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Article

Co-Valorisation Energy Potential of Wastewater Treatment Sludge and Agroforestry Waste

by
Amadeu D. S. Borges
1,2,
Miguel Oliveira
2,
Bruno M. M. Teixeira
2 and
Frederico Branco
3,4,*
1
CQ-VR, Chemistry Research Centre–Vila Real, Laboratory of Thermal Sciences and Sustainability, Engineering Department, University of Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
2
Laboratory of Thermal Sciences and Sustainability, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
3
Department of Engineering, School of Sciences and Technology, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
4
INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Environments 2024, 11(1), 14; https://doi.org/10.3390/environments11010014
Submission received: 26 October 2023 / Revised: 19 December 2023 / Accepted: 3 January 2024 / Published: 9 January 2024
Browse Figures
Versions Notes

Abstract

:
The growing demand for sustainable and environment-friendly energy sources resulted in extensive research in the field of renewable energy. Biomass, derived from organic materials such as agricultural waste, forestry products, and wastewater treatment plant (WWTP) sludge, holds great potential as a renewable energy resource that can reduce greenhouse gas emissions and offer sustainable solutions for energy production. This study focused on diverse biomass materials, including sludge from WWTPs, forest biomass, swine waste, cork powder, and biochar. Chemical and physicochemical characterizations were performed to understand their energy potential, highlighting their elemental composition, proximate analysis, and calorific values. Results showed that different biomasses have varying energy content, with biochar and cork powder emerging as high-energy materials with net heating values of 32.56 MJ/kg and 25.73 MJ/kg, respectively. WWTP sludge also demonstrated considerable potential with net heating values of around 14.87 MJ/kg to 17.44 MJ/kg. The relationships between biomass compositions and their heating values were explored, indicating the significance of low nitrogen and sulphur content and favourable carbon, hydrogen, and moisture balances for energy production. Additionally, this study looked into the possibility of mixing different biomasses to optimize their use and overcome limitations like high ash and moisture contents. Mixtures, such as “75% Santo Emilião WWTP Sludge + 25% Biochar,” showed impressive net heating values of approximately 21.032 MJ/kg and demonstrated reduced emissions during combustion. The study’s findings contribute to renewable energy research, offering insights into efficient and sustainable energy production processes and emphasizing the environmental benefits of biomass energy sources with low nitrogen and sulphur content.

1. Introduction

The growing demand for sustainable energy sources and the worldwide effort to decrease greenhouse gas emissions have driven a transformative wave of research into renewable energy in recent decades, introducing alternative economic models, such as circular economy and bioeconomy to encourage sustainable growth and development [1,2,3,4].
Among this evolving landscape, biomass, as a renewable energy reservoir derived from organic materials such as agricultural residues, forestry products, and waste streams, holds significant promises as a renewable energy resource, capable of reducing greenhouse gas emissions, mitigating environmental pollution, fostering self-sufficient energy, and supplanting fossil fuels [5,6,7].
Biomass is a diverse category that embraces a wide range of materials, including wood and agricultural waste to sludge from wastewater treatment plants (WWTPs). Biomass presents an inherent complexity of chemical composition and physical properties [8]. This diversity offers a wide range of opportunities for energy production, but it also challenges the optimization of conversion processes and the maximization of energy potential [9]. The different biomass forms have been recognized as a plentiful and sustainable source of renewable energy.
Unlike fossil fuels, which emit carbon dioxide (CO2) into the atmosphere when burned and significantly contribute to global warming, biomass energy is carbon (C) neutral. The carbon emitted during combustion is compensated for by the carbon absorbed by plants during growth. Biomass is an essential component of a low-carbon energy portfolio [7].
The biomass utilization for energy production can take multiple forms, including direct combustion, gasification, pyrolysis, and the production of biogas through anaerobic digestion. These distinct conversion pathways allow flexibility when the adaptation of biomass resources to meet specific energy needs is required, whether heat, electricity, or biofuel forms [6,7,10].
Municipal wastewater treatment plants (WWTPs) globally generate substantial wastewater solids due to urbanization and population growing [11]. In 2017, WWTP sludge production reached 45 million dry tonnes [12], serving as a reservoir for inorganic nutrients (phosphorus (P) and nitrogen (N)) and organic compounds [13].
WWTP sludge, especially in a non-stabilized form, may contain dangerous substances, including pathogens [14], organic contaminants (polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs)) [15], heavy metals [16], and emerging contaminants (hormones, antibiotics, pharmaceuticals, personal care products) [17]. The continuous sludge generation and associated risks emphasize the need for alternative treatment approaches. Traditional disposal methods (landfilling, composting, land application, incineration) for WWTP sludge require significant financial investments [18].
Sludge treatment goals focus on waste reduction and resource recovery. Waste-to-energy conversion is recognized for reducing greenhouse gas emissions and fossil fuel dependence [19].
Despite anaerobic digestion (AD) treatment decomposing organic substances, 40–50% of them remains and needs proper disposal [20]. Hydrothermal liquefaction (HTL) is an emerging technology, which transforms waste biomass, including municipal sludge, into biofuel resembling petroleum, commonly referred to as biocrude. However, aqueous phase and hydrochar, two important HTL outputs, must be properly managed for their contaminants (such as ammonia and heavy metals) and recoverable resources (N and P). The direct land application of hydrochar could be limited by heavy metal quantity permission by regulation authorities, such as cadmium (Cd), molybdenum (Mo), and zinc (Zn) [21].
Intensive pig production, with high pig concentrations per land unit, exceeds soil capacity [22,23]. Slurry application presents environmental challenges, including nutrient release, methane emissions, and leaching [24,25]. Excessive slurry use has been related to elevated P and N in water bodies [25,26] and substantial copper (Cu) and Zn accumulation in soils [27,28]. Nutrient enrichment disrupts aquatic ecosystems, causing eutrophication, nitrate contamination, and impacts on soil microbiota [29,30,31].
Among diverse biomass sources, such as municipal solid waste, agricultural residues, and various organic byproducts, the innovation in this study is the focus on the energy potential of WWTPS as a solid fuel in pellet form. To optimize resource utilization and to overcome challenges like high ash and moisture contents, this study employed mixtures of different biomasses, such as forest biomass, swine waste, cork powder, and biochar. These materials were chosen based on their high environmental impact or ready availability in Portugal.
The utilization of biomass for energy generation offers a path towards a greener and more sustainable future, but the efficient harnessing depends on a comprehensive understanding of the chemical composition and energetic potential. The widespread characterization of biomass materials requests advanced analytical techniques, such as proximate analysis, ultimate analysis, and calorimetry, and provides valuable insights into the elemental composition, energy content, and structural features of biomass, which can unveil their intricate chemical and physical properties.

2. Materials and Methods

2.1. Samples

The samples were collected from different locations. The sludge samples were provided by Águas do Norte, S.A., from the WWTP of Serzedo, Ponte da Baia-Amarante, and Santo Emilião. The swine waste was collected from the UTAD swine pens. The cork powder was provided by the company AJ. Gomes & Cia Lda. The conventional biomass is sawdust from the carpentry of UTAD and the biochar is the result of the pyrolysis of oak biomass carried out in the laboratory of thermal sciences and sustainability of UTAD. All samples were properly identified and labelled, following the corresponding scientific nomenclature. Samples were air-dried to reduce them to small, uniform particles, to ensure homogeneity. Samples were accurately weighed in an analytical balance, model 120-5DM from Kern, according to predetermined mixture ratios, encompassing variations such as 75% sludge and 25% other biomass, as well as 50% sludge and 50% other biomass. Then, they were stored in airtight bags for preservation and to prevent external contamination.

2.2. Chemical Characterization

2.2.1. Proximate Analysis

Proximate analyses (moisture, volatile matter, ash, and fixed carbon) were performed on all the samples according to ISO 18134-1 [32], ISO 18122 [33], and ISO 18123 [34].
A representative sample was obtained from each biomass and mixtures, and the initial mass of each sample was recorded. The samples were placed into a Protherm PLF 100/6 furnace set at 105 °C until the weight stabilized, signifying the total removal of moisture. The final mass of each dried sample was recorded, and the moisture content was calculated by weight difference.
A portion of the dried biomass sample from the moisture analysis was taken, and the mass was recorded. The sample was heated in a covered crucible to 550 °C until no further weight loss was observed, indicating the volatilization of organic components. The volatile matter content (%) was calculated by weight difference.
The residue from the volatilization process was then ashed in a muffle furnace at 550 °C until a constant weight was achieved, indicating the complete combustion of organic matter.
The fixed carbon content (%) was calculated by subtracting the moisture content, the volatile matter content, and the ash content from 100%.

2.2.2. Elemental Analysis

Biomass samples were weighed, and mass was recorded. The elemental analysis (carbon, hydrogen (H), nitrogen, sulphur (S)) was performed on all samples according to ISO 16948 [35] in a Thermoscientific Flashsmart CHNS/O elemental analyser using an aluminium tin container. The oxygen (O) was determined by subtracting the other elements and the ash content at 100%.

2.2.3. Calorimetry

An isoperibolic calorimeter (mod. 6300, Parr Instruments Co., Moline, IL, USA) was used to perform the sample combustion according to ISO 18125 [36]. The calorimeter was calibrated with a benzoic acid standard (Benzoic Acid Parr No. 3415). The net heat value (NHV) was calculated according to ISO 18125.

2.3. Statistical Analysis

PCA stands out as one of the frequently employed chemometric methods for reducing data complexity and conducting an exploratory analysis on datasets with high dimensions. It dissects the original matrix into a product of loading matrices (representing chemical components of biomass) and score matrices (representing biomass samples). The principal components, being linear combinations of the initial variables, are uncorrelated and collectively encapsulate the entire variance of the original variables. PCA operates as an unsupervised method for pattern recognition, requiring no pre-established data grouping before the analysis.
The resultant subspace, defined by the principal components, yields a model that is more straightforward to interpret than the original dataset. These outcomes should facilitate the identification of various characteristics and enable correlation with the chemical composition of distinct biomass samples subjected to the analysis.

3. Results and Discussion

3.1. Physicochemical Characterization of Biomass

The Santo Emilião WWTP sludge has the highest nitrogen content (Table 1) registered (8.54%), while the Ponte da Baia-Amarante WWTP sludge possesses the lowest nitrogen content among the sampled sludges (6.86%). Regarding carbon and hydrogen content, the Serzedo WWTP sludge shows off the highest values among the sludge samples, with the sample of cork powder having the highest carbon and hydrogen content. The Ponte da Baia-Amarante WWTP sludge has the highest sulphur content (0.84%), while the other two sludges show similar sulphur contents around 0.67%. Regarding oxygen content, Santo Emilião WWTP sludge has the highest value (22.29%) among the sludge samples while conventional biomass has the highest oxygen content of all the samples studied with 45.80%.
Since WWTPs vary in size, layout, and single unit characteristics, as well as in the area they serve and urban waste management techniques they employ, it is challenging to compare the physicochemical characteristics’ results of WWTPS samples with those published in other studies.
A study conducted by H. Liu et al. (2023) provides specific values for elemental composition of WWTP sludge, reporting C at 47.9%, H at 5.7%, N at 3.7%, S at 0.6%, and O at 32.3% [21]. Another study led by H. Liu, I.A. Basar, A. Nzihou et al. (2021) offers a broader perspective by presenting ranges for these elements: carbon ranging from 11.2% to 47.0%, hydrogen from 1.7% to 6.9%, nitrogen spanning 0.3% to 7.8%, sulphur fluctuating between 0.1% and 5.62%, and oxygen varying from 4.2% to 42.66% [37]. It is important to remember that while it is not a standard that is particularly applied to pellets made from sludge, the ISO 17225-6:2014 standard [38], offers information on content restrictions because it was created for pellets made from non-woody materials. For example, there is a limit of 2% nitrogen content for a class B pellet and a limit of 0.3% of sulphur content. Consequently, using the sludges as standalone materials falls outside these standard limits.
Swine waste, also a sludge from animal production, presented a much lower nitrogen value with 2.68%, slightly surpassing the ISO 17225-6:2014 standard limit, similar to biochar with 2.11%. Regarding sulphur content, swine waste records 0.14%, a level suitable for a class A pellet.
These findings align with literature values; for instance, in a previous study, swine waste demonstrated C (%) at 35.59, N (%) at 1.72, H (%) at 4.84, and S (%) at 0.44 [39]. Minor differences in results could be attributed to factors such as animal species, diet, bedding, as well as the storage and handling of the manure [40].
The Santo Emilião WWTP sludge exhibits the highest moisture content among various sludges and the highest moisture content among the different biomass sources studied (Table 2). Additionally, it shows the highest volatile matter content among the WWTP sludges studied, which ranges from 51% to 56.21%, while the highest volatile matter value was registered by the conventional biomass sample. In contrast, the Ponte da Baia-Amarante WWTP sludge has the highest ash content (29.90%), and the lowest fixed carbon content (10.22%).
In comparison, the study by H. Liu et al. (2023) reports a WWTP sludge with an ash content of 9.8%, volatile matter of 81.7%, and fixed carbon of 8.6% [21]. In a separate investigation focused on WWTP sludge, G. Kor-Bicakci and C. Eskicioglu (2019) found volatile solids ranging from 60 to 85% [11]. In another study by H. Liu et al. (2021), volatile matter ranged from 27.8% to 9.1%, ash content varied from 10.9% to 70.1%, and fixed carbon content spanned from 0.7% to 21.9% [37].
The ISO 17225-6:2014 standard limits the moisture content to 12% to make a class A pellet but limits the ash content to 6% in the same classification. For a class B pellet, the limit rises to 10%. Since the sludges studied have such a high ash content, the use of these materials alone again falls outside the standard limits.
The results for Gross Heating Value (GHV) and net heating value (NHV) (Table 3) provide insights into the energy potential and the energy content after accounting for water vaporization. Ponte da Baia-Amarante WWTP sludge has the lowest GHV among the WWTP sludge samples with 15.82 MJ/kg, while the other two WWTP sludges registered similar GHV around 18 MJ/kg.
In 2023, H. Liu et al. reported a GHV of 19.9 megajoules per kilogram (MJ/kg) for a wastewater treatment plant (WWTP) sludge [21]. On the other hand, a study by G. Kor-Bicakci and C. Eskicioglu (2019) found a slightly lower GHV of 15.12 MJ/kg for a different WWTP sludge [11]. Additionally, H. Liu et al. (2021) reported a range of GHV values for WWTP sludge, spanning from 3.5 to 22.2 MJ/kg [37]. These variations in GHV values across different studies highlight the diverse composition and energy content of WWTP sludge, influenced by factors such as feedstock characteristics, treatment processes, and measurement methods [41].
Regarding net heating value (NHV), the Ponte da Baia-Amarante WWTP sludge registered the lowest value at 14.87 MJ/kg. This value is slightly above the lower limit imposed by ISO 17225-6:2014, which is 14.5 MJ/kg. This indicates that the NHV of the sludge is within the acceptable range for producing a standardized pellet according to ISO specifications. Conventional biomass showed a GHV of 19.79 MJ/kg and an NHV of 18.72 MJ/kg, indicating a higher energy potential compared to WWTP sludge. The swine wastes exhibited a GHV of 17.51 MJ/kg and an NHV of 16.58 MJ/kg, indicating a calorific value lower than the conventional biomass, but still significant.
The cork powder revealed a GHV of 27.24 MJ/kg and an NHV of 25.73 MJ/kg, suggesting a high energy potential. Finally, biochar presented the highest values of GHV (32.87 MJ/kg) and NHV (32.56 MJ/kg), indicating a considerable calorific potential.
It was observed that there were variations in energy characteristics across the samples. Biochar and cork powder stand out with GHV values of 32.87 MJ/kg and 27.24 MJ/kg, respectively. These values show that both materials hold significant energy potential, making them suitable for energy production and high-energy applications when compared to the typical GHV of biomass, which generally ranges between 17 and 20 MJ/kg [42]. On the other hand, the WWTP sludge samples, such as Serzedo and Santo Emilião, also exhibit substantial GHV values, around 18.09 MJ/kg and 18.52 MJ/kg, respectively. These values suggest that WWTP sludge can be considered as a potential energy source, although with slightly lower energy content when compared to biochar and cork powder.
The NHV values are slightly lower than the GHV values across the board; NHV considers the energy required to vaporize water formed during combustion, which indicates that the water content in the samples does have an impact on their energy content. The NHV values are aligned with the GHV trends, with biochar having the highest NHV of 32.56 MJ/kg, and cork powder following closely with 25.73 MJ/kg. These materials retain a substantial portion of their energy content even after water vaporization. Swine waste, a potentially valuable waste material, also demonstrates moderate NHV values, providing a practical option for energy recovery. These results collectively support the assessment of using different materials for energy production, emphasizing the significance of GHV and NHV in the determination of their suitability for different applications, from waste management to sustainable energy production.

3.2. Study of Biomass Mixtures to Improve the Use of WWTP Sludge as Fuel

This study considered a range of material mixtures, including 75% sludge and 25% other biomasses, as well as 50% sludge and 50% other biomasses. The main objective was to assess their viability as energy sources, with a focus on enhancing the NHV, and simultaneously reducing ash and sulphur content. The N and S content in these mixtures play an essential role in their suitability determination in order to produce standardised pellets as stated in ISO 17225-6:2014 (Table 4 and Table 5). Mixtures with lower N and S content, such as “75% SES + 25% CP” or “75% SES + 25% B,” exhibit an advantage in terms of reduced emissions during combustion, as there will be a lower production of sulphur oxides and nitrogen oxides. The GHV of these mixtures is notably higher, with GHV values of approximately 20.720 MJ/kg and 21.954 MJ/kg, respectively.
The C and H content is another critical factor, influencing the energy potential of these mixtures. Combinations with higher C and H content, like “75% SES + 25% CP” or “75% SES + 25% B,” show substantially higher GHV values, reaching up to 21.032 MJ/kg. This result highlights their potential for efficient energy production. Moreover, the presence of volatile matter in these mixtures further enhances their utility for practical applications, as materials with high volatile matter have a propensity for quick ignition and sustained combustion.
Moisture content is another crucial element, affecting the heating values of these mixtures. Higher moisture content necessitates additional energy to water vaporization during combustion, reducing the heating values. Mixtures with lower moisture content, such as “75% PBAS + 25% CB,” exhibit a favourable balance between moisture content and GHV, with a GHV value of around 17.129 MJ/kg. This insight is essential for optimizing energy production processes, as it emphasizes the importance of moisture reduction to enhance overall efficiency.
The mixture “50% SS + 50% CB” demonstrates a well-balanced elemental composition, providing favourable C and H content alongside moderate moisture levels. This balance results in GHV of approximately 19.014 MJ/kg and NHV of 17.746 MJ/kg, indicating its potential as an efficient and environmentally friendly energy source.
Otherwise, mixtures such as “50% SS + 50% SB” reveal reduced N and S content, leading to lower GHV values of around 17.941 MJ/kg but with an advantage in reduced emissions during combustion, making them environmentally attractive for specific applications.
Some mixtures, like “50% SS + 50% B,” boast lower moisture content, contributing to GHV and NHV values of approximately 25.079 MJ/kg. These mixtures, characterized by lower N and S content, are suitable for situations where moisture reduction is possible and where environmentally friendly combustion is a priority.
Despite the improvements in net heating value (NHV) observed in some mixtures, challenges arise due to the presence of ash content exceeding 10%, nitrogen content surpassing 2%, and sulphur content exceeding 0.3%. Consequently, these characteristics make it challenging to categorize these products as class B pellets according to ISO 17225 standards [38].
The non-linearity in the various content of the mixtures compared to the weighted average of their components can be attributed to the differences in the density of the materials used in the biomass mixtures.

3.3. Relationship between Proximate Analysis, Elemental Composition, and Biomass Calorific Values

To understand the relationship between the proximate chemical composition and elemental composition of the biomass and the NHV values obtained, as well as the contribution of each variable to the NHV value in the different mixtures studied, a principal component analysis (PCA), cluster analysis, and multiple regression analysis were performed.
In this work, PCA was used as an unsupervised exploratory technique to study the presence of discrepant samples and pattern recognition in the distribution of sludge mixtures with different biomasses. The three sludges from different WWTPs were mixed in two groups with quantities (50–50%) and (75–25%) according to their energy potential, as well as the relationships between variables and potential clusters.
The PCA produces three main principal components (PCs) that represent 93.63% of the total variation of the original dataset (Figure A1). PC1, which represents 52.33% of the original total variation, correlates positively with moisture, sulphur, ash, and nitrogen content and negatively with fixed carbon, GHV, and NHV contents. PC2, which explains 24.54% of the original total variation, correlates positively with the volatile matter and hydrogen content. As for PC3, which represents 16.76% of the original total variability, it correlates positively with carbon content and negatively with oxygen.
The results reveal a positive relationship between the heat values and fixed carbon content of the studied biomass samples, while showing a negative relationship between heat values and the moisture, sulphur, nitrogen, and ash content of these samples (Figure 1). Volatile matter and H content present a significant correlation with PC2 that is orthogonal to PC1, therefore not making a significant contribution to the variation of the heat values of the studied biomass. Carbon and oxygen content are found between PC1 and PC2, as fixed carbon and volatile matter content variation between biomass samples both contribute to the carbon and oxygen content of the biomasses. These results are in line with the simple correlation between variables, where it was found that there were a significant positive correlation between GHV and fixed carbon (r = 0.941, p < 0.05) and C (r = 0.637, p < 0.05) and a negative correlation with moisture (r = −0.768, p < 0.05), S (r = −0.653; p < 0.05), volatile matter (r = −0.423, p < 0.05), ash (r = −0.473, p < 0.05), and N (r = −0.492; p < 0.05).
The distribution of sample scores according to PC1 vs. PC2 is shown in Figure 2. Most samples are grouped near the source, but there are samples with high negative PC1 values, such as biochar (sample 7), cork powder (sample 6), and conventional biomass (sample 4).
Using the Euclidean distance and Ward’s hierarchical approach, a cluster analysis was applied to the PC1 and PC2 scores of the biomass samples. This method evaluates the distances of the clusters using an analysis of the variance methodology, seeking to minimize the sum of the squares of any two hypothetical clusters that may form in each agglomeration phase.
Figure 3 shows a formation of five clusters (considering the cut in region 11 obtained from the Connection Distances plot in Figure A2). These clusters are formed by samples that show some similarity between them.
Sample 7 (biochar), sample 6 (conventional biomass), and sample 4 (swine waste) formed isolated agglomerates. The fourth cluster consists of the samples of sludge mixtures with biochar, except for sample 15 (75% SS + 25% B), which was placed in the fifth group, despite the obvious similarity between these samples, as observed in Figure 2, where the projection of sample 15 is very close to the projected samples of the fourth cluster. The rest of the fifth cluster consists of all the samples of mixtures without biochar, the samples of WWTP sludges (1, 2, and 3), and cork powder (sample 5).
A multiple regression analysis was performed using elementary composition data to understand their relative contribution to the value of NHV. The “best subset” method with R2 Adjusted was used to generate the MLR models. The student’s t-test (p < 0.05) was used to evaluate the parameter estimates for all models. The resulting model was statistically significant (R = 0.83; F = 30.86; p < 0.001), representing 69% of the variance of the NHV, as shown in Table 6.
The moisture content has the highest weight in the model, followed by the volatile matter content (Figure A3). The moisture content has the highest beta value, indicating that it is the variable with the greatest contribution to the regression equation (Table A1). This variable shares a large percentage of its variability with the NHV (50.66%) through its zero-order regression coefficient (r = 0.712), followed by volatile matter (r = −0.433; 18.79%). The quadratic value of the structural correlation coefficient (rX,y) shows that the moisture content shares most of its variance (rX,y = 0.845; 71%) with the predicted values of NHV, followed by volatile matter (rX,y = −0.515; 26%).
The product of β×r allows us to calculate the partition of the regression effect into non-overlapping parts based on the interaction of the β coefficients and the correlation coefficients of zero order with the dependent variable [43], showing that, in this regard, the moisture content (59%) and the volatile matter content (14%) represent the majority of the variation of the regression equation. These results clearly show that, for these biomasses, the variation of the NHV is mainly explained by the variation of the moisture content, and volatile matter, to a lesser extent.

4. Conclusions

To explore the potential utilization of wastewater treatment plant sludge (WWTPS) as a viable solid fuel, elemental analysis, comprehensive elemental analysis, proximate analysis, and calorific value assessments were conducted on three distinct WWTP sludges collected from the Serzedo, Ponte da Baia-Amarante, and Santo Emilião facilities in Portugal. To enhance their physicochemical characteristics as a fuel and align with ISO standards, mixtures of these WWTPSs with other materials were analysed. The selected materials, including swine waste, cork powder, sawdust from carpentry, and biochar resulting from the pyrolysis of oak biomass, were chosen based on their environmental impact or availability.
Moreover, advanced statistical analyses, such as a principal component analysis, cluster analysis, and multiple regression analysis, unravel patterns and relationships within the dataset. These analyses unveil key factors influencing NHV values, with moisture and volatile matter content identified as critical variables.
The results suggested that using the sludges as standalone materials deviates from the ISO standard limits due to their elevated levels of nitrogen, sulphur, and ash content.
Mixtures with lower nitrogen and sulphur content, as well as favourable carbon, hydrogen, and moisture balances, have potential to enhance energy production and reduce emissions during combustion. However, despite observed improvements in net heating value (NHV) for specific mixtures, challenges persist due to the elevated levels of ash, nitrogen, and sulphur content. These challenges impact the classification of products according to ISO 17225 standards [38], highlighting the need for further consideration and optimization in the composition of these mixtures.

Author Contributions

Conceptualization, A.D.S.B.; methodology, A.D.S.B., M.O., B.M.M.T., and F.B.; validation, A.D.S.B., M.O., B.M.M.T., and F.B.; formal analysis, A.D.S.B., M.O., B.M.M.T., and F.B.; investigation, A.D.S.B., M.O., B.M.M.T., and F.B.; data curation, A.D.S.B.; writing—original draft preparation, A.D.S.B. and B.M.M.T.; writing—review and editing, M.O. and F.B.; visualization, A.D.S.B. and B.M.M.T.; supervision, A.D.S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to institutional indications.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Environments 11 00014 g0a1
Figure A1. Eigenvalues of correlation matrix.
Figure A1. Eigenvalues of correlation matrix.
Environments 11 00014 g0a1
Environments 11 00014 g0a2
Figure A2. Plot of linkage distances across steps.
Figure A2. Plot of linkage distances across steps.
Environments 11 00014 g0a2
Environments 11 00014 g0a3
Figure A3. Pareto chart of t-values for coefficients.
Figure A3. Pareto chart of t-values for coefficients.
Environments 11 00014 g0a3
Table A1. Collinearity statistics for terms in the equation.
Table A1. Collinearity statistics for terms in the equation.
EffectToleranceVarianceR2NHV Beta (β)NHV PartialNHV Semi-PairNHV tNHV p
Moisture0.97671.02390.0233−0.6982−0.7772−0.6900−6.53620.0000
Volatile matter0.97671.02390.0233−0.3537−0.5304−0.3495−3.31080.0026

References

  1. Ferreira Gregorio, V.; Pié, L.; Terceño, A. A Systematic Literature Review of Bio, Green and Circular Economy Trends in Publications in the Field of Economics and Business Management. Sustainability 2018, 10, 4232. [Google Scholar] [CrossRef]
  2. Mujtaba, M.; Fernandes Fraceto, L.; Fazeli, M.; Mukherjee, S.; Savassa, S.M.; Araujo De Medeiros, G.; Do Espírito Santo Pereira, A.; Mancini, S.D.; Lipponen, J.; Vilaplana, F. Lignocellulosic Biomass from Agricultural Waste to the Circular Economy: A Review with Focus on Biofuels, Biocomposites and Bioplastics. J. Clean. Prod. 2023, 402, 136815. [Google Scholar] [CrossRef]
  3. Sohi, S.; Lopez-Capel, E.; Krull, E.; Bol, R. Biochar, Climate Change and Soil: A Review to Guide Future Research Soils of the Atacama Desert: Reservoir for and Fingerprint of Life View Project Agricultural Nutrient-Use Efficiency View Project. 2009. Available online: https://www.researchgate.net/publication/228656328 (accessed on 23 September 2023).
  4. Stephenson, P.J.; Damerell, A. Bioeconomy and Circular Economy Approaches Need to Enhance the Focus on Biodiversity to Achieve Sustainability. Sustainability 2022, 14, 10643. [Google Scholar] [CrossRef]
  5. Demirbas, A. Competitive Liquid Biofuels from Biomass. Appl. Energy 2011, 88, 17–28. [Google Scholar] [CrossRef]
  6. Mutascu, M. Is Biomass Energy Really Sustainable in the United States? Renew. Sustain. Energy Rev. 2023, 181, 113325. [Google Scholar] [CrossRef]
  7. Wang, F.; Harindintwali, J.D.; Yuan, Z.; Wang, M.; Wang, F.; Li, S.; Yin, Z.; Huang, L.; Fu, Y.; Li, L.; et al. Technologies and Perspectives for Achieving Carbon Neutrality. Innovation 2021, 2, 100180. [Google Scholar] [CrossRef]
  8. Aguilar, R.; Ramírez, J.A.; Garrote, G.; Vázquez, M. Kinetic Study of the Acid Hydrolysis of Sugar Cane Bagasse. J. Food Eng. 2002, 55, 309–318. [Google Scholar] [CrossRef]
  9. Kumar, A.; Kumar, N.; Baredar, P.; Shukla, A. A Review on Biomass Energy Resources, Potential, Conversion and Policy in India. Renew. Sustain. Energy Rev. 2015, 45, 530–539. [Google Scholar] [CrossRef]
  10. Zhou, Y.; Xu, G.; Li, H.; Chen, Y.; Xu, X.; Li, M. Effect of Greenhouse Gas Emissions on the Life Cycle of Biomass Energy Production and Conversion under Different Straw Recycling Modes. Environ. Res. 2023, 238, 117184. [Google Scholar] [CrossRef]
  11. Kor-Bicakci, G.; Eskicioglu, C. Recent developments on thermal municipal sludge pretreatment technologies for enhanced anaerobic digestion. Renew. Sustain. Energy Rev. 2019, 110, 423–443. [Google Scholar] [CrossRef]
  12. Gao, N.; Kamran, K.; Quan, C.; Williams, P.T. Thermochemical conversion of sewage sludge: A critical review. Prog. Energy Combust. Sci. 2020, 79, 100843. [Google Scholar] [CrossRef]
  13. Zhai, Y.; Chen, H.; Xu, B.B.; Xiang, B.; Chen, Z.; Li, C.; Zeng, G. Influence of sewage sludge-based activated carbon and temperature on the liquefaction of sewage sludge: Yield and composition of bio-oil, immobilization and risk assessment of heavy metals. Bioresour. Technol. 2014, 159, 72–79. [Google Scholar] [CrossRef] [PubMed]
  14. Lopes, B.C.; Machado, E.C.; Rodrigues, H.F.; Leal, D.; Calábria De Araújo, J.; Teixeira De Matos, A. Environmental Technology Effect of alkaline treatment on pathogens, bacterial community and antibiotic resistance genes in different sewage sludges for potential agriculture use Effect of alkaline treatment on pathogens, bacterial community and antibiotic. Environ. Technol. 2020, 41, 529–538. [Google Scholar] [CrossRef] [PubMed]
  15. Brookman, H.; Gievers, F.; Zelinski, V.; Ohlert, J.; Loewen, A. Influence of hydrothermal carbonization on composition, formation and elimination of biphenyls, dioxins and furans in sewage sludge. Energies 2018, 11, 1582. [Google Scholar] [CrossRef]
  16. Chen, G.; Tian, S.; Liu, B.; Hu, M.; Ma, W.; Li, X. Stabilization of heavy metals during co-pyrolysis of sewage sludge and excavated waste. Waste Manag. 2020, 103, 268–275. [Google Scholar] [CrossRef] [PubMed]
  17. Taboada-Santos, A.; Braz, G.H.R.; Fernandez-Gonzalez, N.; Carballa, M.; Lema, J.M. Thermal hydrolysis of sewage sludge partially removes organic micropol- lutants but does not enhance their anaerobic biotransformation. Sci. Total Environ. 2019, 690, 534–542. [Google Scholar] [CrossRef] [PubMed]
  18. Xu, D.; Lin, G.; Liu, L.; Wang, Y.; Jing, Z.; Wang, S. Comprehensive evaluation on product characteristics of fast hydrothermal liquefaction of sewage sludge at different temperatures. Energy 2018, 159, 686–695. [Google Scholar] [CrossRef]
  19. Li, S.; Jiang, Y.; Snowden-Swan, L.J.; Askander, J.A.; Schmidt, A.J.; Billing, J.M. Techno-economic uncertainty analysis of wet waste-to-biocrude via hydrothermal liquefaction. Appl. Energy 2021, 283, 116340. [Google Scholar] [CrossRef]
  20. Zhen, G.; Lu, X.; Kato, H.; Zhao, Y.; Li, Y.Y. Overview of pretreatment strategies for enhancing sewage sludge disintegration and subsequent anaerobic digestion: Current advances, full-scale application and future perspectives. Renew. Sustain. Energy Rev. 2017, 69, 559–577. [Google Scholar] [CrossRef]
  21. Liu, H.; Basar, I.A.; Eskicioglu, C. Hydrothermal liquefaction for sludge-to-energy conversion: An evaluation of biocrude production and management of waste streams. Energy 2023, 281, 128268. [Google Scholar] [CrossRef]
  22. Angnes, G. Gas Emissions in the Swine Manure Composting Process; Federal University of Santa Catarina, Centre for Agrarian Sciences: Florianopolis, Brazil, 2012. (In Portuguese) [Google Scholar]
  23. Wang, X.B.; Nan, W.U.; Cai, R.J.; Geng, W.N.; Xu, X.Y. Changes in speciation, mobility and bioavailability of Cd, Cr and As during the transformation process of pig manure by black soldier fly larvae (Hermetia illucens). J. Integr. Agric. 2021, 20, 1157–1166. [Google Scholar] [CrossRef]
  24. Lopez-Real, J.; Baptista, M. A Preliminary Comparative Study of Three Manure Composting Systems and their Influence on Process Parameters and Methane Emissions. Compost. Sci. Util. 1996, 4, 71–82. [Google Scholar] [CrossRef]
  25. Romero, C.M.; Li, C.; Owens, J.; Ribeiro, G.O.; Mcallister, T.A.; Okine, E.; Hao, X. Nutrient cycling and greenhouse gas emissions from soil amended with biochar-manure mixtures. Pedosphere 2021, 31, 289–302. [Google Scholar] [CrossRef]
  26. Wang, H.; Magesan, G.N.; Bolan, N.S. An overview of the environmental effects of land application of farm effluents. (Special Issue: Land application of farm wastes). N. Z. J. Agric. Res. 2004, 47, 389–403. [Google Scholar] [CrossRef]
  27. Berenguer, P.; Cela, S.; Santiveri, F.; Boixadera, J.; Lloveras, J. Copper and zinc soil accumulation and plant concentration in irrigated maize fertilized with liquid swine manure. Agron. J. 2008, 100, 1056–1061. [Google Scholar] [CrossRef]
  28. Wang, N.; Wu, X.; Liao, P.; Zhang, J.; Liu, N.; Zhou, Z.; Zhang, L. Morphological transformation of heavy metals and their distribution in soil aggregates during biotransformation of livestock manure. Biocatal. Agric. Biotechnol. 2021, 32, 101963. [Google Scholar] [CrossRef]
  29. Formentini, T.A.; Basile-Doelsch, I.; Legros, S.; Borschneck, D.; Venzon, J.S.; Pinheiro, A.; Doelsch, E. Redistribution of Zn towards light-density fractions and potentially mobile phases in a long-term manure-amended clayey soil. Geoderma 2021, 394, 115044. [Google Scholar] [CrossRef]
  30. Mattias, J.L. Heavy Metals in Soils under Swine Liquid Manure Application in Two Watersheds in Santa Catarina. Ph.D. Thesis, Federal University of Santa Maria, Santa Maria, Brazil, 2006. (In Portuguese). [Google Scholar]
  31. Penuelas, J.; Sardans, J.; Alcaniz, J.M.; Poch, J.M. Increased eutrophication and nutrient imbalances in the agricultural soil of NE Catalonia, Spain. J. Environ. Biol. 2009, 30, 841–846. [Google Scholar]
  32. ISO 18134-1:2015; Solid Biofuels—Determination of Moisture Content Oven Dry Method. ISO: Geneva, Switzerland, 2015.
  33. ISO 18122:2015; Solid Biofuels—Determination of Ash Content. ISO: Geneva, Switzerland, 2015.
  34. ISO 18123:2015; Solid Biofuels—Determination of the Content of Volatile Matter. ISO: Geneva, Switzerland, 2015.
  35. ISO 16948:2015; Solid Biofuels—Determination of Total Content of Carbon, Hydrogen and Nitrogen. ISO: Geneva, Switzerland, 2015.
  36. ISO 18125:2017; Solid Biofuels—Determination of Calorific Value. ISO: Geneva, Switzerland, 2015.
  37. Liu, H.; Basar, I.A.; Nzihou, A.; Eskicioglu, C. Hydrochar derived from municipal sludge through hydrothermal processing: A critical review on its formation, characterization, and valorization. Water Res. 2021, 199, 117186. [Google Scholar] [CrossRef]
  38. ISO 17225-6:2014; Solid Biofuels—Fuel Specifications and Classes—Part 6: Graded Non-Woody Pellets. ISO: Geneva, Switzerland, 2014.
  39. Zhang, Q.; Zeng, L.; Fu, X.; Pan, F.; Shi, X.; Wang, T. Comparison of anaerobic co-digestion of pig manure and sludge at different mixing ratios at thermophilic and mesophilic temperatures. Bioresour. Technol. 2021, 337, 125425. [Google Scholar] [CrossRef]
  40. Wilson, M. Manure and Nutrients: A Complicated Relationship; Minnesota Crop News; University of Minnesota Extension: St. Paul, MN, USA, 2017; Available online: https://blog-crop-news.extension.umn.edu/2017/11/manure-and-nutrients-complicated.html (accessed on 13 July 2021).
  41. Di Bella, G.; Corsino, S.F.; De Marines, F.; Lopresti, F.; La Carrubba, V.; Torregrossa, M.; Viviani, G. Occurrence of Microplastics in Waste Sludge of Wastewater Treatment Plants: Comparison between Membrane Bioreactor (MBR) and Conventional Activated Sludge (CAS) Technologies. Membranes 2022, 12, 371. [Google Scholar] [CrossRef] [PubMed]
  42. Núñez-Regueira, L.; Rodríguez-Añon, J.A.; Proupín-Castiñeiras, J.; Vilanova-Diz, A.; Montero-Santoveña, N. Determination of calorific values of forest waste biomass by static bomb calorimetry. Thermochim Acta 2001, 371, 23–31. [Google Scholar] [CrossRef]
  43. Nathans, L.L.; Oswald, F.L.; Nimon, K. Multiple linear regression: A guidebook of variable importance. Pract. Assess. Res. Evaluation. 2012, 17, 1–19. [Google Scholar]
Environments 11 00014 g001
Figure 1. Projection of the variables on the factor plane (1 × 2).
Figure 1. Projection of the variables on the factor plane (1 × 2).
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Figure 2. Projection of the samples on the factor plane (1 × 2).
Figure 2. Projection of the samples on the factor plane (1 × 2).
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Figure 3. Tree diagram for 31 samples.
Figure 3. Tree diagram for 31 samples.
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Table 1. Chemical composition of elemental biomass analysis.
Table 1. Chemical composition of elemental biomass analysis.
IDSampleNd (%)Cd (%)Hd (%)Sd (%)Od (%)
1Serzedo WWTP sludge8.12 ± 0.37148.82 ± 0.3666.76 ± 0.0970.66 ± 0.0279.34 ± 0.356
2Ponte da Baia-Amarante WWTP sludge6.86 ± 0.36435.94 ± 0.3344.59 ± 0.1340.84 ± 0.04321.87 ± 0.618
3Santo Emilião WWTP sludge8.54 ± 0.35142.97 ± 0.3085.25 ± 0.1440.68 ± 0.02422.29 ± 0.455
4Swine waste2.68 ± 0.32940.03 ± 0.2994.55 ± 0.0590.14 ± 0.01538.49 ± 0.285
5Cork powder0.62 ± 0.37962.74 ± 0.2897.31 ± 0.123ND28.79 ± 0.710
6Conventional biomass1.11 ± 0.33847.24 ± 0.2655.22 ± 0.172ND45.80 ± 0.700
7Biochar2.14 ± 0.36050.70 ± 0.2891.51 ± 0.076ND34.35 ± 0.223
ND—Not Detected.
Table 2. Proximate analysis of biomasses.
Table 2. Proximate analysis of biomasses.
IDSampleMoisturead (%)Volatile Matterd (%)Ashd (%)Fixed Carbond (%)
1Serzedo WWTP sludge9.77± 0.15753.24 ± 0.01026.30 ± 0.02810.69 ± 0.169
2Ponte da Baia-Amarante WWTP sludge8.88 ± 0.11751.00 ± 0.01829.90 ± 0.02610.22 ± 0.097
3Santo Emilião WWTP sludge11.32 ± 0.19456.21 ± 0.01320.27 ± 0.01112.20 ± 0.192
4Swine waste8.86 ± 0.12456.97 ± 0.01214.11 ± 0.02120.06 ± 0.115
5Cork powder2.11 ± 0.11866.14 ± 0.0120.54 ± 0.02431.21 ± 0.153
6Conventional biomass8.26 ± 0.18576.01 ± 0.0150.63 ± 0.02815.10 ± 0.144
7Biochar3.89 ± 0.15334.10 ± 0.01911.30 ± 0.01550.71 ± 0.157
Table 3. NHV and GHV values.
Table 3. NHV and GHV values.
IDSampleGHV (MJ/kg)NHV (MJ/kg)
1Serzedo WWTP sludge18.09 ± 0.42116.70 ± 0.441
2Ponte da Baia-Amarante WWTP sludge15.82 ± 0.03314.87 ± 0.054
3Santo Emilião WWTP sludge18.52 ± 0.13917.44 ± 0.160
4Swine waste17.51 ± 0.03716.58 ± 0.029
5Cork powder27.24 ± 0.06225.73 ± 0.037
6Conventional biomass19.79 ± 0.01818.72 ± 0.027
7Biochar32.87 ± 0.83532.56 ± 0.826
Table 4. Results of the physicochemical characterization of the mixture (75 + 25%).
Table 4. Results of the physicochemical characterization of the mixture (75 + 25%).
ID121314152021222328293031
Sample75% SS + 25% CB75% SS + 25% SB75% SS + 25% CP75% SS + 25% B75% PBAS + 25% CB75% PBAS + 25% SB75% PBAS + 25% CP75% PBAS + 25% B75% SES + 25% CB75% SES + 25% SB75% SES + 25% CP75% SES + 25% B
Nd (%)5.783 ± 0.3106.620 ± 0.3466.020 ± 0.2646.042 ± 0.3395.796 ± 0.2625.747 ± 0.3995.901 ± 0.3806.045 ± 0.3377.208 ± 0.2936.456 ± 0.3145.841 ± 0.3866.863 ± 0.253
Cd (%)48.780 ± 0.37945.953 ± 0.34451.789 ± 0.32351.171 ± 0.31339.164 ± 0.48638.157 ± 0.42645.242 ± 0.25240.416 ± 0.43943.631 ± 0.28843.226 ± 0.31248.315 ± 0.45245.312 ± 0.265
Hd (%)6.105 ± 0.1305.600 ± 0.0696.247 ± 0.1535.590 ± 0.1564.910 ± 0.0944.694 ± 0.0885.706 ± 0.1314.156 ± 0.1025.309 ± 0.1315.206 ± 0.1535.924 ± 0.1624.472 ± 0.170
Sd (%)0.511 ± 0.0470.576 ± 0.0620.515 ± 0.0370.494 ± 0.0130.646 ± 0.0690.636 ± 0.0140.652 ± 0.0850.665 ± 0.0660.567 ± 0.0990.494 ± 0.0160.419 ± 0.0580.510 ± 0.059
Od (%)18.440 ± 0.73717.503 ± 0.76515.071 ± 0.58414.653 ± 0.19327.402 ± 0.81127.097 ± 0.71619.588 ± 0.78324.095 ± 0.89228.344 ± 0.38025.392 ± 0.29023.665 ± 0.53425.221 ± 0.418
Moisturead (%)9.592 ± 0.1799.744 ± 0.1288.355 ± 0.1568.800 ± 0.1278.626 ± 0.1508.772 ± 0.1687.823 ± 0.1618.083 ± 0.16910.357 ± 0.19410.207 ± 0.1459.518 ± 0.1389.493 ± 0.172
Volatile Matterd (%)58.435 ± 0.01953.673 ± 0.01356.965 ± 0.01948.955 ± 0.01557.753 ± 0.02053.088 ± 0.01355.302 ± 0.01746.017 ± 0.02060.510 ± 0.01455.898 ± 0.01459.190 ± 0.01449.665 ± 0.016
Ashd (%)20.380 ± 0.01023.748 ± 0.02920.358 ± 0.02122.051 ± 0.02022.082 ± 0.01523.671 ± 0.01822.911 ± 0.02424.623 ± 0.01314.941 ± 0.02919.226 ± 0.02715.836 ± 0.02117.622 ± 0.024
Fixed Carbond (%)11.592 ± 0.18212.834 ± 0.14414.322 ± 0.19420.195 ± 0.12211.538 ± 0.14614.469 ± 0.19213.964 ± 0.20221.277 ± 0.13714.192 ± 0.22614.669 ± 0.13615.456 ± 0.16423.220 ± 0.191
GHVd (MJ/kg)18.546 ± 0.27917.940 ± 0.17120.043 ± 0.73321.779 ± 0.49317.129 ± 0.12816.435 ± 0.03118.902 ± 0.19119.991 ± 0.17819.025 ± 0.03418.269 ± 0.09620.720 ± 1.61221.954 ± 0.203
NHVd (MJ/kg)17.391 ± 0.30216.786 ± 0.15818.756 ± 0.74620.833 ± 0.48716.117 ± 0.11215.467 ± 0.03317.726 ± 0.21219.134 ± 0.16117.930 ± 0.05617.196 ± 0.06519.499 ± 1.64521.032 ± 0.222
SS: Serzedo WWTP Sludge; PBAS: Ponte da Baia-Amarante WWTP Sludge; SES: Santo Emilião WWTP Sludge; CB: Conventional Biomass; SB: Swine Biomass; CP: Cork Powder; B: Biochar.
Table 5. Results of the physic-chemical characterization of the mixture (50 + 50%).
Table 5. Results of the physic-chemical characterization of the mixture (50 + 50%).
ID8910111617181924252627
Sample50% SS + 50% CB50% SS + 50% SB50% SS + 50% CP50% SS + 50% B50% PBAS + 50% CB50% PBAS + 50% SB50% PBAS + 50% CP50% PBAS + 50% B50% SES + 50% CB50% SES + 50% SB50% SES + 50% CP50% SES + 50% B
Nd (%)4.707 ± 0.3875.439 ± 0.3644.222 ± 0.2674.933 ± 0.3594.184 ± 0.3714.774 ± 0.3753.964 ± 0.2634.599 ± 0.3695.464 ± 0.3395.369 ± 0.3515.258 ± 0.3645.193 ± 0.377
Cd (%)49.016 ± 0.38143.025 ± 0.42555.305 ± 0.25349.943 ± 0.47842.966 ± 0.38038.663 ± 0.37950.134 ± 0.48741.696 ± 0.37145.375 ± 0.40142.843 ± 0.47351.125 ± 0.40347.393 ± 0.330
Hd (%)6.151 ± 0.0975.346 ± 0.1246.981 ± 0.1194.227 ± 0.1065.023 ± 0.0534.608 ± 0.1606.111 ± 0.1433.849 ± 0.1425.298 ± 0.1105.044 ± 0.1296.228 ± 0.1023.885 ± 0.123
Sd (%)0.314 ± 0.0670.456 ± 0.0290.359 ± 0.0340.233 ± 0.0820.408 ± 0.0740.484 ± 0.0430.391 ± 0.0410.345 ± 0.0830.406 ± 0.0460.393 ± 0.0540.363 ± 0.0720.357 ± 0.080
Od (%)26.315 ± 0.54925.055 ± 0.62920.008 ± 0.16022.360 ± 0.12033.809 ± 0.22329.164 ± 0.43924.181 ± 0.37929.387 ± 0.70233.388 ± 0.23129.611 ± 0.60227.122 ± 0.11826.884 ± 0.421
Moisturead (%)9.148 ± 0.1219.848 ± 0.1526.449 ± 0.1186.880 ± 0.1198.650 ± 0.1109.203 ± 0.1045.495 ± 0.1926.822 ± 0.1179.656 ± 0.15410.081 ± 0.1596.814 ± 0.1487.705 ± 0.151
Volatile Matterd (%)63.054 ± 0.01954.998 ± 0.01860.198 ± 0.01844.169 ± 0.01463.104 ± 0.01854.301 ± 0.01358.569 ± 0.01641.647 ± 0.01566.444 ± 0.01556.560 ± 0.01862.173 ± 0.01746.152 ± 0.019
Ashd (%)13.497 ± 0.01420.678 ± 0.01413.124 ± 0.02918.303 ± 0.02413.610 ± 0.01122.306 ± 0.01415.220 ± 0.01320.123 ± 0.01310.068 ± 0.01116.740 ± 0.0259.904 ± 0.02716.288 ± 0.020
Fixed
Carbond (%)		14.302 ± 0.11914.475 ± 0.14920.230 ± 0.10930.649 ± 0.10914.636 ± 0.08414.189 ± 0.12220.716 ± 0.16331.408 ± 0.08813.832 ± 0.17616.619 ± 0.14421.109 ± 0.13929.855 ± 0.184
GHVd (MJ/kg)19.014 ± 0.03817.941 ± 0.13022.613 ± 0.51225.744 ± 1.22718.221 ± 0.47416.854 ± 0.03021.037 ± 1.08524.080 ± 0.28119.173 ± 0.22518.077 ± 0.09422.419 ± 2.00725.397 ± 0.642
NHVd (MJ/kg)17.746 ± 0.04916.840 ± 0.15521.278 ± 0.50025.079 ± 1.24917.186 ± 0.46415.904 ± 0.01719.623 ± 1.09523.492 ± 0.26318.081 ± 0.23217.038 ± 0.10521.136 ± 2.00724.390 ± 0.666
Table 6. Test of SS Whole Model vs. Residual SS.
Table 6. Test of SS Whole Model vs. Residual SS.
Dependent VariableMultiple
R		Multiple
R2		Adjusted
R2		SS
Model		Df
Model		MS ModelSS
Residual		Residual dfMS
Residual		Fp
NHV0.82940.68790.6656288.51422144.2571130.8501284.673230.86890.001
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Borges, A.D.S.; Oliveira, M.; Teixeira, B.M.M.; Branco, F. Co-Valorisation Energy Potential of Wastewater Treatment Sludge and Agroforestry Waste. Environments 2024, 11, 14. https://doi.org/10.3390/environments11010014

AMA Style

Borges ADS, Oliveira M, Teixeira BMM, Branco F. Co-Valorisation Energy Potential of Wastewater Treatment Sludge and Agroforestry Waste. Environments. 2024; 11(1):14. https://doi.org/10.3390/environments11010014

Chicago/Turabian Style

Borges, Amadeu D. S., Miguel Oliveira, Bruno M. M. Teixeira, and Frederico Branco. 2024. "Co-Valorisation Energy Potential of Wastewater Treatment Sludge and Agroforestry Waste" Environments 11, no. 1: 14. https://doi.org/10.3390/environments11010014

APA Style

Borges, A. D. S., Oliveira, M., Teixeira, B. M. M., & Branco, F. (2024). Co-Valorisation Energy Potential of Wastewater Treatment Sludge and Agroforestry Waste. Environments, 11(1), 14. https://doi.org/10.3390/environments11010014

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