Introduction

The enormous increase of municipal solid wastes due to population increase has resulted in significant concerns on behalf of citizens and politicians about the appropriate waste management method that should be followed in order to protect both the environment and human health (Castagna et al. 2013). Thus, environmental issues that emerge could be faced with a proper waste management method contributing simultaneously to such important issues (Gallardo et al. 2014).

Across the world, many organizations have developed various strategies in order to meet the increased amount of municipal solid wastes and their impacts, also targeting to ensure sustainable development. In the European Union, the SDS (European Union Sustainable Development Strategy) has been developed with an overall objective to improve management and avoid overexploitation of natural resources, recognizing the value of ecosystem services by avoiding waste generation, as well as to promote good public health on equal conditions and improve protection against health threats (Pires et al. 2011).

In order to select the most appropriate solid waste treatment, several social, economic, and environmental criteria have to be addressed. According to Christensen (2011), an ideal waste treatment method cannot exist. It is a combination of factors that should be managed in such a manner in order to satisfy the needs of the proposed area. Various solid waste treatment technologies are now available. Waste treatments that either include energy and material recovery in incineration furnaces or aerobic and anaerobic composting and mechanical and biological treatment as well as landfilling, whose appropriate selection meets the above criteria may play a significant role in a successful solid waste management system (Themelis et al. 2013).

Although results reveal that wastes produced per capita have decreased, the remaining wastes after reuse and recycle are of remarkable amount and are still disposed in landfills. Thus, across Europe almost 38% of municipal solid wastes are still landfilled, varying from 0% (Denmark, Netherland) to over 70% for the majority of European countries (EUROSTAT 2014), while worldwide landfilling is over 80% (Themelis et al. 2013). As it may be easily realized, there is a tremendous amount of available MSW (municipal solid wastes) which can constitute a major source of energy in WtE (waste-to-energy) plants instead of mainly creating many social and environmental issues by their disposal in landfills.

It is obvious that exploitation of MSW in such power plants, taking also into account the high interest in increasing the penetration of renewable energy sources in electricity production sector in EU and worldwide, nowadays, constitute a mature solution. Furthermore, exploitation of the energy in the form of heat, supporting steam, or heating industrial and residential infrastructures, can contribute to a faster decarbonization of the energy system. In the report of ERC entitled ‘The 2014 ERC directory of Waste-to-Energy facilities’, it is demonstrated that in the USA, thermal conversion of 30.21 million tons of MSW produced 14.5 billion kWh also revealing the challenges of this specific market having the potential to avoid 250 million tons of MSW from disposal in landfills by producing a clean and sustainable energy. In Europe, according to Heat Roadmap Europe II, 50% of the local heat demand in many European cities is supplied by district heating systems (DHS) based on WtE plants representing almost 50 TWht while the ability of wastes to produce 200 TWht of heat until 2050, reveals the potential of such plants (Connolly et al. 2013; Pardo et al. 2012).

A crucial point that should be taken into consideration in the development process of energy recovery facilities is the gate fee. According to “Costs for Municipal Waste Management in the EU” European Commission report (Hogg 2011) the gate fee is defined as: “The gate fee paid represents a unit (usually per tonne) payment made by the local authority to the service provider to generate a stream of revenue”. It is a cost that is usually covered by local authorities but also is depending on the administrative structure of each country. Sanitary landfills have cheaper gate fee than energy recovery facilities (Themelis et al. 2013), therefore, the latter in order to be more attractive must adopt techniques that could reduce it to the minimum possible.

European Union, pioneering, has introduced an energy factor in order to ensure the quality of energy production. However, specific values of this factor must be reached for the authorization of the plant. Waste Framework Directive 2008/98/EU has set this value in the order of 0.60 for plants that were granted permit before December 2008 and 0.65 for future plants or plants that will be granted permit after the particular date.

The work presented here is an effort on developing and introducing a concrete methodological approach for the selection of the site and the size of a waste-to-energy facility considering a significant number of technical factors affecting this decision. The method is aiming to provide a tool for the decision makers and stakeholders for evaluating their alternatives trying to minimize the gate fees and maximizing revenues and R1. The developed algorithm was validated using data retrieved from a previous publication of two from the authors of this paper, for locations in Greece, and mentioned in Psomopoulos et al. 2013.

Methodology

For each waste-to-energy plant decision, a significant number of parameters related to potential site locations, treatment methods, and potential revenues are always being considered along with available waste quantities. All these data can be processed mainly based on the experience of the experts involved in the processes, while no concrete approaches seem to be proposed in the literature. Under this context, Kyriakis in Kyriakis 2016 proposed along with Kyriakis et al. (2016) algorithm (Fig. 1) for a step by step approach to the issue. The interaction between these data will reveal the most appropriate site and the size of the plant, based on the restrictions and possibilities given by the available data, when compared to other locations. With the proposed approach, the target is the energy factor to be the highest and the gate fee the minimum possible. The interaction of the data collected targets to the evaluation of a specific site, mainly according to the technical considerations of the proposed area. When the technical aspects of the area have been satisfied, each area is evaluated according to its potential in reaching maximum energy efficiency factor and minimum gate fee. The proposed method is shown in Fig. 1 and as it can be seen, consists of four sub-models, each one focusing on a specific part of this multi-parametric problem when it comes to decide about the site and the size of a WtE plant with low as possible gate fee and the highest possible R1 factor. These four sub-models are described below.

Fig. 1
figure 1

Site and size selection algorithm (Kyriakis 2016) (NPV - Net Present Value, IRR - Internal Rate of Return, CPBP - Complete PayBack Period)\

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Site evaluation model

The location of any waste treatment facility independently from the selected method, always arise conflicts on behalf of citizens. However, in contrast to landfilling method, the treatment of wastes with energy recovery during the previous years has been accepted as a useful and secure method from the citizens in the majority of countries worldwide and the development of this technology has achieved high rates (Chaliki et al. 2016). Waste disposal at landfills, unfortunately, generates serious environmental impacts even for the most appropriate location; several studies have revealed that its location remains the most significant problem (Themelis et al. 2013).

The small land requirement along with the environmental benefits constitutes the major advantage that WtE plants have. Several technical considerations should always be taken into account when a specific area is proposed for the implementation of such facility. Below the most important technical considerations according to literature are listed and should be satisfied in order to achieve a site evaluation as accurate as possible, with high possibility to achieve a low gate fee (Kyriakis 2016; Themelis et al. 2013). In the cases were some of these parameters are not existent then dependently on which the potential revenues are small in the case of lack of district heating or cooling or industrial steam users, either the proposed site should not be used or it will be very expensive to operate and construct in case of lack of roads or lack of landfill site or waste generation center.

  • Proximity to waste generation center

  • Proximity to electricity connection lines

  • Proximity to district heating or cooling

  • Proximity to water

  • Proximity to industrial steam consumers

  • Proximity to landfill (for ash disposal)

  • Proximity of fuel sources

  • Access roads

  • Traffic issues

  • Utilities proximity

This part of the algorithm categorizes the proposed sites on a qualitative way from the most preferred site to the least one. At the same time, it can be used also to exclude potential sites that do not conform to the majority of the criteria addressed in this part.

Size estimation model

The available solid wastes as feedstock in incineration plant with energy recovery, as it may be easily realized, constitute the ultimate parameter of WtE concept which also determines the size of the plant. Usually, the amount of wastes considered equals to a percentage of the ones generated in the area under evaluation. In some cases, this area can be as big as a region (Psomopoulos et al. 2013; Themelis et al. 2013). Once the potential amount of MSW to treated by the proposed facility is determined, the estimation of the calorific value constitutes the major factor that may reveal their ability to produce sustainable and clean energy. Ιt is the initial parameter which its estimation concludes to energy input of the system. Compounds with high net calorific value (NCV) result in high energy input and therefore increased power output (Ferdan et al. 2015; Murphy and McKeogh 2004; Themelis et al. 2013).

However, what should also be emphasized is the potential of heat users. It is not common sense for a plant to produce a specific power output based on the available MSW where fewer users are ready to exploit this power. Incorrect dimensioning of the plant may result in loss of energy production while installation and operation cost would not be coped with. Consequently, the estimation of the available MSW that can be used for energy recovery must be properly studied and guaranteed through the year in order to ensure the feasibility of the plant (Ferdan et al. 2015; Murphy and McKeogh 2004; Themelis et al. 2013). According to Perkoulidis et al. (2015), an estimation of the installed capacity of the plant (Y) can be achieved by using the next Eq. 1, where X is the amount of MSW:

$$ Y=9\cdot {10}^{-5}\cdot X-2.49 $$
(1)

Since there are several available technologies for thermal treatment of wastes including grate combustion, fluidized bed, RDF combustion, rotary kiln technology, as well as gasification processes such as pyrolysis and plasma, the most proven technology with wide range of implementations, is the grate combustion using as received MSW as feedstock, representing 80% of the installations worldwide. Consequently, in order to reach the most reliable results, the above mentioned technology has been selected in the validation process. The energy recovery by using this technology is at the range of 0.5–0.7 MWh per ton of MSW when the plant’s output is only power. In case of heat production, the energy recovery can be as high as 2 MWh. When a CHP (combined heat and power) scheme is implemented, the output of the plant can reach 0.5– 0.6 MWh electricity plus 1 MWh heat (Psomopoulos et al. 2013; Themelis et al. 2013). In the proposed algorithm, all the methods can be applied considering each time the technological limitations existing each time and the cost of implementation. Considering the data provided in Perkoulidis et al. 2015, the grate combustion method seems the most used method globally. This is a parameter that usually affects the decision making in favor of the technologies present that high implementation. Following the technological improvements as well as local special issues other technologies such as fluidized bed can also be applied very successfully (Themelis et al. 2013)

The energy contained in MSW when combusted can be converted into useful energy in the form of electricity, heat, and steam. Various WtE plants based on the potential market, they use the most appropriate technology to produce energy. Thus, globally, there are operational plants that their energy production is either in the form of electricity or heat as well as they use CHP technology in order to achieve both electricity and heat production. Energy Report III (status 2007–2010) issued by CEWEP (2012) reveals that across Europe, there are in operation 184 WtE plants using CHP technology, 83 plants that produce only electricity, and 47 plants that produce only heat. Accordingly, the mean energy recovery rate was 21.6% for plants only producing electricity, 77.2% for plants only producing heat, while the energy recovery rate for CHP plants was 15% for power and 37.5% for heat, respectively.

R1 energy factor estimation model

European Union, in an attempt to ensure the quality of energy production, introduced efficiency criteria specifically for the energy generated by MSW, in the energy sector at first. Thus, according to the Waste Framework Directive 2008/98/EU, an energy efficiency factor (R1 formula) has been established. When the values of this efficiency factor are over 0.60 that leads to a combined waste treatment method and energy recovery facility, otherwise the plant is characterized as disposal (D10), i.e., only waste treatment facility. It is also an energy factor that is based on energy balance of the under study facility and expresses the output power utilized, also determining the corresponding size of the facility. Thus, R1 formula focuses more on the increase of the exploitation of energy derived used by third parties (Chefdebien and Perron-Pique 2014).

The energy efficiency factor can be calculated from the following Eq. 2:

$$ {R}_1=\frac{E_p-\left({E}_f+{E}_i\right)}{0.97\cdot \left({E}_w+{E}_f\right)} $$
(2)

According to European Commission guidelines for the interpretation of the R1 energy efficiency formula for incineration facilities (2011), each indicator represents:

  • E p means annual energy produced as heat or electricity. It is calculated with energy in the form of electricity being multiplied by 2.6 and heat produced for commercial use multiplied by 1.1 (GJ/year).

  • E f means annual energy input to the system from fuels contributing to the production of steam (GJ/year).

  • E w means annual energy contained in the treated waste calculated using the net calorific value of the waste (GJ/year).

  • E i means annual energy imported excluding Ew and Ef (GJ/year).

  • 0.97 is a factor accounting for energy losses due to bottom ash and radiation.

Mean values of the above indicators, necessary for the estimation of R1 factor for the needs of this research are derived from the Energy report III (status 2007–2010), published by CEWEP (2012).

Economic analysis model—Gate fee estimation model

Since the aforementioned parameters contribute significantly towards the appropriate site and size selection, obstacles such as public acceptance and gate fee must also be taken into consideration. Although extracted energy is by far higher than utilized energy from extracted landfilling methane, the gate fee is much higher and also constitutes one of the major parameters that has to be examined, where its minimization may be provided as a strong asset for waste to energy development (Murphy and McKeogh 2004).

In order to formulate a comparative economic analysis, both expenses and revenues have to be investigated. Below expenses and revenues that should be considered in each implementation of the algorithm are presented. Furthermore, Table 1 presents the values of these parts of the economic analysis that were used for the validation of the algorithm.

Table 1 Expenses and revenues for the economic analysis of a WtE plant including indicative economic rates
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Basic expenses that should be included in each economic evaluation of a WtE facility are (Themelis et al. 2013):

  • Land cost

  • Construction cost

  • Operation and maintenance cost

  • Fuels cost

  • Transportation cost

  • Bottom ash treatment

  • Fly ash treatment

Basic revenue sources in WtE plants are as follows (Themelis et al. 2013):

  • Electricity

  • Heat

  • Steam

  • Carbon credits

  • Metal recovery

  • Gate fee

Although the above mentioned revenues constitute a considerable economic support for the project, what should also be properly calculated is the gate fee. It is a cost that is usually covered by local authorities but also it is dependable on the administrative structure of each country. Consequently, this gate fee represents the expenses for a proper waste treatment. Comparison of gate fee paid by the authorities between landfilling method and waste treatment with energy recovery reveals that the latest requires by far higher gate fees. According to (Psomopoulos and Themelis 2014), a waste-to-energy facility can be characterized as an attractive alternative option for waste treatment method when gate fee is considered low or government supported. Typical mathematical models used for the economic analysis, in order to estimate the economic feasibility of the project, are mostly based on the final determination of the gate fee. The evaluation of expenses and revenues of the plant, using several economic tools such as net present value (NPV), the internal rate of return (IRR) and the complete payback period (CPBP), will finally formulate the corresponding gate fee for the proposed area and thus the preferred size of the WtE facility. Commonly based on international experience, the higher the size of the WtE plant is the lower the gate fees are (Ferdan et al. 2015; Murphy and McKeogh 2004; Perkoulidis et al. 2015; Themelis et al. 2013)

Simulation model

The selected data, each time, will be processed by using the four sub-models, which are presented in section 2, above. The combination of these four sub-models in to one concrete and consolidated algorithm is presented in Fig. 1. The most suitable site and the size of the plant will be revealed through the interaction of the collected data, when compared to other locations, where the energy factor is the highest and the gate fee is the minimum possible.

When the potential sites have been identified in the first sub-model, the next sub-model is the evaluation of the appropriate size. Initially by inserting the value of available MSW along with net calorific value, the algorithm calculates the amount of primary energy inserted in the system. Next step is to select the combustion technology of the wastes. For the reasons described above, the algorithm is predefined to select the grate combustion technology automatically. Through a specific modification, the algorithm can allow more selections in this point, and this is the work executed during this period by the authors. The amount of energy input to the system has to be converted into useful energy production. Three options for selecting the most appropriate energy output is now available: electricity only production, heat only production, or combined heat and power (CHP). Moreover, the corresponding energy efficiency values of the conversion technologies have to be put in the next step of the algorithm. However, before the calculation of the energy production is achieved, the algorithm checks if there are any potential heat consumers before finally estimating the size of the plant. For instance, if the available amount of MSW has the potential to reach higher energy production than the one that can be delivered, this may result in over-dimensioning of the plant and therefore lead to increased costs (investment, operation and maintenance, transportation). However, in the case of electricity only production, the algorithm overcomes this process due to the fact that electricity production is usually totally absorbed from the grid.

The ability of the developed algorithm to estimate the energy efficiency factor R1 is based on the input values regarding the energy input to the system for steam production, the energy produced and used inside the facility, as well as the utilization factor of the output power. The data input to this step finally determines the R1 factor. When the R1 factor is estimated over the value of 0.65, the algorithm is moving to economic analysis of the plant in order to determine the gate fee. When the value is under 0.65 but over 0.60, it is required to change the utilization factor of the power output, and this happens due to the fact that increasing the utilization factor has a deep positive impact on the value of R1. However, in the case that the R1 factor is less than 0.60, the algorithm provides the opportunity to increase the efficiency of the energy conversion technology in order to increase the energy recovery rate. When all the processes have resulted in an energy factor less than 0.60, then the project is characterized as D10 (disposal only) and the algorithm ends. The final sub-model contains the economic analysis of the proposed site.

The main target is to estimate the value of the gate fee in the specific site, considering the potential expenses and revenues if the plant is installed there. Thus, when the estimation of R1 factor is equal or over 0.65, the economic analysis begins estimating the gate fee as a function of three financial parameters: IRR, NPV, and CPBP. The algorithm provides the opportunity to the user to decide which financial parameter could be the most suitable to estimate the gate fee. When the selected economic evaluation model is satisfied, the algorithm reveals the size of the plant, the energy efficiency factor R1, as well as the estimated gate fee for the proposed site.

Results and discussion

For the validation of the proposed method, the authors used data from a previous publication of Psomopoulos et al. in Psomopoulos et al. 2013 where data about potential sites and size for the development of WtE facilities in Greece were presented. Based on these data presented in the aforementioned publication, four Greek cities—Athens, Thessaloniki as well as Larisa and Volos have been selected. From this paper, a significant number of data were used. A higher utilization of the heat output can be exploited by a considerable number of industries which are active in these regions. The amount of MSW produced in Greece according to (EUROSTAT 2014) is almost 450 kg per capita, which 39% is generated in the wide area of Attica, while 19% is generated in Central Macedonia where the city of Thessaloniki is situated. Two waste management systems in Greece are used: landfilling and composting. The first one treats the 88% of the generated MSW while composting is used for the rest 12% (Psomopoulos et al. 2013; Stehlik 2009).

Athens is one of the largest cities in Greece and constitutes the capital of the country. The examination of the Athens City includes the wide region of Attica. The city is located in central Greece, it has an area of 3808km2 and covers 2.9% of the total area of the country. According to the last census (2011), the population of Attica was 3,752,973 (ELSTAT 2012). Being the city with the higher population, has resulted to high volumes of MSW generated, and the required treatment concerns about 2200 kt annually, with NCV about 9–10 MJ/kg. A portion of this quantity can be utilized in a WtE plant (Psomopoulos et al. 2013). Following suggestions in this paper (Psomopoulos et al. 2013), the proposed volumes to be treated in a WtE plant are 650 kt and 1 Mt annually. These quantities will be examined below.

Thessaloniki is the second largest city in Greece and according to the last census (2011), the population was 1,104,460 (ELSTAT 2012). The examination of the Thessaloniki city includes the wide region of Thessaloniki. It is located at the north of the country, covering an area of 3683 km2. It is also a city with high population and great amount of MSW are generated, reaching almost 971 kt per year with NCV about 9–10 MJ/kg (Psomopoulos et al. 2013). Also a portion here will be considered as potentially treated in a WtE facility taking also in account that wastes produced in more distant areas can be utilized partly also.

Central Greece includes two cities that will be investigated. The cities of Larissa and Volos. The amount of MSW generated annually in Central Greece is about 632,000 tons. According to the last census, the population in Larisa was 144,651 while in Volos was 125,248. Furthermore, the number of active industries in Larissa is slightly more than in Volos. Based on technical considerations for the sitting of a plant, the two proposed cities represent almost similar characteristics with minor differences regarding population and industries. In these terms, the implementation of a WtE facility with annual capacity 200,000 tons is proposed (Psomopoulos et al. 2013).

The waste amount of each city is significantly high, and wherever a waste-to-energy plant exists, the recycling rate increases too, as well as the possibility of adoption of another waste treatment method. Thus, the capacity of the power plant is intentionally lower, mainly for a proper dimensioning and operational feasibility. Also, the EU legislation on waste management hierarchy places the highest one in the waste prevention (Pires et al. 2011; Psomopoulos et al. 2013). Taking all these consideration into account, it can be easily concluded that the waste quantities reaching the WtE facilities will be less than the amount of the ones produced in the area under evaluation.

First, a number of locations were evaluated in each case. In all of them, it come as a result the area in the proximity of the existing landfills of each case, as these areas were the ones that covered all the required parameters set out by the algorithm. The same locations with small changes in the case of Athens were the ones selected and proposed in the manuscript by Psomopoulos et al. back in Psomopoulos et al. 2013. Following these evaluation, the algorithm was executed, and the results are presented below and were in good agreement with the previous studies for the selected areas (Karagiannidis et al. 2010; Psomopoulos et al. 2013)

In case of Athens, the annual capacity of the plant should be 960,000 t resulting in an installed capacity of 75 MWel or 218 MWth. When a CHP scheme is necessary, the corresponding size for Athens area should be 53 MWel and 90 MWth, while for Thessaloniki, the annual capacity of the plant should be 650,000 t resulting in 50M Wel, 145 MWth installed capacity. In case of a CHP scheme, the power output would be 36 MWel and 61 MWth. Finally, for the Central Greece, the annual capacity of tonnes per year should be 200,000 resulting in an installed capacity of 16 MWel, 46 MWth or 12 MWel–21MWth in case of CHP installation (Fig. 2).

Fig. 2
figure 2

Power output of WtE plants

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A plethora of utilization factors has been taken into account and used as the basic parameter for Ep value. The formulation of the energy efficiency R1 is highly connected to the utilization of energy output, as that is portrayed by the plethora of results which were obtained. In Table 2, the maximum energy efficiency factors R1 appear for each of the cities studied, and the corresponding utilization factors that values of R1 are achieved. As it is easily realized, higher utilization factors have resulted in higher values of R1 factor. However in case of electricity production only, authorization value of R1 requires higher utilization factor instead of CHP scheme where the authorization value (0.65) of R1 can be achieved with lower utilization factors.

Table 2 Results of energy efficiency factor (R1) and utilization factor
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What should also be emphasized in the forming of the R1 factor is the energy that is inserted into the plant in the form of fuel (Ef). In case where the level of feedstock is represented by unstable solid wastes through the year and the plant operator should maintain identical power or heat output due to already signed contracts, then greater fuel amounts will need to be inserted for the production of demanded energy. Thus, the specific plant will reach lower values of R1 factor, even less than the minimum authorization value, and therefore will be operating as an only disposal plant D10.

Finally, concerning the gate fee, both expenses and revenues have been evaluated through the economic analysis model. From the results obtained, the gate fee can be affected by several parameters. From a technical point of view, the higher the energy production has a positive influence. This could be a result of the installation of plants with high annual capacity such as in the case of Athens. However, it should always be accompanied with high utilization factors (Table 2). It does not make sense to produce large amount of energy without having the ability to sell it. From an economical point of view, the higher the selling prices, the lower gate fee is proposed. Furthermore, the output of the plant may formulate the final gate fee. Thus, it is obvious from the results that the lower gate fee is achieved when a CHP scheme is installed. By the implementation of a CHP scheme in a plant with high installed capacity, as in the case of Athens, the gate fee is the minimum possible (48€) (Fig. 3), and as the installed capacity of the plant is decreased, the gate fee is increased (108€). Concluding, in order for the gate fee to be reduced to the minimum possible, high utilization factors of the plant’s output is necessary to be achieved combined with increased installed capacity of the plants.

Fig. 3
figure 3

Gate fee based on plant’s size

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However, it would be interesting to investigate if the proposed algorithm may reach the same outcomes with another published research or how this may cover any gap in this field. The majority of the research interest has focused on the potential benefits that the use of waste to energy plant may achieve.

Energy report III (status 2007–2010), published by CEWEP (2012) researched the energy efficiency factors from 314 WtE plants which are in operation in 17 European countries (15 European member states + CH + NO). The amount of MSW used as feedstock in WtE plants was 59.4 million tons representing a share of 85.5% of the total amount incinerated in EU of 27 + CH + NO, in the year 2009. Results reveal that 206 plants meet the R1 criterion and are classified as energy recovery facilities. For a plant producing electricity only, it is too difficult to reach the R1 criterion and the mean values were in the range between 0.22 and 0.85. For a plant producing only heat, it is less difficult and the mean values are in the range of 0.21–1.05. Moreover, when the plant has installed a CHP scheme then it is by far easier to achieve the R1 criterion. In this case, the mean values are in the range of 0.23–1.45. Taking into consideration the size of the plant, small sized plants reach the lowest R1 factor with a mean value equal to 0.63 and large size plant reach higher R1 factor with a mean value equal to 0.77. According to the location, plants located in northern countries reach higher R1 factor than those located in Central Europe and much higher than those located in South Europe. Summarizing the results derived from this report, what should be concluded is the strong correlation between the types of energy recovery, the energy efficiency factor R1, the geographical location, and the size of the plant. As it may be easily realized is that the developed algorithm presented in this study has the ability to simulate all of the above mentioned characteristics and to provide secure results when a WtE plant is under investigation.

Ferdan et al. (2015) in their publication entitled “A Waste-to-Energy project: A complex approach towards the assessment of investment risks”, through the development of a gate fee prediction tool, conclude that for a WtE plant which only produces electricity with annual capacity of 400,000 t, the gate fee can be estimated in the range of 81–99€ per ton of MSW. Moreover, for the same power output of the plant but with annual capacity close to 200,000 t, the estimated gate fee is predicted in the range between 95 and 112€ per ton. According to the results obtained from this study, in case of the WtE plant suited in Tagarades of Thessaloniki for the almost same annual capacity and the same form of power output, the gate fee has been estimated at 92€ while in case of the plant suited in Central Greece with 200,000 t annual capacity, the corresponding gate fee has been estimated at 108€ (Table 3). As it may be easily realized is that the developed algorithm presented in this study can be confirmed from the above mentioned results.

Table 3 Gate fee for each form of energy output adopted
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Conclusions

This study for the first time is an attempt to concentrate the existing experience derived from various studies, in a simulation process, evaluating the major constraints that the implementation of a WtE facilities has to face, leading to a fast assessment tool for each one of these constraints. Thus, the development of a fast decision making tool in the form of an algorithm, taking into consideration various parameters has been achieved.

The model has simulated the change of sites according to the technical considerations provided and determined the total size of the plant. Accordingly, based on various utilization factors of the output energy either when delivered to third parties or for internal use as well as the amount of energy input in the form of fuel, it has simulated the estimation of the energy efficiency factor R1 that could be achieved in each of the investigated sites. Moreover, the algorithm by taking into consideration both expenses and revenues based on the estimated size of the plant, calculated the corresponding gate fee.

From the results obtained, the development of a concrete methodological approach can be feasible in order to assess either a Waste-to-Energy plant that is already in operation or to investigate if a proposed area can be of suitable location for the implementation of such a plant. In the case of a plant that is already in operation, data derived from measurement values will conclude to much more accurate results. On the other hand, when the implementation of a waste-to-energy plant is considered as a prompt solution for waste treatment, the developed algorithm can assess several potential sites concluding to the one that the possibilities for increased energy efficiency factors as well as the minimization of gate fee, are the highest possible.

Concerning the gate fee, both expenses and revenues have been evaluated through the economic analysis model. From the results obtained, the gate fee can be affected by several parameters. From a technical point of view, the higher the energy production has a positive influence. This could be a result of the installation of plants with high annual capacity such as in the case of Athens.

However, the weakness of the site model was the lack of available data in terms of heat and power consumption of the industries. These data could be useful to determine real utilization factors resulting to much more accurate outcomes of either size, R1 factor as well as gate fee. From the results obtained, the proposed algorithm can be applied in each of the selected sites or wherever is possible, also determining the appropriate size of the plant according to the potential of the site to reach maximization of R1 and minimization of gate fee.

This study has helped to answer, on one hand, of how we can cope the increased investment as well as the operation and maintenance cost of WtE facilities and on the other hand to investigate where the plant could reach the maximum energy efficiency factor R1, in order to be an attractive investment with the minimum financial and environmental risk. Consequently, it constitutes a partial solution in assessment of the above parameters, and a future work has to do with its conversion to a solid codified algorithm with more accurate and secure results.