₀ > 1, then the infective population tends to increase and an outbreak occurs.

In December 2019, the first case of a novel coronavirus disease was recognized at Wuhan in China [14]. The wave of this disease has spread all over the world, and the World Health Organization (WHO) named it the coronavirus disease outbreak in 2019 (COVID-19) on 11 February, 2020 [14]. In China, during the period from December 2019 to 31 January, 2020, about 10 thausand (9,720) cases of COVID-19 were confirmed [14]. We have to note that asymptomatic individuals of COVID-19 can transmit the infection [15]. Therefore, there would be more cases that could not be reported by medical authorities. In the absence of effective vaccines and therapeutics against COVID-19, countries have to resort to non-pharmaceutical interventions to avoid the infection or to slow down the spread of the epidemic.

In Algeria, the first case was reported on 25 February 2020 [16]. Since then, the number of confirmed cases of COVID-19 has increased day after day. From the end of March, 2020, the Algerian government mandated several approaches to eradicate the spread of COVID-19 such as trying to control the source of contagion and reducing the number of contacts between individuals by confinement and isolation [17]. The purpose of this study is to know how the epidemic will evolve in Algeria with and without such interventions. We use the early data reported in [18] until 31 March, 2020. Recently, many works used a mathematical models for COVID-19, for instance, see the following contributions [19]–[23]. In [20], an SEIR epidemic model with partially identified infected individuals was used for the prediction of the epidemic peak of COVID-19 in Japan. The results in [20] were restricted only to the cases in Japan, and the applicability of the model to the cases in any other countries were not discussed. In this paper, we apply a similar SEIR epidemic model as in [20] to the cases in Algeria. This work would contribute not only in understanding the possible spread pattern of COVID-19 in Algeria in order to act appropriately to reduce the epidemic damage, but also in showing the applicability of the model-based approach as in [20] to the cases in other countries, which might help us to assess the epidemic risk of COVID-19 worldwide in future.

2. Materials and method

2.1. Data

We use the data of confirmed COVID-19 cases in Algeria, which is available in the epidemiological map in [18]. The data consists of the daily reported number of new cases and accumulated cases for COVID-19 in Algeria from 25 February to 18 April, 2020 (see Figure 1 and Table 1). The number of reported cases has increased rapidly in the exponential sense until the beginning of April.

Table 1. Number of newly reported cases and cumulative number of COVID-19 in Algeria from 25 February to 18 April, 2020 with the nationwide isolation. From 25 February to 18 April 2020.

Date (day/month)	Number of newly reported cases	Cumulative number
25 February	1	1
26 February	0	1
27 February	0	1
28 February	0	1
29 February	2	3
1 March	0	3
2 March	0	3
3 March	2	5
4 March	12	17
5 March	0	17
6 March	0	17
7 March	2	19
8 March	1	20
9 March	0	20
10 March	0	20
11 March	0	20
12 March	5	25
13 March	0	25
14 March	10	35
15 March	17	52
16 March	6	58
17 March	2	60
18 March	12	72
19 March	18	90
20 March	12	102
21 March	37	139
22 March	60	201
23 March	29	230
24 March	34	264
25 March	38	302
26 March	65	367
27 March	42	409
28 March	45	454
29 March	57	511
30 March	73	584
31 March	132	716
1 April	131	847
2 April	139	986
3 April	185	1171
4 April	80	1251
5 April	69	1320
6 April	103	1423
7 April	45	1468
8 April	104	1572
9 April	94	1666
10 April	95	1761
11 April	64	1825
12 April	89	1914
13 April	69	1983
14 April	87	2070
15 April	90	2160
16 April	108	2268
17 April	150	2418
18 April	116	2534

| Show Table

DownLoad: CSV

Figure 1. Daily reported number of new cases (left) and accumulated cases (right) of COVID-19 in Algeria from 25 February to 18 April, 2020 [18].

DownLoad: Full-Size Img PowerPoint

2.2. Model

In this paper, we use the following well-known SEIR epidemic model, for t > 0,

${\begin{matrix} S^{'} (t) = - β S (t) I (t), \\ E^{'} (t) = β S (t) I (t) - λ E (t), \\ I^{'} (t) = - γ I (t) + λ E (t), \\ R^{'} (t) = γ I (t), \end{matrix}$ (1)

with initial conditions

$S (0) = S_{0}, E (0) = E_{0}, I (0) = I_{0} and R (0) = R_{0} .$ (2)

(1)–(2) is a system of ordinary differential equations based on the phenomenological law of mass action. For simplicity, we suppose that E₀ = R₀ = 0 (initially, there is no exposed and recovered individual). Moreover, we assume that S(0) + E(0) + I(0) + R(0) = 1 from which we have S(t) + E(t) + I(t) + R(t) = 1 for all t > 0, and hence, each population implies the proportion to the total population. All the parameters of the model are nonnegative constants, and they are described in Table 2. Figure 2 provides a schematic representation of model (1).

Figure 2. Interactions between the compartments of the epidemiological model (1). The continuous lines represent transition between compartments, and entrance and exit of individuals. The dashed line represents the transmission of the infection through the interaction between susceptible and infected individuals. The recovered class is omitted because it is decoupled from the other compartments.

DownLoad: Full-Size Img PowerPoint

Note that γ implies the removal rate and the removed population R includes the individuals who died due to the infection.

We follow the same idea in [20] to give the prediction for Algeria. Parameter estimation and epidemic peak are treated and obtained. Moreover, we estimate the basic reproduction number for the epidemic COVID-19 in Algeria. Since the virus presents asymptomatic cases and the fact that there is a sufficiently lack of diagnostic test, we consider an identification function

$t \in ℜ^{+} \mapsto X (t) = ϵ \times I (t) \times N .$ (3)

This quantity describes the number of infective individuals who are identified at time t, with N is the total population in Algeria (N = 43411571) and ε is the identification rate. As in [20], we suppose that ε ∈ [0.01, 0.1].

3. Results

3.1. Epidemic prediction

In this section, we develop simulations to provide epidemic predictions for the COVID-19 epidemic in Algeria. We focus on predicting the cases and parameter estimation. We are able to find the basic reproduction number and to estimate the infection rate. Recall that the number ℜ₀ is defined as the average number of secondary infections that occur when one infective individual is introduced into a completely susceptible population. In epidemiology, the method to compute the basic reproduction number using the next-generation matrix is given by Diekmann et al. [24] and Van den Driessche and Watmough [25]. For our model, the value of the basic reproduction number of the disease is defined by

$ℜ_{0} = \frac{β S_{0}}{γ} = \frac{β}{γ} (1 - E_{0} - I_{0} - R_{0}) = \frac{β}{γ} (1 - \frac{X (0)}{ϵ N}) .$ (4)

In fact, the largest eigenvalue or spectral radius of FV⁻¹ is the basic reproduction number of the model, where

$F = [\begin{matrix} 0 & β S_{0} \\ 0 & 0 \end{matrix}] and V = [\begin{matrix} λ & 0 \\ - λ & γ \end{matrix}] .$ (5)

In our case, we assume that X(0) = 1 and N = 43411571, see Tables 1 and 2.

By some choices on the parameter ε, illustrations for prediction are given in Figures 3, 4 and 5. To estimate the parameters, we use the method of least squares and the best fit curve that minimizes the sum of squared residuals. We remark that ε does not affect the basic reproduction number and the infection rate as the total population N is large. We obtain an estimation of them as shown in Table 2 (ℜ₀ = 4.1 and β = 0.41). However, we observe that ε is an important parameter for prediction. In fact, the three illustrations in Figures 3, 4 and 5 show its influence on the peak.

As stated before, we use a simple but useful measure to provide the average number of infections caused by one infected individual R₀ = 4.1. The R₀ value in China was estimated to be around 2.5 in the early stage of epidemic. In April 2020, the contagiousness rate was reassessed upwards, between 3.8 and 8.9 (see, [26]). Comparing with other results (see, [27]), R₀ may be unstable.

Table 2. Parameter values for numerical simulation.

Description	Value	Reference
β: Contact rate	0.41	Estimated
γ: Removal rate	0.1	[28], [29]
λ: Onset rate	0.2	[14], Situation report 30, [28], [30]
1/γ: The average infectious period	10	[28], [29]
1/λ: The average incubation period	5	[14], Situation report 30, [28], [30]
ε: Identification rate	0.01–0.1	[20]
N: Total population in Algeria	43411571	[31]
ℜ₀: Basic reproduction number	4.1	Estimated

| Show Table

DownLoad: CSV

Figure 3. Graph of X(t), with ε = 0.01, is plotted. It represents the number of identified newly cases. The red small circles are the reported case data. In this case, the data from the number of newly reported cases is well fitted the epidemic. Without the nationwide lockdown, the peak occurs approximately at t = 100 associated to a date between the beginning and the middle of June.

DownLoad: Full-Size Img PowerPoint

Figure 4. Graph of X(t), with ε = 0.05, is plotted. It represents the number of identified newly cases. The red small circles are the reported case data. In this case, the data from the number of newly reported cases is well fitted the epidemic. Without the nationwide lockdown, the peak occurs approximately at t = 110 associated to a date close to the middle of June.

DownLoad: Full-Size Img PowerPoint

Figure 5. Graph of X(t), with ε = 0.1, is plotted. It represents the number of identified newly cases. The red small circles are the reported case data. In this case, the data from the number of newly reported cases is well fitted the epidemic. Without the nationwide lockdown, the peak occurs approximately at t = 115 associated to a date between the middle and the end of June.

DownLoad: Full-Size Img PowerPoint

3.2. Intervention effect

We now discuss the effect of intervention. We first assume that the intervention is carried out for two months from April 1 (t = 37) to May 31 (t = 96) with reducing the contact rate β to kβ, where 0 ≤ k ≤ 1. We use the parameter values as in Table 2 with ε = 0.1. Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 is displayed in Figure 6.

Figure 6. Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the two months intervention from April 1 (t = 37) to May 31 (t = 96).

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From Figure 6, we see that the two months intervention in this case has the positive effect on the time delay of the epidemic peak. On the other hand, the epidemic size is almost the same for each k in this case.

We secondly assume that the intervention is carried out for three months from April 1 (t = 37) to June 30 (t = 126).

From Figure 7, we see that the epidemic peak is also delayed in this case. Moreover, the epidemic size is reduced for k = 0.75. In contrast, the epidemic size for k = 0.5 and k = 0.25 is almost the same as that for k = 1.

Figure 7. Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the three months intervention from April 1 (t = 37) to June 30 (t = 126).

DownLoad: Full-Size Img PowerPoint

We thirdly assume that the intervention is carried out for four months from April 1 (t = 37) to July 31 (t = 157).

Figure 8. Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the four months intervention from April 1 (t = 37) to July 31 (t = 157).

DownLoad: Full-Size Img PowerPoint

From Figure 8, we see that the epidemic peak is also delayed in this case. On the other hand, similar to the example for three months intervention, the epidemic size is effectively reduced only for k = 0.75.

We fourthly assume that the intervention is carried out for five months from April 1 (t = 37) to August 31 (t = 188). From Figure 9, we see that the epidemic peak is also delayed in this case. Moreover, the epidemic size is effectively reduced for k = 0.75 and k = 0.5. In contrast, the epidemic size for k = 0.25 is almost the same as that for k = 1.

Figure 9. Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the five months intervention from April 1 (t = 37) to August 31 (t = 188).

DownLoad: Full-Size Img PowerPoint

We finally assume that the intervention is carried out for six months from April 1 (t = 37) to September 30 (t = 218). From Figure 10, we see that the epidemic peak is also delayed in this case. Moreover, the epidemic size is effectively reduced for k = 0.75 and k = 0.5. In contrast, the epidemic size for k = 0.25 is almost the same as that for k = 1.

Figure 10. Time variation of the identification function X(t) for k = 1, 0.75, 0.5 and 0.25 in the six months intervention from April 1 (t = 37) to September 30 (t = 218).

DownLoad: Full-Size Img PowerPoint

From these examples, we obtain the following epidemiological insights.

The epidemic peak is delayed monotonically as k decreases (that is, the contact rate is reduced by the intervention).
The epidemic size is not necessarily reduced even if a long and strong intervention is carried out. To effectively reduce the epidemic size by the intervention, it suffices to continue the intervention until the epidemic peak attains during the intervention period.

In Figure 11, we look at the case of measures that are more effective. Our model shows that the end of the disease can be reached in the three months intervention, from April 1, for k = 0.05 and in the four months intervention for k = 0.1, from April 1. More severe measures can induce an end of the disease in two months (see Figure 12 (a) with k = 0.01).

Figure 11. Case of a very strong intervention, with ε = 0.1. The red small circles are the reported case data. Left: time variation of the identification function X(t) for k = 0.05 in the three months intervention, from April 1 (t = 37). Right: time variation of the identification function X(t) for k = 0.1 in the four months intervention, from April 1 (t = 37).

DownLoad: Full-Size Img PowerPoint

In Figure 12, we consider the cases where interventions are not taken early. It is shown that a delay in intervention implies a larger peak and additional duration for the epidemic to disappear. For instance, an intervention from April 20 implies a supplementary delay by 23 days for the epidemic to disappear with high considerable peak.

Figure 12. Simulation of different start times of carrying out the measures, with ε = 0.1 and k = 0.01 (Case of a very strong intervention). The red small circles are the reported case data. (a) from April 1 (t = 37), (b) from April 10 (t = 46), (c) from April 20 (t = 56) and (d) from April 30 (t = 66).

DownLoad: Full-Size Img PowerPoint

For some economical and social reasons and according to the situation of the epidemic, the strictness of intervention measures will decrease in a gradual way. The Figure 13 shows the case where the severity of intervention measures is reduced. This simulation suggests that decrease the parameter k gradually, on each half month, implies automatically a delay, at least of one month, for the end of the epidemic.

Figure 13. Simulation of the case where the strictness of intervention measures is reduced. The red small circles are the reported case data. From April 1 (t = 37), we take k = 0.01. From April 15 (t = 52), we take k = 0.05. From April 30 (t = 67), we take k = 0.1.

DownLoad: Full-Size Img PowerPoint

4. Conclusion

We have applied a mathematical model to predict the evolution of a COVID-19 epidemic in Algeria. It is employed to estimate the basic reproduction number ℜ₀, to obtain the epidemic peak and to discuss the effect of interventions. In this model, we take the fact that the virus presents asymptomatic cases and that there exists a sufficiency lack of diagnostic test.

The prognostic capacity of our model requires a valid values for the parameters β, λ, γ, the mean incubation period 1/λ and the mean infectious period 1/γ. The precision of these parameters is very important for predicting the value of the basic reproduction number ℜ₀ and the peak of the epidemic. Their estimations depend on the public health data in Algeria. To fight the new coronavirus COVID-19, it is necessary to control information based on valid diagnosis system.

From 25 February to 31 March, we founded that ℜ₀ = 4.1 > 1, which means that we need strong interventions to reduce the epidemic damage that could be brought by the serious disease. Moreover, the model suggests that the pandemic COVID-19 in Algeria would not finish at a fast speed.

In the Figures 3, 4 and 5, where ε = 0.01, 0.05, 0.1, respectively, the data from the number of newly reported cases is well fitted the epidemic. The peak will occur at the month of June and approximately close to the middle, the maximum number of new cases (relatively also the cumulative number) could achieve an important value in Algeria. This number will probably persist at a high level for several days if we do not apply intervention measures (isolation, quarantine and public closings). The model's predictions highlight an importance for intervening in the fight against COVID-19 epidemics by early government action. To this end, we have discussed different intervention scenarios in relation to the duration and severity of these interventions. We see that the intervention has a positive effect on the time delay of the epidemic peak. On the other hand, the epidemic size is almost the same for short intervention (effective or not) and decrease depending on the severity of the measures. In contrast with the last previous case, we observe that a large epidemic can occur even if the intervention is long and sufficiently effective.

At the moment, the consequence of COVID-19 in China is encouraging for many countries where COVID-19 is starting to spread. Despite the difficulties, Algeria must also implement the strict measures as in Figure 11, which could be similar to the one that China has finally adopted.

References

[1]	Ionescu G, Rada EC, Ragazzi M, et al. (2013) Integrated municipal solid waste scenario model using advanced pretreatment and Waste to Energy processes. Energ Conser Manage 76: 1083-1092. doi: 10.1016/j.enconman.2013.08.049
[2]	Rada EC, Ragazzi M (2014) Selective collection as a pretreatment for indirect Solid Recovered Fuel generation. Waste Manage 34(2): 291-297.
[3]	European Environmental Agency (EEA), Europe's Environment, The Fourth Assessment, Copenhagen, 2007. Available from: http://www.eea.europa.eu/
[4]	EU Directive 1999/31/EC of 26 April 1999 on the landfill of waste. Available from: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:31999L0031.
[5]	EU Directive 2000/76/EC of 4 December 2000 on the incineration of waste. Available from: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32000L0076.
[6]	EU Directive 2001/80/EC of 23 October 2001 on the limitations of emissions of certain pollutants into the air from large combustion plants. Available from: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32001L0080.
[7]	EU Directive 2001/77/EC of 27 September 2001 on the promotion of electricity produced from renewable energy sources in the internal electricity market. Available from: http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32001L0077.
[8]	McDougall F, White P, Franke M, et al. (2001) Integrated Solid Wastes Management: A Life Cycle Inventory, Blackwell Publishing.
[9]	Rada EC, Andreottoala G (2012) RDF/SRF which perspective for its future in EU. Waste Manage 32(6): 1059-1060.
[10]	Van Tubergen J, Glorius T, Waeyenbergh E, Classification of Solid Recovered Fuels, European Recovered Fuel Organisation, 2005. Available from : http://erfo.info/fileadmin/user_upload/erfo/documents/classification/Classification_report.270205.pdf.
[11]	Archer E, Baddeley A, Klein A, et al. (2005) Mechanical-Biological-Treatment: A Guide for Decision Makers Processes, Policies and Markets (Juniper Consultancy Services Ltd.)
[12]	European Commission - Directorate General Environment, Refuse Derived Fuel, Current Practice and Perspectives (B4-3040/2000/306517/Mar/E3), Final Report, 2003. Available from: http://ec.europa.eu/environment/waste/studies/pdf/rdf.pdf.
[13]	Facts and Figures, European Recovered Fuel Organisation (Erfo). Available from : http://erfo.info/fileadmin/user_upload/erfo/documents/facts_and_figures/facts_and_figures_201205.rev3.doc.
[14]	Ragazzi M, Rada EC (2012) RDF/SRF evolution and MSW bio-drying. WIT T Ecol Environ 163: 199-208.
[15]	EN 15359 - Solid recovered fuels - Specifications and classes.
[16]	Kakaras E, Grammelis P, Agraniotis M, et al. (2005) Solid Recovered Fuels as Coal Substitute in the Electricity Generation Sector. Thermal Sci J 9: 17-30. doi: 10.2298/TSCI0502017K
[17]	Koukouzas N, Katsiadakis A, Karlopoulos E, et al. (2008) Co-gasification of solid waste and lignite - A case study for Western Macedonia. Waste Manage 28: 1263-1275. doi: 10.1016/j.wasman.2007.04.011
[18]	Hilber T, Maier J, Scheffknecht G, et al. (2007) Advantages and possibilities of solid recovered fuel co-combustion in the European energy sector. J Air Waste Manag Assoc 57(10): 1178-1189.
[19]	Garg A, Smith R, Hill D, et al. (2007) Wastes as co-fuels: the policy framework for solid recovered fuel (SRF) in Europe, with UK implications. Environ Sci Technol 41(14): 4868-74.
[20]	Samolada MC, Zabaniotou AA (2014) Energetic valorization of SRF in dedicated plants and cement kilns and guidelines for application in Greece and Cyprus. Resour, Conserv Recycling 83: 34-43. doi: 10.1016/j.resconrec.2013.11.013
[21]	Kara M (2012) Environmental and economic advantages associated with the use of RDF in cement kilns. Resour, Conserv Recycling 68: 21-28
[22]	Consonni S, Giugliano M, Grosso M (2005) Alternative strategies for energy recovery from municipal solid waste: Part A: Mass and energy balances. Waste Manage 25(2): 123-135
[23]	Ragazzi M, Rada EC, Panaitescu V, et al. (2007) Municipal solid waste pre-treatment: A comparison between two dewatering options. WIT T Ecol Environ 102 : 934-949.
[24]	European Commission, Integrated Pollution Prevention and Control, Reference Document on “Best Available Techniques for the Waste Incineration”, 2006. Available from: http://ec.europa.eu/environment/ippc/brefs/wi_bref_0806.pdf.
[25]	Marsh R, Griffiths AJ, Williams KP, et al. (2007) Physical and thermal properties of extruded refuse derived. Fuel Process Technol 88 : 701-706.
[26]	Consonni S, Vigano F (2011) Material and energy recovery in integrated waste management systems: The potential for energy recovery. Waste Manage 31(9-10): 2074-2084.
[27]	Theochari Ch, Aravossis K, Varelidis P, et al. (November 2006) Solid waste management in Greece - The Attica case, Technical Chamber of Greece, Athens, Final Report.
[28]	Hernandez-Atonal FD, Ryu C, Sharifi VN, et al. (2007) Combustion of refuse-derived fuel in a fluidised bed. Chem Eng Sci 62: 627-635. doi: 10.1016/j.ces.2006.09.025
[29]	RECOFUEL (2004) TREN/04/FP6EN/S07.32813/503184. Available from: http://www.eu-projects.de/recofuel.
[30]	CERTH/ISFTA (2002) Co-gasification of coal and wastes for power generation, THERMIE-SF/08/98/DE, Final Report.
[31]	Bilitewski B (2007) Incineration of Refuse Derived Fuel - Development, problems and chances, Proc. Int. Conf. Envir. Manag. Eng. Plan. Econ. (CEMEPE), Skiathos, Greece, 1543-1548.
[32]	Psomopoulos CS, Bourka A, Themelis NJ (2009) Waste to energy: A review of the status and benefits in U.S.A.. Waste Manage 29: 1718-1724. doi: 10.1016/j.wasman.2008.11.020
[33]	Tillman DA, Duong D, Miller B (2009) Chlorine in Solid Fuels Fired in Pulverized Fuel Boilers — Sources, Forms, Reactions, and Consequences: a Literature Review. Energ Fuel 23(7): 3379-3391
[34]	Chyang CS, Han YL, Wu LW, et al. (2010) An investigation on pollutant emissions from co-firing of RDF and coal. Waste Manage 30(7): 1334-1340.
[35]	Grammelis P, Kakaras E, Skodras G (2003) Thermal exploitation of wastes with lignite for energy production. J Air Waste Manag Assoc 53(11): 1301-11.
[36]	Ozkan A, Banar M (2010) Refuse derived fuel (RDF) utilization in cement industry by using Analytic Network Process (ANP). Chem Eng T 21: 769-774.
[37]	Wagland ST, Kilgallon P, Coveney R, et al. (2011) Comparison of coal/solid recovered fuel (SRF) with coal/refuse derived fuel (RDF) in a fluidised bed reactor. Waste Manage 31(6): 1176-1183
[38]	Greek Association of Communities and Municipalities in the Attica Region (ACMAR), 2009. Project: Composition and physicochemical properties of MSW of Attica 2006-2008. Presentation in Environmental Protection Committee of The Greek Parliament, January 29, Athens, Greece.
[39]	Eurostat, Municipal waste generation and treatment, by type of treatment method, 2014. Available from: http://epp.eurostat.ec.europa.eu/tgm/refreshTableAction.do?tab=table&plugin=1&pcode=tsdpc240&language=en.
[40]	Envitec SA (2004) The MSW Recycling and Composting Plant in Ano Liosia, Information brochure.
[41]	Economopoulos P (2004) Methodology for Development of an Optimized Waste Treatment Plan in the Attica Region, Presentation Technical Chamber of Greece.
[42]	Economopoulos AP (2007) Technoeconomic aspects of alternative municipal solid wastes treatment technologies, Proc. Int. Conf. Envir. Manag. Eng. Plan. Econ. (CEMEPE), Skiathos, Greece, 1731-1736.
[43]	http://www.wtert.gr, (accessed March, 2014)
[44]	Statistical Data About Solid Waste Treatment in Greece, Ministry of Environment, Department of Environmental Planning, Section of Solid Waste Treatment: Record Nr. 109947/3120, December 2004
[45]	Tsatsarelis Th, Karagiannidis A (2008) Evaluation of refuse derived fuel production and consumption potential in Greece, Proc. 2^nd Int. Conf. Eng. Waste Valorisation, Patra, Greece, 1-8.
[46]	Lee SH, Themelis NJ, Castaldi MJ (2007) High-Temperature Corrosion in Waste-to-Energy Boilers. J Thermal Spray Techn 16(1): 1-7
[47]	Persson K, Brostrom M, Carlsson J, et al. (2007) High temperature corrosion in a 65MW waste to energy plant, Fuel Proc Tech 88: 1178-1182.
[48]	Vainikka P, Bankiewicz D, Frantsi A, et al. (2011) High temperature corrosion of boiler waterwalls induced by chlorides and bromides. Part 1: Occurrence of the corrosive ash forming elements in a fluidised bed boiler co-firing solid recovered fuel. Fuel 90(5): 2055-2063.
[49]	Bankiewicz D, Vainikka P, Lindberg D, et al. (2012) High temperature corrosion of boiler waterwalls induced by chlorides and bromides - Part 2: Lab-scale corrosion tests and thermodynamic equilibrium modeling of ash and gaseous species. Fuel 94: 240-250
[50]	Themelis NJ (2008) Older and Newer Thermal Treatment Technologies from a Reaction Engineering, IT3, Montreal, Canada.
[51]	Psomopoulos CS, Venetis I, Themelis NJ (2014) The impact from the implementation of Waste to Energy to the economy. A macroeconomic approach for the trade balance of Greece. Fresen Environ Bull, 23(11) in press.

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