Multi-Criteria Decision Analysis-Supported Evaluation of Biowaste Anaerobic Digestion Options in Slovakia ^†

Abstract

Slovakia’s biomethane production potential represents up to 10% of Slovakia’s natural gas consumption, which is largely unexploited. The aim of this paper is to develop a model of each available technology (continuous, dry batch, and wet batch) as well as that of a biogas treatment unit and evaluate the energetic, economic, and environmental potential of building a new anaerobic digestion plant in Slovakia, considering four plant locations with feedstock abundance within a 30 km perimeter. Feedstock composition and availability, energy integration, and product usability are evaluated. The applied multi-criteria decision analysis (MCDA) considers four evaluation criteria: return on investment (ROI), CO₂ emissions production, potential industrial biowaste revenue, and municipal density within the operational region. Biogas plant deployment analysis yielded the Levice facility as top-ranked, primarily due to its minimal environmental impact and superior logistical performance, closely followed by the Žilina, Michalovce, and Prešov facilities. When comparing biomethane production facilities, the Levice plant was excluded due to economic infeasibility, and the Žilina facility emerged as the optimal choice, particularly due to its superior ROI performance and the largest biomethane production potential of over 1 million m³ biomethane per year. Thus, biomethane station deployment in Slovakia has proved feasible and can enhance the energy self-sustainability of the country and contribute to meeting the decarbonization goals.

1. Introduction

Anaerobic digestion (AD) represents an environmentally sustainable alternative for the treatment of organic waste [1,2]. The process is conducted in either batch or continuous reactors, wherein organic substrates undergo transformation into biogas and digestate. Two primary technological approaches are differentiated based on total solids content in the feedstock: wet fermentation (<15% TS, total solids), and dry fermentation (>20% TS, total solids) [1,3]. Biogas typically consists of approximately 55% of methane, 40% of carbon dioxide, with trace quantities of nitrogen, oxygen, and hydrogen sulfide [4]. Biogas yield is substantially influenced by feedstock composition [5]. Organic waste streams, including food waste and sewage sludge, exhibit variable methane potentials ranging from 100 to 350 m³ CH₄/ton vs. (volatile solids) [6]. Seasonal variations in feedstock availability require strategic planning and storage solutions, particularly for agricultural-based biogas facilities dependent on harvest seasonal materials. Produced biogas can be utilized for combined heat and power generation [7,8], or for biomethane production, comprising multiple purification steps [9,10]. Various separation technologies and pre-separation methane enrichment methods are available [11,12,13]; however, membrane separation is gaining prominence primarily due to its high efficiency [14,15]. Patel et al. [11] recently reviewed the most common methods of gas separation applied to biogas purification, including membranes, absorption, adsorption, cryogenic separation, and biological techniques. They found that the specific energy consumption of membrane separation (0.2–0.35 kWh/Nm³ biomethane) represents an above-average value compared to that of other methods. However, the premium achievable biomethane purity and methane recovery, along with the ability to partially separate nitrogen and oxygen from biomethane, the absence of liquid waste streams, and low to zero heat consumption make it a preferred method for common applications. Kuczynski showed [16] high energy consumption in absorption and cryogenic separation technologies and methane losses when employing water scrubbing or absorption, while Aguilloso et al. [17] mentioned lower membrane maintenance costs due to fewer moving parts and zero consumption of chemical reagents or solvents. Jusoh et al. [15] came to a similar conclusion, evaluating specifically biogas desulphurization techniques, stressing that well-developed methods, such as absorption or absorption, come with several drawbacks (frothing, rusting, and heat consumption), which membrane-based separations can overcome at the expense of higher power consumption.

Currently, only two biomethane facilities are operated in Slovakia; however, the country possesses the potential to substitute 10% of its annual natural gas consumption with biomethane, amounting to approximately 500 million m³, of which around 300 million m³/year could be made a reality by 2030 [18]. Existing market demand and AD could provide an effective biomethane production pathway. Given Slovakia’s dependence on natural gas imports [19] and its persisting extensive use both in industry and space heating [20], biomethane production and market development can enhance domestic industrial resilience regarding energy security [18,21] in line with initiatives supporting biomethane solutions in Central and Eastern Europe [22]. Moreover, it can contribute to reaching the country’s fossil GHG (greenhouse gases) emission reduction goals [23].

Multi-criteria decision analysis (MCDA) allows for the integration of technical, economic, and environmental indicators within a unified model, supporting transparent decision-making in contexts characterized by multiple, often conflicting objectives (such as cost, performance, sustainability, and energy efficiency) [24]. In the renewable energy domain, MCDA has been increasingly used to support decision-making processes, including economic, technical, social, and environmental aspects of alternative energy technologies and systems, demonstrating its suitability in settings analogous to biomethane deployment [24,25]. Tahir et al. recently applied MCDA to the sustainability assessment of biogas plants, comparing Germany and Pakistan [26]. A similar approach was chosen in another study examining conflicting objectives in supporting biomethane plants deployment from a European perspective [27]. The potential of biogas and biomethane production in individual countries was analyzed by MCDA in the papers by Llano et al. [28], Pehlken et al. [29], or by Lenarczyk et al. [30].

Therefore, this study employs MCDA as a structured assessment framework enabling the systematic comparison and prioritization of AD system configurations, being the pilot study applying this approach to biogas and biomethane plant deployment in Slovakia to our knowledge. A search performed in SCOPUS using the words “biogas” AND “Slovakia” AND “decision” yielded only one relevant output [31] where the authors touched on the area of decision making via a questionnaire survey, while a search using “biomethane” instead of “biogas” yielded zero results. A less constrained search in SCOPUS, omitting the word “decision” yielded several tens of results, but only the paper by Belinska et al. [32] dedicated to Strengths, Weaknesses, Opportunities, and Threats (SWOT) and eco-economic analysis of biogas plant deployment can be deemed relevant to decision-making problems in biogas and biomethane production.

2. Materials and Methods

For the successful characterization of the anaerobic digestion process, the implementation of mathematical modeling is necessary. While numerous mathematical models are documented in the literature, the majority exhibit sensitivity to feedstock variation, a common operational challenge in AD systems throughout the year, particularly in facilities processing municipal organic waste. Consequently, Anaerobic Digestion Model 1 (ADM1) was selected to describe the process dynamics. However, implementation of the standard ADM1 often presents significant computational challenges due to its complexity. Therefore, a simplified ADM1 was employed, incorporating 10 biological processes and 13 components with their respective concentrations, stoichiometric coefficients, and kinetics.

Table 1. Components and processes are considered in mathematical modeling. X_acidogenesis = acidogenic bacteria concentration, X_acetogenesis = acetogenic bacteria concentration, X_metaAC = acetoclastic methanogens bacteria concentration, X_metaH2 = hydrogenotrophic methanogens bacteria concentration.

Process	Component
Disintegration	Simple particulate organic matter represented by a mixture of carbohydrates, proteins, and lipids
Hydrolysis	Composite particulate matter, which includes organic and inorganic materials in particulate form, is contained in waste and biomass
Acidogenesis	Particulate inert matter
Acetogenesis	Soluble organic inert matter
Acetoclastic methanogenesis	Soluble monomers, a mixture of sugars, amino acids, and long-chain fatty acids
Hydrogenotrophic methanogenesis	Mixture of organic acids—propionate, butyrate, and valerate
Xacidogenesis Decay	Acetate
Xacetogenesis Decay	Methane
XmetaAC Decay	Hydrogen
XmetaH2 Decay	Acetogenic bacteria
	Acidogenic bacteria
	Acetoclastic methanogens bacteria
	Hydrogenotrophic methanogens bacteria

summarizes the mentioned components and processes.

Disintegration, biomass decay, and hydrolysis were modeled using first-order kinetics. Michaelis–Menten and Monod kinetics were applied to the rest of the processes [33]. The above mentioned model was implemented for both continuous stirred tank reactor (CSTR) configurations and CSTR cascade systems, which approximate plug flow reactor (PFR) behavior for both dry (20% total solids) and wet (10% total solids) processes [34].

For biogas-to-biomethane upgrading, two membrane modules arranged in series were considered, with gas compression to 0.8 MPa prior to the first module. The estimated pressure differential between modules was 0.1 MPa. Biogas entering the separation unit was assumed to contain 60% CH₄ and 40% CO₂, with a molar flow rate of 10 mol/s. MATLAB R2019b Simulink program was used for both biogas and biogas-to-biomethane mathematical modeling.

Table 2. Parameters of tested membranes, permeability data were retrieved from [36].

Membrane	CH4 Permeability, Barrer	CO2 Permeability, Barrer
Polyimide	0.25	10.7
Cellulose acetate	0.21	6.3
Polycarbonate	0.13	4.2
Polysulfone	0.25	5.6

displays the investigated membrane materials and their permeabilities. Details of process modeling can be found in previous work [35].

Biogas production units coupled with cogeneration units produce 4 main products: power, heat, liquid digestate, and solid digestate pellets. Economic evaluation employs the following assumptions:

• Produced electricity value, depending on installed power output: <500 kW: 152.36 EUR/MWh; for installed power between 500 and 1000 kW: 139.40 EUR/MWh; for >1 MW of installed power: 128.78 EUR/MWh [37];
• Average produced heat value: 12 EUR/MWh [38]; heat sale throughout the year is assumed as the plants are to be built in industrial areas;
• Average liquid digestate value of 5 EUR/t [39];
• Solid digestate pellets average value of 150 EUR/t [40].

Annual working time of 8160 h is considered. The selling price of biomethane consists of two parts—the market price of natural gas, which varies from 40 to 50 EUR/MWh in the long term, and the biomethane certificate price, which depends on several factors [37]. The market price of natural gas used for the biomethane plant economic calculations is from the end of March 2025: 42.9 EUR/MWh [41]. Biomethane certificate price is estimated to be 25 EUR/MWh. Purchase costs for major equipment units were estimated using [38]. Total investment costs were obtained by the factor method, multiplying the cost of major equipment by a sum of factors of 2.6. The operation of an AD plant for 25 years was considered. An inflation rate of 4%, a tax rate of 19%, and linear depreciation of all assets to be completed in 9 years were assumed. Other details on economic evaluation, including operation and maintenance costs estimation, are provided in [35].

MCDA applied in this study required defining suitable alternatives for evaluation. Therefore, a preliminary geographic assessment was performed to identify potential locations for a new AD unit in Slovakia. Candidate regions were selected based on ROI, emission production, logistics (number of municipalities in the region), and potential (amount of produced industrial biowaste in the whole county). Biowaste generation was evaluated across all Slovak regions based on underlying data published in 2023 [42], and areas exhibiting the highest technically recoverable biowaste within a 30 km transport radius were shortlisted. Considering these criteria, four regions were selected as feasible alternatives: Žilina, Levice, Prešov, and Michalovce. Figure 1 describes the spatial distribution of these candidate locations within Slovakia.

The amount of biowaste available in the selected areas is summarized in Table 3. The potential amount corresponds to total regional biowaste generation, whereas the realistic amount represents the fraction currently separated and accessible for AD treatment (<34% of total municipal biowaste). Assessment of the examined alternatives was conducted following the principles of MCDA. To ensure transparent and consistent comparison, the analytic hierarchy process (AHP) was adopted as the main evaluation framework, which is a well-established approach in engineering decision-making.

The conceptual basis of AHP was originally formulated by Saaty [44]. In this work, a modified AHP method introduced by Janošovský et al. [45] was implemented to reduce subjectivity in criteria weighting. Instead of relying on expert-defined preferences supplemented with sensitivity analyses and testing scenarios [26,28,30], the modified version applies a consistency-driven algorithm that systematically explores feasible combinations of criterion weights. This procedure enhances methodological transparency, reduces in consistency, and improves the efficiency of the overall evaluation process. A detailed description of the developed method can be found in previous work [46].

Table 3. Amount of biowaste produced in selected regions calculated based on data available in [47].

Location	Potential Amount, t/Day	Reality (2021), t/Day
Žilina	190.3	64.0
Levice	54.4	18.3
Prešov	146.9	49.4
Michalovce	140.7	47.3

Table 3. Amount of biowaste produced in selected regions calculated based on data available in [47].

Location	Potential Amount, t/Day	Reality (2021), t/Day
Žilina	190.3	64.0
Levice	54.4	18.3
Prešov	146.9	49.4
Michalovce	140.7	47.3

3. Results and Discussion

3.1. Anaerobic Digestion Modeling and Biogas Plants Assessment

Figure 2 displays a batch-type anaerobic digestion simulation over 40 days. The double exponential trend of biogas production is caused by two bacterial families activity occurring at different times. First, hydrogenotrophic methanogenesis occurs, followed by acetate consumption. Comparable trends in biogas yields have been reported in multiple studies in the literature [48,49,50,51]. The dry regime shows a higher specific biogas yield and biogas volumetric flow rate compared to the wet regime. Especially, lower hydraulic retention time (HRT) and higher loading rates favor a dry technological approach over the wet one.

Given that biogas is converted to electricity and thermal energy through cogeneration systems, elevated specific biogas yields directly correlate with enhanced production of both energy products, consequently resulting in improved economic returns. Furthermore, higher biogas yields significantly reduce the required digester volume, thereby decreasing capital infrastructure requirements [33,34].

The assessment of emissions during anaerobic digestion (AD) constitutes a complex challenge requiring comprehensive life-cycle analysis. However, the production method represents a more critical factor in emission evaluation than the biogas yield itself. The European Parliament and Council Directive 2018/2001 establishes that emissions from biogas utilization in cogeneration systems may be quantified based on the production pathway employed. Under conditions of closed digestion utilizing biowaste as feedstock without external thermal or electrical energy input, emission reductions of 84% can be achieved relative to untreated biogas scenarios [52].

The results of continual AD simulation are displayed in Figure 3. Compared to batch-type AD, the continuous process shows visibly higher biogas production due to the rectangular shape of the digester, enhancing various factors, such as heat exchange.

The abovementioned mathematical model was used to determine actual biogas flow rates in each location. Summarized values for individual technological regimes are displayed in

Table 4. Summarized biogas volume flow rates in each location. Thermo = thermophilic regime.

Technological Regime	Žilina, Nm3/d	Levice, Nm3/d	Prešov, Nm3/d	Michalovce, Nm3/d
Dry thermo.	9380	2695	7248	6935
Wet thermo.	10,173	2909	7863	7521
Continual thermo.	11,380	3253	8766	8409

. Subsequently, MCDA was applied to identify the most convenient location based on selected criteria, such as ROI, emission production, logistics—number of municipalities in the region, and potential—amount of produced industrial biowaste in the whole county, since determining the exact value in the selected region was not possible. The results of MCDA for the candidate regions are shown in Figure 4, illustrating their comparative evaluation.

Levice AD facility emerged as the most favorable option (Figure 5), primarily due to its superior performance in emission reduction and logistical efficiency. The Žilina facility was second, particularly due to its substantial capacity for incorporating industrial biowaste as feedstock. Despite exhibiting the highest ROI, the Michalovce facility was relegated to third position due to suboptimal performance across the remaining evaluation criteria.

3.2. Biogas to Biomethane Upgrade and Biomethane Plants Assessment

Biogas to biomethane upgrading was proposed by gas compression and membrane separation, considering only two components—CH₄ and CO₂. In most cases, biomethane is stipulated to contain minimally 95% of methane. Various membrane materials can be used; however, according to multiple sources, polymers show the best results [47,53]. Figure 5 presents performance characteristics of four selected membrane materials. The polyimide membrane provided satisfactory methane purity (>95%); however, it exhibited the lowest biomethane yield among the tested materials. Nevertheless, experimental validation is required to establish definitive conclusions regarding membrane performance.

Biomethane production was assessed for each region. Individual volumetric flow rates are presented in

Table 5. Summarized volume flow rates and membrane areas in each location.

Location	Membrane Area, m2	Biomethane Flow Rate, Nm3/Year	Total Flow Rate, Nm3/Year
Žilina	2 modules á 13.5	1,062,231	1,094,098
Levice	2 modules á 2.5	299,226	308,801
Prešov	2 modules á 9.9	840,535	868,272
Michalovce	2 modules á 9.5	793,965	820,166

, where the total flow rate represents the combined biomethane flow enhanced with propane addition for heat value optimization.

Finally, individual facilities were assessed using MCDA (Figure 6). The Levice facility was excluded from the analysis due to economic infeasibility resulting from small biomethane production potential compared to other locations. As demonstrated in Figure 6, the Žilina region exhibited the highest suitability for biomethane production, primarily due to its superior ROI. The Michalovce facility ranked second overall, demonstrating the lowest environmental impact among the evaluated options. The Prešov region consistently ranked lowest across all assessment categories.

Tahir et al. [26] argue that, despite the spatial placement of a bioenergy plant, it must be productive and efficient in terms of substrate resources so that the essence of reducing emissions and fossil consumption does not become lost. This is in line with our findings that the best performing plants, Žilina and Michalovce, were well-supplied with easily available feedstock and found placement for all products.

In addition to logistics, product placement, and efficiency issues, the building of new biogas plants in Slovakia and the operation of existing ones are hindered by low public acceptance [54], lack of trust [55], split land ownership, and lack of cooperation of waste producers, collectors, and local authorities on a regional scale [23]. All these belong to external factors that might be included in our further studies via choosing and evaluating suitable socioeconomic indicators, learning from recently published focused analysis [56] dedicated to AD plants perception in Slovakia and Czechia.

4. Conclusions

Dry thermophilic AD demonstrated the highest biogas production rates and, in combination with additional operational advantages such as reduced digester volume requirements and lower HRT, establishes dry AD as the superior regime compared to wet fermentation processes. Biogas upgrading to biomethane through membrane separation technology was proposed. Various membrane materials were evaluated, of which only polyimide membranes exhibited satisfactory results. Moreover, facility location was assessed considering multiple criteria, including biowaste generation, existing AD infrastructure, and potential industrial biowaste revenue streams. Four locations with 30 km operational radius were selected: Žilina, Levice, Michalovce, and Prešov.

MCDA was employed to determine the most favorable location for both biogas and biomethane facilities based on four evaluation criteria: ROI, emission production, potential industrial biowaste revenue, and municipal density within the operational region. Among biogas plants, the Levice facility ranked first, primarily due to its minimal environmental impact and superior logistical performance. The Michalovce facility ranked third despite achieving the highest ROI score. Finally, MCDA was applied to compare biomethane production facilities, with the Levice plant excluded due to economic infeasibility. Here, the Žilina AD facility emerged as the optimal choice, particularly due to its superior ROI performance, followed by the Michalovce facility. Inclusion of socioeconomic indicators in further studies may offer a novel look at the deployment of biogas and biomethane stations.