Next Article in Journal
Comparative Approaches to Energy Transition: Policy Guideline for Enhancing Thailand’s Path to a Low-Carbon Economy
Next Article in Special Issue
Valorization of White Lupin Straw Through Mild Dilute Acid Hydrothermal Treatment: A Sustainable Route for Monosaccharide and 5-Hydroxymethylfurfural Production
Previous Article in Journal
Numerical Investigations on the Enhancement of Convective Heat Transfer in Fast-Firing Brick Kilns
Previous Article in Special Issue
Valorization of Spent Mushroom Compost Through a Cascading Use Aproach
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Energies
Volume 17
Issue 22
10.3390/en17225618
energies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Examining the Potential of Biogas: A Pathway from Post-Fermented Waste into Energy in a Wastewater Treatment Plant

by
Krzysztof Michalski
1,
Magdalena Kośka-Wolny
1,2,
Krzysztof Chmielowski
3,
Dawid Bedla
4,
Agnieszka Petryk
5,
Paweł Guzdek
6,
Katarzyna Anna Dąbek
3,
Michał Gąsiorek
7,*,
Klaudiusz Grübel
8 and
Wiktor Halecki
9
1
AQUA S.A. ul. 1 Maja 23, 43-300 Bielsko-Biała, Poland
2
University of Economics and Humanities, ul. gen. Wł. Sikorskiego 4-4c, 43-300 Bielsko-Biała, Poland
3
Department of Natural Gas Engineering, Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland
4
Department of Ecology, Climatology and Air Protection, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Kraków, Poland
5
Department of Space Management and Social-Economic Geography, Krakow University of Economics, 31-510 Kraków, Poland
6
Faculty of Environmental and Energy Engineering, Krakow University of Technology, 31-155 Kraków, Poland
7
Department of Soil Science and Agrophysics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Kraków, Poland
8
Department of Environmental Protection and Engineering, University of Bielsko-Biala, Willowa 2, 43-309 Bielsko-Biała, Poland
9
Institute of Technology and Life Sciences—National Research Institute, Falenty, Al. Hrabska 3, 05-090 Raszyn, Poland
*
Author to whom correspondence should be addressed.
Energies 2024, 17(22), 5618; https://doi.org/10.3390/en17225618
Submission received: 25 September 2024 / Revised: 31 October 2024 / Accepted: 6 November 2024 / Published: 10 November 2024
(This article belongs to the Special Issue Sustainable Biomass Energy Production and Utilization)
Browse Figures
Versions Notes

Abstract

:
Biogas has improved due to technological advancements, environmental awareness, policy support, and research innovation, making it a more cost-effective and environmentally friendly renewable energy source. The Generalized Linear Model (GLM) was employed to examine the relationship between purchased and generated energy from 2007 to 2023. Metrics such as deviance, log likelihood, and dispersion phi were examined to assess model fit. The Mann–Kendall test was utilized to detect trends in energy datasets. Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) reduction was significant, exceeding 97% from 2014 to 2023. However, treated sewage displayed limited susceptibility to biological degradation, with COD to BOD5 ratios increasing from 2.28 to 6.59 for raw sewage and from 2.33 to 7.05 for treated sewage by 2023. Additionally, the efficiency of sewage purification processes was calculated, and multivariate regression analysis was conducted on gas composition data. Principal Coordinate Ordination (PCO) and k-means clustering were used for dimensionality reduction and biogas component clustering, respectively. This research showed that biogas from the waste water treatment process can be used, particularly in methane production. Technological advancements have made biogas production more efficient, enhancing energy generation within a circular economy framework.

1. Introduction

Modern wastewater treatment plants are crucial components of infrastructure, purifying water of contaminants before it is reused or released into the environment [1]. Recently, artificial intelligence modeling has also been incorporated [2]. Biomethanation occurs within anaerobic reactors, where living organisms decompose organic substances in the absence of oxygen [3,4,5]. This natural process results in the emission of methane and carbon dioxide, which are then extracted from the residual waste [6,7]. In wastewater treatment facilities, biomethanation offers significant advantages due to the substantial organic content present in wastewater, ideal for biogas production [8,9]. Throughout the wastewater treatment process, biogas is generated by treating sewage sludge, a byproduct of water purification [10]. Stored in anaerobic reactors, this sludge undergoes decomposition by microorganisms, yielding biogas. Subsequently, the biogas is purified to yield high-quality biomethane [11]. One of the significant challenges associated with biomethanation is the efficient utilization of the produced gas [12]. Methane is a highly valuable gas, but it requires an appropriate distribution and storage system. For biomethane, which is produced in smaller quantities than natural gas, it is essential to ensure suitable conditions for its distribution and storage to enable its effective use [13]. Biomethanation has many advantages. Firstly, it is an environmentally friendly process that reduces the amount of organic waste, such as food scraps and agricultural residues. Secondly, biomethane can be used as a green energy source, contributing to the reduction in greenhouse gas emissions. Thirdly, the biomethanation process can help develop new energy sources in rural areas [14]. The biomethanation process consists of two stages: hydrolysis and fermentation [15]. Hydrolysis is the process by which large organic molecules are broken down into smaller compounds such as sugars, organic acids, amino acids, and glycerol. This process is catalyzed by enzymes present in microorganisms. Fermentation is the second stage of biomethanation, during which sugars, organic acids, and amino acids are converted into a gas composed of methane and carbon dioxide. Methane is produced through the reaction between microorganisms and carbon dioxide. The entire process takes place in special biogas reactors [16,17]. Anaerobic co-digestion (AcoD) can utilize the spare fermentation capacity in existing wastewater treatment plants (WWTP) to produce excess biogas beyond the internal energy needs of the treatment plant. It is expected that, through the combination of AcoD and biogas upgrading, more wastewater treatment plants will become net energy producers [18]. Biomethanation also helps reduce greenhouse gas emissions because biogas is produced in a sustainable and renewable manner, contributing to the reduction in fossil fuel consumption [19].
In wastewater treatment plants, biomethanation allows for the use of sewage sludge to produce biogas, enabling the generation of both electrical and thermal energy and reducing the amount of waste produced by the treatment plants [20]. In studies, process water (PW) was treated using coagulation, and the generated sludge after treatment (GSAT) was converted into methane-rich gas. Coagulation and anaerobic digestion were combined to effectively sanitize PW and convert the by-products of coagulation into biogas. Wastewater treatment plants are key facilities in cities that ensure water reaches the required quality before being returned to the environment. Their energy consumption is the most critical cost of their operation, although the sludge produced in the treatment process can be subjected to anaerobic digestion to produce biogas [21,22,23,24]. This biogas is typically burned in a cogeneration reciprocating engine, which generates electricity fed into the grid and the heat necessary to maintain the required temperature in the digester. This energy recovery technique also prevents direct methane emissions from biogas into the atmosphere [25]. The growing demand for water and energy has highlighted the importance of energy-efficient anaerobic wastewater treatment; however, anaerobic effluents still contain a significant portion of total CH4 production and are discharged into the environment without being utilized as a valuable energy source. The importance of long-term assessments of biogas production’s environmental impacts, including greenhouse gas emissions, is crucial for improving biogas production and wastewater treatment [26]. Studies should explore a wider variety of organic substrates, including urban organic waste [27] and industrial by-products, to enhance methane yield and optimize resource use [28]. Future research should also utilize sewage sludge or manage wastewater treatment for biogas production [29].
Research on energy production efficiency from wastewater treatment processes remains limited despite widespread biogas production reports from agricultural sources. This study aimed to achieve four objectives: (i) establishing a correlation between biogas composition and its calorific value; (ii) assessing efficiency indicators of the wastewater treatment process, focusing on Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD); (iii) evaluating the energy production potential derived from biogas post-wastewater treatment, considering biogas components and their utilization; (iv) analyzing trends in potential energy generation following waste material fermentation.

2. Materials and Methods

2.1. Characteristics of the Research Object

The wastewater treatment plant in Komorowice is located in the Silesian Voivodeship, Poland. In addition to the inflow of wastewater, the system includes a receiving station with wastewater storage tanks. There is also an inflow from stormwater overflow on the left bank of the Biała River at kilometer 9 + 760 along its course. The wastewater treatment plant is a technologically advanced facility divided into two main sections: wastewater treatment and sludge treatment. It operates as a mechanical–biological treatment facility utilizing anaerobic sludge processing, primarily based on the activated sludge method.
At the wastewater treatment plant, sludge undergoes a fermentation process that produces biogas, which is then used as fuel in cogeneration units to generate both electricity and heat. The maximum wastewater discharge capacity of the plant is 1.44 m3/s, with an average daily discharge of 124,800 m3. Additionally, the local company manages a sewage network exceeding 1290 km across the municipality, operating 43 sewage pumping stations. Combined, these facilities have a total treatment capacity of 138,000 m3 per day and process an annual volume of approximately 32.85 million m3 of wastewater.
The treatment plant is equipped with two mechanical belt thickeners and two gravity thickeners, which are used to concentrate excess sludge before directing it to the fermentation chambers. The sludge from the gravity thickeners is combined with the sludge received from the waste reception station and the sludge thickened by the belt thickeners before being sent for fermentation. The introduction of sludge to the thickeners is carried out using sludge feed pumps, while the transfer of thickened sludge to the fermentation chambers is performed using thickened sludge pumps. Mechanical thickening of the sludge requires the addition of a polyelectrolyte, which is prepared at the polyelectrolyte station and added via polymer pumps. After mixing with the polyelectrolyte, the excess sludge is introduced onto the filter belt. The thickening process using thickeners occurs through gravitational means.

2.2. Description of the Technological Process at the Wastewater Treatment Plant

The type of waste introduced into the biogas production process was municipal waste. The substrate for fermentation primarily consists of thickened excess activated sludge. Additionally, waste brought to the processing station is also included in the fermentation process. The daily amount does not exceed 100 t/d, which can account for a maximum of up to several percent of the volume of the excess sludge stream. Raw sewage passes through a gravel trap, then it proceeds to screens and sand washers to capture larger impurities, including debris. In the subsequent stage of the process, mineral pollutants are removed through sedimentation in the sand trap. In the next step, there is the process of pumping the preliminarily treated sewage and sludge. Later, there is a gravitational sludge thickener, where excess sludge is thickened for fermentation chambers. The effluent in the co-fermentation process consisted of pre-dewatered sewage sludge and grass clippings from mowing residential gardens. The bioreactor is utilized for purification in biological processes, where sewage aeration occurs. Additionally, to reduce nitrate nitrogen, in each of the final nitrification chambers, a sediment transfer system to the denitrification zone is installed—internal recirculation is performed by pumping mixers (a total of 3 pieces), dependent on the concentration of nitrate nitrogen in the denitrification zones. After the aeration process, the sewage is directed to the coagulant dosing station, where the addition of PIX is conditioned by the analysis of phosphates at the outlet after the secondary settler. The final clarification of sewage occurs in a secondary settling tank equipped with sludge scraping devices. Excess sludge, in turn, is recirculated. During periods of intense rainfall, the stormwater settler also operates. The technological line also includes a retention tank for delivered sewage and emergency tanks storing excess sewage during periods of heavy rainfall. The general plan of the technological procedure is depicted in Figure 1.
The Komorowice Wastewater Treatment Plant uses the activated sludge method for treatment. Sludge is fermented to produce biogas, which is then directed to a storage tank through a desulfurization unit that utilizes activated carbon. The carbon is replaced when the hydrogen sulfide levels in the exiting biogas exceed acceptable limits. Additionally, devices for removing silicon compounds from the biogas are installed. The biogas storage tank is a membrane tank with a capacity of 2100 m3, designed in the shape of a truncated sphere using UV-resistant PVC-coated polyester. The operating pressure in the tank is maintained at 15 mbar (1.5 kPa) by creating a slight overpressure in the space between the outer and inner membranes. The biogas installation includes connection chambers with automatic drainage systems. A biogas flare with a capacity of 350 Nm3/h features a concealed combustion flame and is designed to burn off excess biogas produced or to handle failures in the cogeneration units. The flare activates automatically when the tank reaches 95% capacity, leading to the automatic supply and ignition of biogas, and extinguishes when the tank capacity drops to 90%. The biogas collected in the gas tank is fed into the cogeneration unit building, which houses units with electric powers of 450 kW and 365 kW. Biogas is burned in cogeneration units to generate electrical energy for the external grid and thermal energy for heating sludge treatment devices, meeting approximately 40% of the wastewater treatment plant’s energy needs. Additionally, biogas is used to produce self-generated electricity that powers the technological equipment of the plant and provides thermal energy needed to heat the fermentation chambers and warm the sewage sludge within them. The heat source consists of waste heat generated by the two cogeneration units and a local boiler house equipped with two biogas-fired boilers. When biogas supply is insufficient, the boilers can also utilize natural gas as an alternative fuel for heating.

2.3. Statistical Analysis of Research Results

We explored the relationship between purchased energy and generated energy using the Generalized Linear Model (GLM), which offers a more advanced way to analyze this connection compared to simpler linear models. By selecting the identity function within the GLM, we could appropriately model the data, considering its distribution from 2007 to 2023. In our analysis, the intercept (b) played a crucial role, indicating the level of generated energy when purchased energy is at zero. Its high value (3,218,400) suggests there are fixed costs or a minimum level of activity required for energy generation. To assess how well our model fits the data, we examined metrics like deviance and log likelihood. Lower deviance values suggest a better fit, while higher log likelihood values indicate a stronger match. Additionally, we considered dispersion phi, which measures how spread out the data points are around the regression line, with higher values indicating greater dispersion. The G statistic and p(slope = 0) are crucial for gauging the statistical significance of the slope coefficient. A higher G statistic reflects a more significant deviation of the slope coefficient from zero, while a lower p(slope = 0) value indicates increased statistical significance.
Moving to trend analysis, the Mann–Kendall test serves as a valuable non-parametric tool for detecting trends in temporally or spatially ordered data. In our study, we applied this test to both generated and purchased energy datasets. A larger S value signifies a stronger trend, while the Z statistic measures the deviation from the expected value in the absence of a trend.
Based on the physicochemical indicators of raw and treated sewage from the years 2014–2023, the efficiency of the purification process was calculated using the results of the biological breakdown of organic matter. Additionally, the ratios of COD/BOD5, COD/TN, COD/TP, BOD5/TN, and BOD5/TP were calculated. Multivariate regression analysis was conducted, with the calorific value as the dependent variable and the gas composition, including carbon dioxide, hydrogen sulfide, methane, and oxygen, as the independent variables. Principal Coordinate Ordination (PCO), also known as Multidimensional Scaling (MDS), is a dimensionality reduction technique similar to Principal Component Analysis (PCA) but primarily used for distance or non-Euclidean data. PCO is employed when data are in the form of a distance matrix between observations, aiming to reduce the dimensionality of data while minimizing the loss of information about distances between observations. The data collected for biogas composition included methane, carbon dioxide, oxygen, and hydrogen sulfide, along with the heating value. To analyze these data, the k-means method was used for clustering. In this context, WGSS (Within-Group Sum of Squares) calculates the sum of squared distances within each cluster. Essentially, it gauges how similar the objects within a cluster are to each other. It is computed by summing up the squared distances between each data point and the centroid of its cluster. A lower WGSS value indicates tighter, more homogeneous clusters, reflecting how compact they are. Minimizing WGSS is a common objective in the optimization process of k-means clustering, aiming to achieve more cohesive groupings of data points. The analysis was performed using the PAST 4.17 software. Moreover, a box-whisker plot was created in Excel 2019 for the dominant components produced in the biogas production process.

3. Results

3.1. Parameters of Raw and Treated Sewage

The analysis presented focuses on the operation of the wastewater treatment plant over the years 2014–2023. During this period, the highest average BOD5 value was 303 mg·dm3, and COD was at 722 mg·dm3 in the year 2022. It was similar for TSS and total nitrogen. Meanwhile, for total phosphorus, the maximum average annual value reached 5.9 mg·dm3 in the years 2018 and 2021 (Table 1).
Considering the same research period, the highest average annual value of BOD5 in treated sewage was 6.6 mg·dm−3, while COD was at 46.5 mg∙dm−3 in the year 2023. Similarly, for TSS, the maximum average annual value was 11.4 mg∙dm−3 in the first year of the study (2014). For total nitrogen, a value of 8.78 mg∙dm−3 was recorded twice in the years 2015 and 2023. Meanwhile, for total phosphorus, the highest average annual value reached 0.6 mg∙dm−3 in the year 2016 (Table 2).
The highest value of the average annual sewage inflow reached 21.550867 million m3 in 2020. Meanwhile, the maximum quantity of sewage delivered to the purification process was 105.000 m3∙year−1 in the last year of the study (Table 3).
In most cases, the reduction in BOD5 and COD was significant. For example, in 2014, it was over 97%, similar to 2023. However, treated sewage showed poor susceptibility to biological degradation. For example, the COD to BOD5 ratio for raw sewage was 2.28, and for treated sewage, it was 6.59. In 2023, the ratio for raw sewage was 2.33, and for treated sewage, it was 7.05 (Table 4).
The trend analysis showed a good fit for the fermentation process of utilized waste, with an R2 value of 0.68. Analyzing the amount of waste from wastewater treatment plants directed to fermentation, a significant increase is evident, particularly during the years 2020–2023 (Figure 2).

3.2. Energy Analysis from Biogas Production

Analyzing the figure, it is clear that more energy was purchased than was generated. Since 2011, there has been an increase in generated energy from 200,000 to 400,000 annually (Figure 3). Generally, methane dominated the composition of the resulting biogas (with a median of 61.6%). Carbon dioxide was the second most abundant component with a median of 37.7% (Figure 4). Additionally, hydrogen sulfide was detected in the biogas, with a median concentration of 50 ppm, and the oxygen content was less than one percent. In the examined composition of produced biogas. the calorific value systematically increased with the rise in methane levels. A linear trend was observed with an R2 value of 0.92 (Figure 5). However, no such correlation was noted for CO2 concentrations.
Analysis of electricity consumption and production data spanning from 2007 to 2023 unveils notable trends. Both purchased and generated electricity experienced consistent growth over the years. Notably, the proportion of self-generated energy within total consumption surged from 18% in 2007 to 41% in 2023. The arithmetic mean of this self-generated energy share stood at approximately 31.52%, with a standard deviation indicating variations of around 7.23% around this mean. The median of the share of self-generated energy was slightly higher, at around 32.78%. The variance is relatively low, indicating stability in the shares of self-generated energy over the analyzed period. Skewness for purchased energy is minimal, but for generated energy, it is negative, suggesting asymmetry in the data. Since 2007, there have been upward trends in self-generated energy production, indicating potential economic or ecological benefits. Additionally, the increasing share of self-generated energy in overall consumption suggests a greater utilization of renewable energy sources or more efficient energy systems. During the initial period of the study, waste deliveries for co-fermentation did not exceed 5,000 tons. A significant increase in waste deliveries has only been noticeable since 2020 (Figure 6)
The results of Mann–Kendall test for purchased energy (S = 30, Z = 1.30, p = 0.19) suggested that there is no statistically significant trend. The probability of a trend is relatively high (0.19167), indicating a lack of convincing evidence for a trend in the purchased energy data. The results of Mann–Kendall test for generated energy (S = 60, Z = 2.65, p = 0.0078) indicated the presence of a statistically significant increasing trend. The p-value is significantly lower compared to purchased energy, suggesting convincing statistical evidence for an increasing trend in the generated energy data.
PCO, similar to PCA, considers linear correlations between variables, but its focus is on distances between observations rather than variable variance. In the PCO results, 93.99% of variance was observed for the first coordinate axis. with 5.98% for the second (Figure 7).
Our results in the context of k-means clustering showed that an F-statistic of 1.58 suggested the clusters were relatively similar to each other, with higher F values typically indicating better-separated clusters. The variance of 61.314 pointed to a high level of variability in the data. The average silhouette score of 0.56 indicated that the clusters were moderately well separated, as this metric measured how well objects fit within their clusters, with values ranging from −1 to 1. A score of 0.56 suggested moderately good clustering quality, where values close to 1 denoted well-separated clusters, values near 0 indicated overlapping clusters, and values below 0 implied misclassified objects. Lastly, the WGSS (Within-Group Sum of Squares) value of 2.311 indicated that the clusters were relatively compact and homogeneous (Figure 8).
The positive slope of 0.012 with a small standard error suggests that methane has a significant positive effect on the dependent variable. The high correlation coefficient (r = 0.96) indicates a strong positive linear relationship between methane and the dependent variable—the calorific value. The negative slope of −0.0041 with a relatively large standard error suggests that carbon dioxide has a significant negative effect on the dependent variable. The correlation coefficient (r = −0.26) indicated a moderate negative linear relationship. The slope and error are both zero. indicating that there is no relationship between oxygen and the dependent variable. The positive slope of 0.080 with a moderate standard error suggests that hydrogen sulfide has a significant positive effect on the dependent variable. The correlation coefficient (r = 0.30) indicated a moderate positive linear relationship (Table 5).

4. Discussion

4.1. Biogas and the Circular Economy

The imperative of the circular economy is increasingly evident in waste management and biogas utilization. In our investigation, a substantial volume of waste underwent fermentation, notably including an escalation in wastewater treatment waste by 2000 Mg annually, as indicated by an R2 of 0.68 (Figure 2). Addressing energy management emerges as paramount, urging the exploration of techniques fostering enhanced energy efficiency. While our analyses revealed no statistically significant Mann–Kendall trend for purchased energy, a noteworthy upward trend was evident for generated energy, reaching 4,000,000 kWh∙year−1 in the final year of observation (Figure 3). Importantly, findings underscored that greenhouse gas emissions across the entire value chain are lower for biogas compared to alternative fuel options. Leveraging energy from solid municipal waste and sewage, our study suggests a potential displacement of 4053.47 t of diesel fuel daily, showcasing promising avenues for sustainable energy solutions.
Heat demand for a 50,000-resident plant in Spain: 28–51%, EUR 38–65/MWh [30]. MSP of biomethane: GBP 135–183/MWh, potential reduction: 32–42% [23]. Additionally, the produced gas demonstrated versatility, capable of powering a 50 kW generator for 5.5 to 9 h with specific feedstock mixtures [31]. However, critical levels of H2S and HCl were noted in the biogas, with concentrations reaching 1570 and 26.8 mg∙m−3, respectively, while levels of halogenated VOCs and mercaptans were relatively low [32]. WWTPs distant from high-pressure networks can produce renewable biomethane to replace local natural gas. Economic viability is uncertain; selling CO2 at EUR 46 per t is needed for profitability, while feed-in premiums over EUR 55.5 per MWh may also be required [32]. Understanding the complexity of dependent variables, such as generated energy (Y), is paramount. Its determination can be influenced by various factors including purchased energy, weather conditions, and seasonal trends. Within our Generalized Linear Model (GLM), we focused on the influence of purchased energy on generated energy. The model’s findings unveiled a negative slope coefficient (−0.10852), indicating a negative relationship between purchased energy and generated energy. In simpler terms, as purchased energy increases, generated energy decreases. While the Mann–Kendall test results for purchased energy revealed no statistically significant trend (S = 30; Z = 1.30; p = 0.19), suggesting insufficient evidence for a trend, generated energy exhibited a notable increasing trend (S = 60; Z = 2.65; p = 0.0078), supported by robust statistical evidence with a lower p-value. This underscores the upward trajectory in generated energy data.

4.2. Biogas Production Potential from Agricultural and Industrial Organic Waste

Now, agricultural sources are being utilized for biogas production, with a focus on comparing the renewable energy potential of food and beverage industry waste, evaluating development barriers, and emphasizing the increasing use of industrial organic waste in biogas production as a cost-effective alternative to maize silage [33]. In the other studies, substrates were subjected to anaerobic fermentation, and biogas production was measured. Food waste substrates exhibited higher methane yield ranging from 354 to 347 mL∙g-TCOD−1 and biodegradability of 89 ÷ 87%. For sludge substrates, the yield was from 324 to 288 mL∙g-TCOD−1 with biodegradability of 81–73%. It was also estimated that coffee waste has the potential to generate energy recovery between 4 and 10 million kJ∙day−1 and organic fertilizer (digestate) at the level of 18.8–25.2 kg∙day−1 [34]. Energy balance studies demonstrated the possibility of combining remediation with energy production. The high photosynthesis rate and ability to generate biomass along with nutrient uptake advocate for the use of microalgae as a potential source of next-generation biofuel in waste management [35].
Five main waste streams were identified as potential substrates for biogas production, namely slaughterhouse waste (consisting of dewatered activated sludge); wastewater from cheese factories; commercial and domestic food waste; pig slurry; and sewage sludge. The biomethane potential from these waste streams ranged from as low as 99 CH4∙kg−1 for pig slurry to as high as 787 CH4∙kg−1 for cheese factory wastewater sludge using dissolved air flotation (DAF) [36]. A series of batch experiments were conducted to determine the biomethane potential from three different sewage sludge samples over 74 days, using zinc content in the sludge, either alone or in combination with external addition of 200, 300, and 400 mg Zn/L. In the analysis of Principal Component Ordination (PCO), the data revealed that the first coordinate axis accounted for 93.99% of the variance, indicating its significant influence on the data structure, while the second axis explained 5.98%, representing a lesser degree of variation. Notably, hydrogen sulfide distribution across axes, barring one quadrant in the third axis, suggested either minimal differentiation from other variables or specific relationships with them. This underscores the strong association of data with the first coordinate axis, signifying a predominant variable influencing the data structure. Meanwhile, methane and heating value components exhibited a strong correlation, contrasting with the occasional negative correlations observed with carbon dioxide (Figure 7). The highest biomethane production was 165 ± 1 mL CH4/g with the use of activated sludge (AS) with a background concentration of 93 mg Zn/L. A slight decrease in biomethane yield (i.e., 157 ± 1.158 ± 1. and 159 ± 1 mL CH4∙g−1) was obtained in the presence of 293, 393, and 493 mg Zn/L, respectively [15]. Anaerobic digestion (AD) is a biological process that naturally occurs when bacteria break down organic matter in an environment with or without oxygen. Almost any organic material can be processed in AD, including paper and cardboard (too low-grade for recycling due to food contamination), grass clippings, food scraps, industrial wastewater, and animal waste, AD produces biogas, which consists of approximately 60% methane and 40% carbon dioxide [37]. Our study identified four main gases, with methane comprising approximately 60% and carbon dioxide around 40% of the composition from 2014 to 2023 (Figure 4). Methane exhibited a robust correlation (R2 = 0.92) with the calorific value of the resulting biogas, affirming its status as the primary component essential for achieving a calorific value above 20 MJ∙m−3 (Figure 5).
Currently, there is a growing interest in analytical methodologies for sampling and analyzing biogas from various wastewater treatment plants across Europe, as noted by [37]. Additionally, emerging trends include the transformation and utilization of biogas from diverse sources within the framework of the Green Energy Transformation, as highlighted by [38]. In our study, a large amount of waste was subjected to the co-fermentation process. There is a strong upward trend in the influx of supplied waste, exceeding 5000 Mg annually with an R2 = 0.99 (Figure 6). This indicates a constantly growing need for technological changes, as the waste can include a variety of biological substances [39,40]. This proportion is highly favorable from the perspective of our research findings. A higher methane content relative to CO2 can significantly enhance the calorific value of the produced biogas. In our paper, it was shown that the level of CO2 in the biogas production process does not significantly affect the calorific value. The highest content was observed for methane with R2 = 0.92 (Figure 4), which suggests that treatment plants should focus more on monitoring methane levels rather than carbon dioxide and hydrogen sulfide levels.
Regarding k-means clustering, the results depicted moderate effectiveness, with a Within-Group Sum of Squares (WGSS) value of 2.311 representing relatively compact and homogeneous clusters. The F-statistic of 1.58 implied similarities between clusters, while the variance of 61.314 indicated substantial variability within the dataset. Moreover, an average silhouette score of 0.56 suggested moderately well-separated clusters. While these metrics showed reasonable effectiveness, there exists potential for optimization, possibly through adjustments in cluster numbers or alternative clustering algorithms. The study also emphasized the significance of methane and carbon dioxide in biogas analysis, evident from their distinct clustering (Figure 8).
In multivariate regression analysis spanning from 2014 to 2023, a clear relationship between the technological process and variables was revealed, particularly their impact on calorific value. Methane exhibited a significant positive effect, while carbon dioxide had a notable negative impact. Conversely, oxygen showed no discernible relationship, and hydrogen sulfide displayed a significant positive effect on calorific value. These insights underscored a comprehensive understanding of the intricate relationships within the technological framework of biogas production. This comprehensive analysis offers valuable insights into the dynamics of biogas production, highlighting methane’s crucial role, the importance of managing carbon dioxide levels, and the intriguing dynamics of oxygen and hydrogen sulfide. These findings provide a basis for optimizing production processes, enhancing energy efficiency, and minimizing environmental impact, necessitating further research to deepen our understanding for a sustainable energy future and waste management.

4.3. Key Indicators of Limitations in Nutrient Removal Efficiency of Wastewater Treatment

Several key indicators highlight the limitations in wastewater treatment efficiency, particularly concerning nutrients, Chemical Oxygen Demand, and Biochemical Oxygen Demand. First, the balance between BOD5 and nitrogen is critical; low BOD5 to N ratios can inhibit the growth of denitrifying microorganisms, which reduces nitrogen removal efficiency and may lead to elevated nitrate levels in the effluent.
In raw wastewater, the BOD5 values ranged from 220 to 303 mg∙dm−3, while COD values ranged from 487 to 722 mg∙dm−3 during the study period. TSS reached a maximum value of 373 mg∙dm−3 in 2022, and NT reached 47.4 mg∙dm−3 in 2019. On the other hand, PT reached its maximum value in 2014 (Table 1). In treated wastewater, the BOD5 range was from 5.4 to 6.6 mg∙dm−3, and COD values ranged from 34.6 to 46.5 mg∙dm−3. The highest TSS was recorded in 2014, at 11.4 mg∙dm−3. NP in treated wastewater was highest in 2015 and 2023, at 8.78 mg·dm−3, while PT did not exceed 1 mg∙dm−3 (Table 2). A low ratio of BOD5 to nitrogen (N) causes inhibition of the growth of denitrifying microorganisms. thereby reducing the efficiency of nitrogen compound removal. The biological degradation of pollutants is measured by the ratio of COD to BOD5 [26]. Typically, efficient degradation is observed when this ratio is less than 2. Higher values of the COD/BOD5 ratio reduce the effectiveness of the biological degradation process. The presence of substances resistant to biodegradation (COD/BOD5 >> 2) may result in inadequate denitrification [26].
Despite the high efficiency of wastewater treatment, the biodegradability of the treated wastewater was low. This indicates the presence of a large amount of non-degradable substances. For example, the COD/BOD5 ratio in raw wastewater was 2.28, while in treated wastewater it was 6.59 in the first year of the study. An unfavorable reference value for the BOD5/NT ratio was noted in treated wastewater. A favorable reference value was observed for raw wastewater, as it exceeded 20 dm3. In contrast, a favorable situation was observed in treated wastewater for all the years of the study. The COD/PT ratio in both raw and treated wastewater reached the required value for effective wastewater treatment (Table 4). These results indicate an uneven biological decomposition process and a high accumulation of hardly degradable compounds. The effectiveness of biological phosphorus removal can be assessed based on the ratio of easily degradable organic substances to phosphorus content, i.e., the ratio of BOD5 to phosphorus. It is generally accepted that this ratio should be at least 20, and for low-loaded wastewater, it should be equal to or greater than 25. An unfavorable ratio of BOD5 to nitrogen can hinder the denitrification process, resulting in partial denitrification consequently leading to an increase in nitrates at the outflow. Denitrification proceeds without disturbances when the ratio of COD to nitrogen ranges from 5 to 10. Low values disrupt the process and necessitate the dosing of an external source of organic carbon.
Industry wastewater was treated with a BOD ratio of 100 during the biological treatment of wastewater [41]. Ref. [42] showed that significantly lower COD/N ratios resulted in decreased biological removal efficiency. In low-strength wastewater, the COD/P ratio was found to be 15:1 ÷ 25:1 [26]. The values of the CODCr/BOD5 ratio for raw sewage ranged from 1.9 to 2.5. while for sewage after successive mechanical treatment processes they ranged from 1.8 to 2.8. For sewage after biological processes, the ratio varied between 1.8 and 4.9. The increase in the ratio during the technological treatment process is associated with the breakdown of organic compounds. High CODCr/BOD5 ratios for treated sewage (3.1–9.8) specify that they mainly contain biologically hard-to-degrade organic substances [43].
Based on the insights gleaned from these studies, it is advisable to not only progress agricultural biogas plants but also to delve into the development of biogas facilities utilizing sewage sludge from wastewater treatment plants [44]. In 2015, the European Union demonstrated significant strides in biogas development, boasting an impressive 18 billion m3 in methane production, driven by renewable energy policies and resulting in substantial economic and environmental advantages. With over 10 GW of installed capacity and 17,400 biogas plants, the EU solidified its position as the global leader in biogas electricity generation, contributing to a sustainable energy landscape [45]. Contemporary approaches entail harnessing organic waste for the creation of biomentation processes [46,47,48]. To enhance wastewater treatment and biogas production, it is recommended to advance sewage biogas plants and develop facilities utilizing sewage sludge to optimize methane production through renewable energy policies [49,50].

5. Conclusions

This study revealed that methane exhibited the highest correlation with its calorific value. Assessing the efficiency indicators of the wastewater treatment process, particularly Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD), showed unfavorable ratios for COD/BOD in the treated effluent, despite highly efficient removal exceeding 90%. Although the BOD5/NT ratio in treated wastewater was unfavorable, it remained favorable in raw wastewater. However, the COD/PT ratio met the required value for effective wastewater treatment in both raw and treated wastewater. Evaluating the energy production potential derived from biogas post-wastewater treatment revealed a significant statistical trend of increased generated energy, considering biogas components and their utilization. Understanding the biodegradability of treated wastewater is crucial as it indicates the effectiveness of wastewater treatment processes and highlights areas for improvement in fermentation bioreactions. This knowledge drives innovation by using confirmation methods that are new in wastewater management practices, supporting environmental sustainability. Analyzing trends in energy generation from waste fermentation, we found that methane and carbon dioxide were the key predictors for biogas components. This study introduces new findings on k-means clustering, showing moderate effectiveness with a WGSS of 2.311 and an F-statistic of 1.58. The average silhouette score of 0.56 indicates moderately well-separated clusters, suggesting potential for optimization. The significance of methane and carbon dioxide in biogas analysis is highlighted through their distinct clustering. Such insights offer valuable advancements in understanding and optimizing biogas production processes for improved energy efficiency and environmental sustainability. This research introduces a new approach by analyzing the composition of the resulting biogas to assess specific relationships. This requires long-term research spanning at least 10 years, as this extended period is standard for data analysis. This duration allows for the evaluation of biogas production and purification effects for circular economy as they delineate the path from sludge to resource. This procedure entails directing the residues of waste materials to fermentation processes subsequent to their separation during the purification stage.

Author Contributions

Conceptualization, M.K.-W., W.H. and K.M.; methodology, W.H., D.B. and K.C.; software, W.H. and M.K.-W.; validation, K.C. and M.G.; formal analysis, W.H. and K.C.; investigation, K.G.; resources, K.A.D.; data curation, W.H. and K.C.; writing—original draft preparation, writing—review and editing, W.H. and A.P.; visualization, W.H., A.P. and D.B.; supervision, M.K.-W.; project administration, K.M., P.G., A.P. and D.B.; funding acquisition, M.G. and A.P. All authors have read and agreed to the published version of the manuscript.

Funding

The article present the result of the Project no 026/GGR/2024/POT financed from the subsidy granted to the Krakow University of Economics. This research was co-financed by the Ministry of Science and Higher Education for the University of Agriculture in Krakow for the year 2024.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

Author Krzysztof Michalski and author Magdalena Kośka-Wolny were employed by the company AQUA S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. El-Gohary, F.A.; Badawy, M.I.; El-Khateeb, M.A.; El-Kalliny, A.S. Integrated treatment of olive mill wastewater (OMW) by the combination of Fenton’s reaction and anaerobic treatment. J. Hazard. Mater. 2009, 162, 1536–1541. [Google Scholar] [CrossRef] [PubMed]
  2. Abdalla, H.; Tahan, M.D.D.; Abdallah, M.; Sartaj, M.; Hamza, R. Optimizing aerobic granular sludge process performance: Unveiling the power of coupling experimental factorial design methodology with artificial intelligence modeling. J. Water Process Eng. 2024, 61, 105268. [Google Scholar] [CrossRef]
  3. Acosta-Pavas, J.C.; Robles-Rodríguez, C.E.; Morchain, J.; Dumas, C.; Cockx, A.; Aceves-Lara, C.A. Dynamic modeling of biological methanation for different reactor configurations: An extension of the anaerobic digestion model No. 1. Fuel 2023, 344, 128106. [Google Scholar] [CrossRef]
  4. Mahieux, M.; Richard, C.; Aemig, Q.; Delgenès, J.P.; Juge, M.; Trably, E.; Escudié, R. Archaeal community composition as key driver of H2 consumption rates at the start-up of the biomethanation process. Sci. Total Environ. 2024, 931, 172922. [Google Scholar] [CrossRef]
  5. Tuncay, S.; Akcakaya, M.; Icgen, B. Ozonation of sewage sludge prior to anaerobic digestion led to Methanosaeta dominated biomethanation. Fuel 2022, 313, 122690. [Google Scholar] [CrossRef]
  6. Awe, O.W.; Zhao, Y.; Nzihou, A.; Minh, D.P.; Lyczko, N. A review of biogas utilisation, purification and upgrading technologies. Waste Biomass Valoriz. 2017, 8, 267–283. [Google Scholar] [CrossRef]
  7. Baena-Moreno, F.M.; Leventaki, E.; Riddell, A.; Wojtasz-Mucha, J.; Bernin, D. Effluents and residues from industrial sites for carbon dioxide capture: A review. Environ. Chem. Lett. 2023, 21, 319–337. [Google Scholar] [CrossRef]
  8. François, M.; Lin, K.S.; Rachmadona, N.; Khoo, K.S. Advancement of biochar-aided with iron chloride for contaminants removal from wastewater and biogas production: A review. Sci. Total Environ. 2023, 874, 162437. [Google Scholar] [CrossRef]
  9. Shen, Y.; Linville, J.L.; Urgun-Demirtas, M.; Mintz, M.M.; Snyder, S.W. An overview of biogas production and utilization at full-scale wastewater treatment plants (WWTPs) in the United States: Challenges and opportunities towards energy-neutral WWTPs. Renew. Sustain. Energy Rev. 2015, 50, 346–362. [Google Scholar] [CrossRef]
  10. Lima, D.; Appleby, G.; Li, L. A Scoping review of options for increasing biogas production from sewage sludge: Challenges and opportunities for enhancing energy self-sufficiency in wastewater treatment plants. Energies 2023, 16, 2369. [Google Scholar] [CrossRef]
  11. Rajendran, K.; Aslanzadeh, S.; Taherzadeh, M.J. Household biogas digesters—A review. Energies 2012, 5, 2911–2942. [Google Scholar] [CrossRef]
  12. Dabestani-Rahmatabad, A.; Capson-Tojo, G.; Trably, E.; Delgenès, J.P.; Escudié, R. Assessing the impact of organic loading rate on hydrogen consumption rates during in situ biomethanation. Energies 2024, 17, 2490. [Google Scholar] [CrossRef]
  13. Kougias, P.G.; Angelidaki, I. Biogas and its opportunities—A review. Front. Environ. Sci. Eng. 2018, 12, 14. [Google Scholar] [CrossRef]
  14. Schmid, C.; Horschig, T.; Pfeiffer, A.; Szarka, N.; Thrän, D. Biogas upgrading: A review of national biomethane strategies and support policies in selected countries. Energies 2019, 12, 3803. [Google Scholar] [CrossRef]
  15. Kumari, S.; Das, D. Improvement of gaseous energy recovery from sugarcane bagasse by dark fermentation followed by biomethanation process. Bioresour. Technol. 2015, 194, 354–363. [Google Scholar] [CrossRef] [PubMed]
  16. Abbas, H.F.; Daud, W.W. Hydrogen production by methane decomposition: A review. Int. J. Hydrog. Energy 2010, 35, 1160–1190. [Google Scholar] [CrossRef]
  17. Royer, S.J.; Ferrón, S.; Wilson, S.T.; Karl, D.M. Production of methane and ethylene from plastic in the environment. PLoS ONE 2018, 13, e0200574. [Google Scholar] [CrossRef]
  18. Nouj, N.; Majbar, Z.; Abelouah, M.R.; Hamou, A.B.; Chaoui, A.; Hafid, N.; Cretescu, I. Eco-friendly wastewater treatment using a crab shell-based liquid bio-coagulant: Multi-criteria decision analysis related to different pollutants separation. J. Environ. Chem. Eng. 2024, 12, 112318. [Google Scholar] [CrossRef]
  19. Sreekrishnan, T.R.; Kohli, S.; Rana, V. Enhancement of biogas production from solid substrates using different techniques—A review. Bioresour. Technol. 2004, 95, 1–10. [Google Scholar]
  20. Kapoor, R.; Ghosh, P.; Kumar, M.; Vijay, V.K. Evaluation of biogas upgrading technologies and future perspectives: A review. Environ. Sci. Pollut. Res. 2019, 26, 11631–11661. [Google Scholar] [CrossRef]
  21. Dar, R.A.; Parmar, M.; Dar, E.A.; Sani, R.K.; Phutela, U.G. Biomethanation of agricultural residues: Potential, limitations and possible solutions. Renew. Sustain. Energy Rev. 2021, 135, 110217. [Google Scholar] [CrossRef]
  22. Feng, W.; Xiao, K.; Zhou, W.; Zhu, D.; Zhou, Y.; Yuan, Y.; Zhao, J. Analysis of utilization technologies for Eichhornia crassipes biomass harvested after restoration of wastewater. Bioresour. Technol. 2017, 223, 287–295. [Google Scholar] [CrossRef] [PubMed]
  23. Michailos, S.; Walker, M.; Moody, A.; Poggio, D.; Pourkashanian, M. Biomethane production using an integrated anaerobic digestion, gasification and CO2 biomethanation process in a real wastewater treatment plant: A techno-economic assessment. Energy Convers. Manag. 2020, 209, 112663. [Google Scholar] [CrossRef]
  24. Thakur, N.; Sharma, M.; Alghamdi, H.; Zheng, Y.; Xue, W.; Jeon, B.H.; Li, X. A recent trend in anaerobic digestion (AD): Enhancement of microbiome and digestibility of feedstocks via abiotic stress factors for biomethanation. Chem. Eng. J. 2023, 472, 145047. [Google Scholar] [CrossRef]
  25. Feng, X.; Qian, Y.; Xi, P.; Cao, R.; Qin, L.; Zhang, S.; Wang, D. Partial nitrification and enhanced biological phosphorus removal in a sequencing batch reactor treating high-strength wastewater. Int. J. Environ. Res. Public Health 2022, 19, 5653. [Google Scholar] [CrossRef]
  26. Wrzesińska-Jędrusiak, E.; Czarnecki, M.; Szufa, S.; Maj, G.; Čabalová, I.; Grześkowiak, M. Analysis of Opportunities for Performance Improvement Based on Data from Agricultural Mono Substrate Biogas Plant. J. Water Land Dev. 2024, 55, 55–61. [Google Scholar] [CrossRef]
  27. Wałowski, G. Assessment of Technological Simulation of an Agricultural Biogas Installation Using Integration Mechanisms. J. Water Land Dev. 2023, 283, 283–290. [Google Scholar] [CrossRef]
  28. Czekała, W.; Pulka, J.; Jasiński, T.; Szewczyk, P.; Bojarski, W.; Jasiński, J. Waste as Substrates for Agricultural Biogas Plants: A Case Study from Poland. J. Water Land Dev. 2023, 45, 45–50. [Google Scholar] [CrossRef]
  29. Elalami, D.; Carrere, H.; Monlau, F.; Abdelouahdi, K.; Oukarroum, A.; Barakat, A. Pretreatment and Co-Digestion of Wastewate Sludge for Biogas Production: Recent Research Advances and Trends. Renew. Sustain. Energy Rev. 2019, 114, 1092. [Google Scholar] [CrossRef]
  30. Singh, P.; Kalamdhad, A.S. Biomethane plants based on municipal solid waste and wastewater and its impact on vehicle sector in India—An environmental-economic-resource assessment. Environ. Technol. Innov. 2022, 26, 102330. [Google Scholar] [CrossRef]
  31. Ogejo, J.A.; Li, L. Enhancing biomethane production from flush dairy manure with turkey processing wastewater. Appl. Energy 2010, 87, 3171–3177. [Google Scholar] [CrossRef]
  32. Paolini, V.; Petracchini, F.; Carnevale, M.; Gallucci, F.; Perilli, M.; Esposito, G.; Frattoni, M. Environmental impact of biogas: A short review of current knowledge. J. Environ. Sci. Health Part A Tox. Hazard. Subst. Environ. Eng. 2018, 53, 899–906. [Google Scholar] [CrossRef] [PubMed]
  33. Pazera, A.; Slezak, R.; Krzystek, L.; Ledakowicz, S.; Bochmann, G.; Gabauer, W.; Kara, J. Biogas in Europe: Food and beverage (FAB) waste potential for biogas production. Energy Fuels 2015, 29, 4011–4021. [Google Scholar] [CrossRef]
  34. Beyene, A.; Yemane, D.; Addis, T.; Assayie, A.A.; Triest, L. Experimental evaluation of anaerobic digestion for coffee wastewater treatment and its biomethane recovery potential. Int. J. Environ. Sci. Technol. 2014, 11, 1881–1886. [Google Scholar] [CrossRef]
  35. Brar, A.; Kumar, M.; Singh, R.P.; Vivekanand, V.; Pareek, N. Phycoremediation coupled biomethane production employing sewage wastewater: Energy balance and feasibility analysis. Bioresour. Technol. 2020, 308, 123292. [Google Scholar] [CrossRef]
  36. Browne, J.D.; Allen, E.; Murphy, J.D. Evaluation of the biomethane potential from multiple waste streams for a proposed community scale anaerobic digester. Environ. Technol. 2013, 34, 2027–2038. [Google Scholar] [CrossRef]
  37. Molino, A.; Nanna, F.; Ding, Y.; Bikson, B.; Braccio, G. Biomethane production by anaerobic digestion of organic waste. Fuel 2013, 103, 1003–1009. [Google Scholar] [CrossRef]
  38. Rogala, Z.; Stanclik, M.; Łuszkiewicz, D.; Malecha, Z. Perspectives for the use of biogas and biomethane in the context of the green energy transformation on the example of an EU country. Energies 2023, 16, 1911. [Google Scholar] [CrossRef]
  39. Bodík, I.; Sedláček, S.; Kubaská, M.; Hutňan, M. Biogas production in municipal wastewater treatment plants–current status in EU with a focus on the Slovak Republic. Chem. Biochem. Eng. Q. 2011, 25, 335–340. [Google Scholar]
  40. Zupančič, M.; Možic, V.; Može, M.; Cimerman, F.; Golobič, I. Current status and review of waste-to-biogas conversion for selected European countries and worldwide. Sustainability 2022, 14, 1823. [Google Scholar] [CrossRef]
  41. Balakrishnan, A.; Kanchinadham, S.B.K.; Kalyanaraman, C. Nutrient requirement of tannery wastewater containing tannins. Environ. Technol. Innov. 2021, 23, 101776. [Google Scholar] [CrossRef]
  42. Phanwilai, S.; Noophan, P.; Li, C.W.; Choo, K.H. Effect of COD ratio on biological nitrogen removal using full-scale step-feed in municipal wastewater treatment plants. Sustain. Environ. Res. 2020, 30, 24. [Google Scholar] [CrossRef]
  43. Płuciennik-Koropczuk, E.; Jakubaszek, A. Susceptibility of wastewater for biochemical decomposition in mechanical-biological wastewater treatment processes. Zesz. Nauk. 2012, 148, 73–84. [Google Scholar]
  44. Silva, J.; Gonçalves, J.C.; Rocha, C.; Vilaça, J.; Madeira, L.M. Biomethane production from biogas obtained in wastewater treatment plants: Process optimization and economic analysis. Renew. Energy 2024, 220, 119469. [Google Scholar] [CrossRef]
  45. Scarlat, N.; Dallemand, J.F.; Fahl, F. Biogas: Developments and perspectives in Europe. Renew. Energy 2018, 129, 457–472. [Google Scholar] [CrossRef]
  46. Csedő, Z.; Magyari, J.; Zavarkó, M. Biofuel supply chain planning and circular business model innovation at wastewater treatment plants: The case of biomethane production. Cleaner Logist. Supply Chain 2024, 11, 100158. [Google Scholar] [CrossRef]
  47. Baena-Moreno, F.M.; Malico, I.; Marques, I.P. Promoting sustainability: Wastewater treatment plants as a source of biomethane in regions far from a high-pressure grid: A real Portuguese case study. Sustainability 2021, 13, 8933. [Google Scholar] [CrossRef]
  48. Gavlak, G.; Aguiar Battistelli, A.; Pedroso, C.R.; Magno de Sousa Vidal, C.; Viviane de Souza, K. Evaluating the performance and membrane fouling of a submerged membrane bioreactor (MBR) treating plywood industry wastewater. Water Sci. Technol. 2024, 89, 187–198. [Google Scholar] [CrossRef]
  49. Raich-Montiu, J.; Ribas-Font, C.; De Arespacochaga, N.; Roig-Torres, E.; Broto-Puig, F.; Crest, M.; Cortina, J.L. Analytical methodology for sampling and analysing eight siloxanes and trimethylsilanol in biogas from different wastewater treatment plants in Europe. Anal. Chim. Acta 2014, 812, 83–91. [Google Scholar] [CrossRef]
  50. Serrat, L.; Linares, J.I.; Cledera, M.M.; Morales, C.; Hueso, K. Ground source heat pump driven by reciprocating engine firing biomethane from wastewater treatment plant sludge in a cogeneration for district heating and cooling: A case study in Spain. Appl. Therm. Eng. 2023, 219, 119586. [Google Scholar] [CrossRef]
Energies 17 05618 g001
Figure 1. A technological procedure in the wastewater treatment plant.
Figure 1. A technological procedure in the wastewater treatment plant.
Energies 17 05618 g001
Energies 17 05618 g002
Figure 2. Amount of waste for fermentation during the study period.
Figure 2. Amount of waste for fermentation during the study period.
Energies 17 05618 g002
Energies 17 05618 g003
Figure 3. The average annual energy consumption during the research period. Log likelihood = −7; G-statistics = 0.219; p = 0.639.
Figure 3. The average annual energy consumption during the research period. Log likelihood = −7; G-statistics = 0.219; p = 0.639.
Energies 17 05618 g003
Energies 17 05618 g004
Figure 4. Main components of the biogas produced in the technological process (n = 203).
Figure 4. Main components of the biogas produced in the technological process (n = 203).
Energies 17 05618 g004
Energies 17 05618 g005
Figure 5. Relationship between calorific value and methane content.
Figure 5. Relationship between calorific value and methane content.
Energies 17 05618 g005
Energies 17 05618 g006
Figure 6. Amount of waste received for co-fermentation in the years 2014–2023 with a 5-year forecast.
Figure 6. Amount of waste received for co-fermentation in the years 2014–2023 with a 5-year forecast.
Energies 17 05618 g006
Energies 17 05618 g007
Figure 7. PCO for biogas composition: red diamonds represent heating value, blue squares denote methane, black squares stand for hydrogen sulfide, and green stars symbolize oxygen.
Figure 7. PCO for biogas composition: red diamonds represent heating value, blue squares denote methane, black squares stand for hydrogen sulfide, and green stars symbolize oxygen.
Energies 17 05618 g007
Energies 17 05618 g008
Figure 8. K-means analysis for clustering biogas components.
Figure 8. K-means analysis for clustering biogas components.
Energies 17 05618 g008
Table 1. The average annual physicochemical parameters in raw sewage.
Table 1. The average annual physicochemical parameters in raw sewage.
YearBOD5CODTSSNTPT
(mg∙dm−3)
2014259590316436.58
201528958432443.26.19
2016220487240395
201723150326541.24.98
201826659728747.45.9
201924156429942.75.42
202023653529541.44.96
202125957530243.65.9
2022303722373485.49
202325058334241.25.37
Table 2. The average annual physicochemical parameters in treated sewage.
Table 2. The average annual physicochemical parameters in treated sewage.
YearBOD5CODTSSNT *PT
(mg∙dm−3)
20145.8938.811.48.210.48
20155.234.610.48.780.45
20165.5239118.70.6
20175.435.498.560.41
20185.8541.49.38.210.4
20195.68398.48.140.43
20205.9138.98.98.030.46
20215.7138.68.48.360.48
20225.9240.98.98.180.44
20236.646.5118.780.5
* for temperatures in the bioreactor >12 degrees Celsius.
Table 3. The average annual amount of sewage flowing into the wastewater treatment plant used in the research.
Table 3. The average annual amount of sewage flowing into the wastewater treatment plant used in the research.
YearThe Amount of Sewage Flowing from the Sewer SystemThe Amount of Sewage Delivered from Vacuum Trucks
Million m3∙year−1m3∙year−1
201420.95199798,000
201518.84624154,000
201620.04277340,000
201720.55321130,000
201818.39008432,000
201920.16663138,000
202021.55086753,000
202120.86285063,000
202218.94661064,000
202321.315280105,000
Table 4. The susceptibility of raw and treated sewage to the biological degradation process.
Table 4. The susceptibility of raw and treated sewage to the biological degradation process.
COD/PTCOD/NTBOD5/PTBOD5/NTCOD/BOD5Sewage TypeYear
89.6713.7239.366.022.28raw2014
80.834.7312.270.726.59treated
94.3513.5246.696.692.02raw2015
76.893.9411.560.596.65treated
97.4012.4944.05.642.21raw2016
65.04.489.20.637.07treated
101.0012.2146.395.612.18raw2017
86.344.1413.170.636.56treated
101.1912.5945.085.612.24raw2018
103.505.0414.630.717.08treated
104.0613.2144.465.642.34raw2019
90.704.7913.210.706.87treated
107.8612.9247.585.702.27raw2020
84.574.8412.850.746.58treated
97.4613.1943.905.942.22raw2021
80.424.6211.900.686.76treated
131.5115.0455.196.312.38raw2022
92.955.0013.450.726.91treated
108.5714.1546.556.072.33raw2023
93.05.3013.200.757.05treated
>405 ÷ 10>20 (optimum 25)>2.5 (optimum 4)<2The required value for effective wastewater treatment
Table 5. Multivariate linear regression for the main biogas components.
Table 5. Multivariate linear regression for the main biogas components.
prErrorInterceptErrorSlopeVariable
>0.0010.961.182.400.00020.012Methane
0.00011−0.265.1457.980.0010−0.0041Carbon dioxide
>0.0010.3087.57−338.560.0170.080Hydrogen sulfide
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Michalski, K.; Kośka-Wolny, M.; Chmielowski, K.; Bedla, D.; Petryk, A.; Guzdek, P.; Dąbek, K.A.; Gąsiorek, M.; Grübel, K.; Halecki, W. Examining the Potential of Biogas: A Pathway from Post-Fermented Waste into Energy in a Wastewater Treatment Plant. Energies 2024, 17, 5618. https://doi.org/10.3390/en17225618

AMA Style

Michalski K, Kośka-Wolny M, Chmielowski K, Bedla D, Petryk A, Guzdek P, Dąbek KA, Gąsiorek M, Grübel K, Halecki W. Examining the Potential of Biogas: A Pathway from Post-Fermented Waste into Energy in a Wastewater Treatment Plant. Energies. 2024; 17(22):5618. https://doi.org/10.3390/en17225618

Chicago/Turabian Style

Michalski, Krzysztof, Magdalena Kośka-Wolny, Krzysztof Chmielowski, Dawid Bedla, Agnieszka Petryk, Paweł Guzdek, Katarzyna Anna Dąbek, Michał Gąsiorek, Klaudiusz Grübel, and Wiktor Halecki. 2024. "Examining the Potential of Biogas: A Pathway from Post-Fermented Waste into Energy in a Wastewater Treatment Plant" Energies 17, no. 22: 5618. https://doi.org/10.3390/en17225618

APA Style

Michalski, K., Kośka-Wolny, M., Chmielowski, K., Bedla, D., Petryk, A., Guzdek, P., Dąbek, K. A., Gąsiorek, M., Grübel, K., & Halecki, W. (2024). Examining the Potential of Biogas: A Pathway from Post-Fermented Waste into Energy in a Wastewater Treatment Plant. Energies, 17(22), 5618. https://doi.org/10.3390/en17225618

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop