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Article

Harnessing Livestock and Vineyard Residues for Sustainable Energy Production in Portugal

by
Miguel Oliveira
1,
Fernando Hermínio Ferreira Milheiro Nunes
2 and
Amadeu Duarte da Silva Borges
3,*
1
CQ-VR, Chemistry Research Centre—Vila Real, Laboratory of Thermal Sciences and Sustainability, University of Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
2
CQ-VR, Chemistry Research Centre—Vila Real, Food and Wine Chemistry Lab, Chemistry Department, University of Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
3
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
*
Author to whom correspondence should be addressed.
Clean Technol. 2025, 7(1), 1; https://doi.org/10.3390/cleantechnol7010001
Submission received: 7 October 2024 / Revised: 30 November 2024 / Accepted: 20 December 2024 / Published: 24 December 2024
Browse Figures
Figure 1
<p>(<b>A</b>) Projection of the variables on the factor-plane (1 × 2); (<b>B</b>) projection of the cases on the factor-plane (1 × 2); (<b>C</b>) tree diagram for 53 cases.</p> "> Figure 2
<p>Pareto chart of t-values for coefficients.</p> "> Figure 3
<p>Representativity of renewable energy sources in total primary energy consumption in Portugal.</p> ">
Versions Notes

Abstract

:
This study investigates the potential of utilizing livestock waste and vineyard residues for sustainable energy production in Portugal. Through the physical and chemical characterization of swine waste, grape seeds and skins, cork powder, sawdust, and biochar, 53 distinct samples, including 11 individual biomasses and their derived mixtures, were analyzed to identify optimal combinations for biofuel pellet production. The best-performing mixture, composed of 50% swine waste, 25% grape seeds and skins, and 25% cork powder, achieved a Lower Heating Value (LHV) of 18.34 MJ/kg and low ash content, qualifying it as a class B pellet. This mixture offers significant energy potential while minimizing environmental impacts. The research also presents three energy valorization scenarios, with the most balanced scenario meeting up to 6% of Portugal’s electricity demand and providing energy savings equivalent to 485,463 tons of oil equivalent (toe) annually. A case study on a “Case Study Farm” in the Douro region, managing 2000 pigs and producing 500 tons of wine grapes annually, demonstrated that implementing the optimal biomass mixture could generate 3854 MWh of heat and 1156 MWh of electricity per year. This could result in annual revenues of EUR 189,258 from pellet sales, covering the initial investment of EUR 283,938 within 6.36 years, with a total surplus of EUR 689,666 over 20 years. These findings highlight the economic viability and environmental benefits of converting agricultural waste into renewable energy, contributing to Portugal’s carbon neutrality and reducing energy dependence.

1. Introduction

Due to the depletion of fossil fuels and the need to minimize greenhouse gas (GHG) emissions, biomass has significantly increased its use for heat and power generation [1]. As climate change poses a significant threat to our planet, the concept of circular economy has emerged as vital to ensuring sustainable development. Therefore, it is imperative to implement effective measures to reduce GHG emissions, aiming for decarbonization and clean energy production [2,3]. The climate and energy policy goals of the European Union (EU) for 2030 include a 40% reduction in GHG emissions, a 27% increase in renewable energy installed capacity, and a 27% improvement in energy efficiency [4].
The global pellet market has recently experienced rapid growth, and substantial future growth is anticipated. However, due to the rising need for biomass, the previously used sawmill and pulp and paper industry wood scraps will not be enough to meet future demands. As a result, the pellet industry is facing new challenges due to an increased raw material base that now includes a variety of fibrous waste products from the food and agricultural industries [5]. Residual biomass such as agricultural and livestock waste has emerged as a promising source of raw material for bioenergy production, aligning with circular economy principles and the EU’s renewable energy targets [6]. This shift is essential not only to meet growing demand but also to mitigate the environmental issues associated with improper waste disposal.
Before industrialization, farm animals were one of the primary options for managing nutrient flows in the agroecosystem due to the significant volumes of plant nutrients generated by their manure [7]. However, intensive pig production has led to high animal density per surface area, resulting in large amounts of waste that exceed the soil’s capacity to support them [8,9]. When slurry is applied to crops, it can cause methane emissions [10,11], an excess of nutrients, and subsequent leaching into the surrounding environment [11]. Excessive slurry application has been linked to higher nitrogen (N) and phosphorus (P) levels in water bodies [11,12], as well as significant amounts of copper (Cu) and zinc (Zn) in soils [13,14]. This nutrient enrichment of water bodies causes imbalances in aquatic ecosystems such as surface water eutrophication, nitrate contamination of soil and water, and effects on the soil microbial community [15,16,17].
Due to the lack of commercial value in the wine industry, residues like stalks, skins, seeds, and pruning residues are often discarded as waste in southern European countries. Producers typically burn these materials in agricultural lands as a means of disposal, which can cause serious problems. For instance, this can lead to the development of parasites that are harmful to future cultivation and the spread of uncontrolled fires [18]. In addition, these residues have high organic content, making them an underutilized resource for energy production [19]. Research has shown that the energy potential of vineyard residues, particularly grape pomace and pruning biomass, can be harnessed effectively through biofuel production, thereby reducing environmental impacts and promoting renewable energy generation [20].
Combining livestock waste with vineyard residues to produce energy has shown promise in addressing both environmental and energy-related challenges. Studies have demonstrated that blending these residues can balance their individual shortcomings, such as the high nitrogen and ash content of livestock waste and the variability of grape residue properties [21]. Moreover, this approach aligns with sustainability goals by converting waste materials into valuable energy resources, reducing emissions, and alleviating disposal challenges [22].
In this study, a solid fuel was developed by utilizing agricultural waste and by-products from the wine industry, with the aim of reducing their environmental impact and contributing to the reduction of energy dependence on foreign sources. This effort also aligns with Portugal’s carbon (C) neutrality commitments. Furthermore, the study examines how optimizing biomass mixtures can enhance the efficiency and sustainability of pellet production, addressing both local and national energy demands.

2. Material and Methods

In this study, various types of biomasses were collected from different sources and characterized using several physical and chemical analysis techniques. The purpose of the physical–chemical characterization was to determine the basic properties of the samples, including the moisture content, volatile matter, ash content, and fixed carbon content. Elemental analysis was conducted to determine the C, hydrogen (H), N, and Sulfur (S) content of the samples. Calorimetry was used to determine the heat value of the samples, which is a measure of their energy content. Finally, the potential energetic valorization of swine and wine by-products in Portugal was calculated based on data on pig and wine grape production and reference prices for electricity and natural gas in the country.

2.1. Samples

Biomass from swine waste were collected at livestock facilities located in the University of Trás-os-Montes e Alto Douro (UTAD, Vila Real, Portugal). By-products of wine grape production that included three red varieties: Touriga Franca, Tinta Roriz, and Tinta Barroca; and three white varieties: Viosinho, Malvasia Fina, and Manteúdo Branco were kindly supplied by Quinta do Portal (Sabrosa, Vila Real, Portugal). Cork powder was supplied by A.J.Gomes & Cia. Lda, a cork stopper industry located in Paços de Brandão, Aveiro, Portugal; biochar was obtained in the Laboratory of Thermal Sciences and Sustainability from the pyrolysis of pruning of American oak; and sawdust was obtained from the UTAD’s carpentry.
In total, 53 distinct samples were analyzed, comprising 11 individual biomasses and their corresponding mixtures. These mixtures were designed to optimize biofuel pellet production by combining different proportions of the biomass types mentioned above. Table 1 summarizes the sample identification and the composition of each mixture, which were developed to evaluate their potential for biofuel applications.

2.2. Physical–Chemical Characterization

2.2.1. Sample Drying

Stalks were oven-dried under conditions simulating natural drying at a local ambient temperature of 30 °C until achieving constant weight. Seeds and skins of the Touriga Franca variant were dried together under the same conditions. Analysis was performed in duplicate. Swine waste was air-dried until no further weight change was observed. All the samples were then ground and stored in hermetically sealed in zip bags.

2.2.2. Proximate Analysis

About 1 g of each sample of biomass and mixtures were weighed, and their initial masses were registered to the nearest 0.1 g according to ISO 18134-3 [23], ISO 18122 [24], and ISO 18123 [25] standards.
The samples were dried in a Protherm PLF 100/6 furnace (Protherm, Ankara, Turkey) set to 105 °C, until their weight remained constant, confirming the removal of all moisture. The final dried weight of each sample was recorded, and the moisture content was calculated based on the weight difference before and after drying.
A portion of the dried biomass from the moisture analysis was weighed and heated in a closed crucible at 550 °C until the weight stabilized, indicating the volatilization of organic components. The volatile matter content (%) was calculated from the weight loss observed during this step.
Following the volatile matter determination, the residual material was subjected to ashing at 550 °C in a muffle furnace until a constant weight was achieved, signifying the complete combustion of organic matter.
Fixed carbon content (%) was determined by subtracting the moisture, volatile matter, and ash contents from 100%.
To ensure accurate temperature readings and consistent furnace operation, Type K thermocouples were used for calibration. The calibration process involved verifying the furnace’s temperature accuracy against reference points provided by the thermocouples, ensuring reliable performance across its operating range.

2.2.3. Elemental Analysis

Approximately 2 mg of each biomass sample was weighed and recorded for analysis. The elemental analysis (C, H, N, S) was conducted on all samples according to ISO 16948 [26], using a ThermoScientific FlashSmart CHNS/O elemental analyzer (ThermoScientific, Waltham, MA, USA). The analyzer was calibrated with sulfanilamide, cystine, and BBOT (1,3-butanediol bis(4-benzyloxycarbonyl)oxyethyl ester) to ensure accurate and reliable performance. Oxygen (O) content was determined by subtracting the measured values for carbon, hydrogen, nitrogen, sulfur, and ash content from 100%.

2.2.4. Calorimetry

Approximately 1 g of pellet from each of the 53 samples was combusted in an isoperibolic calorimeter (Model 6300, Parr Instruments Co., Moline, IL, USA) under controlled conditions, following ISO 18125 standards [27]. The calorimeter was calibrated using a benzoic acid standard (Parr Benzoic Acid No. 3415, Parr Instruments Co., Moline, IL, USA). The NHV was determined in compliance with ISO 18125.

2.3. Classification of Pellets: Standards for Class A, and Class B

The ISO 17225-6:2014 standard defines the classification and quality requirements for graded non-woody pellets, which are derived from agricultural residues, herbaceous biomass, fruit biomass, or mixtures of these materials. These pellets are commonly used as solid biofuels, with quality parameters ensuring their suitability for specific applications, ranging from domestic heating to large-scale industrial energy production [28].
The classification of non-woody biomass pellets according to [28] involves a range of parameters that determine their suitability for specific applications. While the standard covers an extensive set of parameters, this study focuses on those we were able to measure and evaluate, including ash content, nitrogen, sulfur, moisture, and low heating value. These parameters are critical in determining the environmental and energy performance of pellets. Table 2 summarizes the quality thresholds for these parameters across the two ISO 17225-6:2014 classes.

2.4. Statistical Analysis

Principal Component Analysis (PCA) was employed to reduce the complexity of the dataset and identify patterns in the biomass composition. PCA transforms the original variables into uncorrelated principal components, which are linear combinations of the input variables and capture the total variance. This unsupervised technique requires no prior grouping, making it suitable for exploratory analyses of high-dimensional data.
Before applying PCA, the data were standardized to zero mean and unit variance to eliminate scale bias among variables. This preprocessing step ensures that PCA results accurately reflect the relationships in the data, irrespective of variable magnitudes. The reduced dimensionality provided by PCA simplifies the interpretation of the dataset and facilitates linking patterns to the chemical composition of the biomass samples.
Cluster analysis was conducted on the first two principal components (PC1 and PC2) to examine sample similarities. Euclidean distance was used to measure similarity between data points, and Ward’s method was selected to form clusters by minimizing within-cluster variance. The optimal number of clusters was determined by analyzing the dendrogram and identifying a significant gap in the linkage distances, which reflects the natural structure of the data.

2.5. Energetic Valorisation of Swine and Wine By-Products

To calculate the potential energetic valorization of the swine and wine by-products in Portugal, the average annual production of pigs and wine grape in Portugal from 2012 to 2016 were used with data supplied by Instituto Nacional de Estatística (2019) [29]. The average production of manure per day per animal of 2.25 kg was used according to [30], considering that about half of its mass is water [31]. The quantity of winery by-products after drying them up at ambient temperature was calculated according to [32,33]. The reference prices of electricity and natural gas in Portugal in 2020 were supplied by Entidade Reguladora dos Serviços Energéticos (ERSE) [34].

3. Results and Discussion

3.1. Physical–Chemical Characterization of Biomasses

All biomasses have a moisture content of less than 12% (Table 3), which is considered the maximum moisture limit in the production of class A pellets described in ISO17225-6:2014 [28]. Cork powder was the sample with the lowest moisture content (2.11%). Regarding volatile matter, biochar had the lowest content (34.10%), as expected due to the result of the pyrolysis process, and sawdust had the higher volatile matter content with 76.02%. Swine waste presented the highest ash content (14.10%) of all studied biomasses; this value hampers the production of standardized pellets solely with this biomass [28].
Touriga Franca, Malvasia Fina, Tinta Barroca, Tinta Roriz, Manteúdo Branco, and Viosinho are all grape varieties that have similar properties, with moderate moisture content and volatile matter but high ash content. These samples had relatively low nitrogen, and no Sulfur content was detected (Table 4), making them a good option for pellet production. On the other hand, cork powder, grape seeds and skins, and sawdust had the lowest levels of ash (0.54%, 2.90% and 0.63% respectively). The ash content of the stalks was exceedingly high when compared to sawdust or cork powder, both of which are arboreous biomasses like the stalks. This may be have been due to the presence of elements such as K, Na, Ca, and Mg [35].
Biochar had the lowest hydrogen content of any biomass (1.45%) since a significant amount of hydrogen was removed during the pyrolysis process.
Because swine waste and biochar contained more than 2% nitrogen, it was impossible to produce a standardized pellet from these materials alone [28]. All other biomasses had a nitrogen content of less than 1%, the maximum value for class A pellet classification, with cork powder having the lowest nitrogen content (0.61%).
Swine waste had a sulfur content of 0.13%, which is below than what is required for a class A pellet. A small amount of sulfur was detected in the seeds and skins samples.
Swine waste presented the lowest LHV of all biomasses studied (14.90 MJ/kg). On the other hand, biochar presented the highest LHV of all biomasses studied (33.35 MJ/kg). The LHV of cork powder and seeds and skin samples was greater than 20 MJ/kg. Grape stalks presented an LHV of around 15.5 MJ/kg, with Touriga Franca being the grape cultivar with the highest LHV (15.85 MJ/kg). Although swine waste had the lowest LHV, it was higher than the ISO standard requirement for non-woody pellets (ISO17225-6:2014).
These results are consistent with the values found in literature. In one study, swine waste was C (%) 35.59, N (%) 1.72, H (%) 4.84, and S (%) 0.44 [36]. Small differences in the results obtained with those described in the literature can be explained by animal species and diet, bedding, storage, and handling of the manure [37]. In another study, an unprocessed grape stalks and pomace substrate was subjected to a proximate and elemental analysis [35]. The results of this study’s elemental analysis are similar to the ones found on the aforementioned study. The differences found on the proximate analyses could be explained by the higher moisture content measured by the authors (20.2%).

3.2. Using the Combination of Biomasses for Improving Swine Waste Use as Fuel

While maintaining a swine waste base, mixtures of biomasses with various stalk contents were studied. To take advantage of cork powder’s high energy value and low ash content, a mixture containing 20% cork powder was considered. This challenge is likely due to cork powder’s unique properties, including its high suberin and wax content [38,39], which hinder particle adhesion, and its low moisture content, which reduces the plasticity and compressibility needed for pellet formation. At higher cork powder concentrations, these factors further disrupted the biomass matrix, making pelletization impossible.
The mixture of two materials did not show a significant improvement in ash content when compared to swine waste, and in some cases, there was an increase in ash content, such as in sample 32, which has the highest ash content verified at 16.65% (Table 5). Sample 27 had the lowest ash content verified (11.18%). As a result, it would be impossible to create a pellet from swine waste and grape stalk that is suitable for ISO 17225-6:2014 classification.
The hydrogen content of the various mixtures was around 4% (Table 6). The lowest recorded value was 3.92% for sample 31, and sample 32 had the highest hydrogen content verified (4.75%). Combining swine waste with other materials appeared to reduce the sulfur content, as no sulfur was detected in sample 23. Notably, an increase in the Lower Heating Value (LHV) of the pellets was observed when 30 percent of stalk content from red grape varieties was introduced into swine waste mixtures. In contrast, mixtures containing white grape varieties showed no significant variations. However, for higher stalk contents, there were no substantial enhancements in the LHV.
Mixtures of swine waste and two other biomasses were created to increase LHV while decreasing ash and sulfur content. These samples were made with the Touriga Franca cultivar stalks, which presented the highest LHV.
The sample with the lowest moisture content was sample 41 (6.35%). The mixture with the highest moisture content was sample 39 (Table 7). In general, the moisture contents of these mixtures were lower than those of two-material mixtures. The content of ashes in the mixtures decreased, maintaining the values between 8.23% and 13.18%. Sample 44 was the mixture with the lowest content of ashes. Four mixtures were obtained with contents of ash lower than 10%, which allowed us to classify them as class B pellets. There was a slight decrease in the amount of ash in the mixtures, which remained between 8.23 and 13.18%. Sample 44 had the lowest ash content.
Four combinations were obtained with ash concentrations less than 10%, allowing them to be classified as class B pellets.
Sample 41 had a hydrogen content of 5.84% (Table 8). However, this sample revealed a nitrogen content above that required to produce a class A pellet but sufficient to produce one of class B. The LHV of the pellets increased as compared to samples of only two components. The seeds and skins, in general, proved to be a better base than the stalks since they achieved higher LHV values. The maximum LHV was found in sample 45, with 19.10 MJ/kg. The sample with the lowest LHV confirmed was sample 39.
Sample 41 stood out as the best-performing mixture due to its balanced characteristics across key parameters. While other samples exhibited better performance in individual aspects—such as higher LHV or lower ash content—these advantages were often offset by unfavorable levels of nitrogen, sulfur, or other components. Sample 41 achieves the optimal compromise across all parameters, making it the best overall performer for producing class B pellets.
Mixtures of swine waste samples enriched with three biomasses, along with a swine waste sample containing 10% of each of the other biomasses, were investigated. The combination of four materials did not significantly enhance the ash content, which remained within the range from 9.63% and 11.96%, the sample 49 exhibiting the highest ash content (Table 9). Nevertheless, two additional mixtures with ash levels low enough to be classified as class B pellets were identified.
Hydrogen contents of these samples varied between 3.22% and 4.66% for samples 52 and 47, respectively (Table 10). Nitrogen contents verified for these mixtures varied between 1.32% for sample 51 and 1.86% for sample 53. LHV values stayed very close to 16 MJ/kg when Touriga Franca stalk was used as a second basis. The LHV of these mixtures ranged from 15.95 MJ/kg for sample 48 to 17.24 MJ/kg for sample 53.
These results show that by combining different biomass residues, it was possible to obtain pellets with enough quality to be commercialized as class B pellets.

3.3. Correlation Between Proximate Analysis, Elemental Composition, and Calorific Values of Biomass

To better understand the relationship between the proximate analysis results, elemental composition, and calorific values obtained for the biomass studied, a Principal Component Analysis (PCA) was performed.
The PCA yielded three principal components (PC) that accounted for 86.17% of the total variation in the original dataset (Figure S1, Supplementary Material). PC1, which accounted for 44.4% of total original variance, correlated positively with the Moisture content and negatively with the fixed carbon, carbon content, HHV, and LHV values. PC2, which explained 25.6% of the total original variation, correlated positively with volatile matter and hydrogen content and negatively with ash and nitrogen content. None of the variables studied were significant in PC3, which explained 16.2% of total original variability.
These results show that there was a positive relationship between heat values and the carbon and fixed carbon content of the biomass samples studied, and a negative relationship between heat values and the moisture content of the samples (Figure 1A). Because carbon and hydrogen content have the impact of boosting calorific value in the combustion process [40], a positive correlation should be expected with the hydrogen content. This was not observed, however, due to the presence of the biochar sample with a high LHV and a very low hydrogen content (1.45%). In fact, if a PCA analysis was performed ignoring this sample, it could a positive correlation could be found between the calorific value and hydrogen as would be expected.
The distribution of the sample scores along PC1 and PC2 is presented in Figure 1B. Most samples were clustered near the origin, but there were samples with high negative values of PC1, such as biochar (sample 11) and cork powder (sample 9). Cluster analysis was performed on the PC1 and PC2 scores of the biomass samples using Euclidean distance and Ward’s hierarchical agglomerative method. This method evaluates cluster distances, using an analysis of variance methodology seeking to minimize the sum of squares of any two hypothetical clusters that may form at each clustering phase.
Figure 1C shows a formation of 20 clusters (considering the cut-off on the 2.6-region obtained from the plot of Linkage Distances (Figure S2, Supplementary Material). Apart from sample 36, forming an isolated cluster and sample 39, the first five clusters were entirely made up of samples with two mixed biomasses of swine waste and grape stalks. The sixth cluster was composed of samples 49–52, which were mixtures of four biomasses. Two of the samples from these four biomass mixtures, samples 47 and 48, were left out of this cluster, most likely due to their lower ash content, and they appeared in the eighth cluster, showing some similarities to samples 38, 42, and 44. Samples 20 and 37 formed the seventh cluster. The three following clusters were composed of isolated samples. Sample 10 and sample 8 were samples outlined in the projection factor plan. Because it had a significantly lower ash content, sample 5 was separated from all the other stalks in the 12th cluster. The 13th and 14th clusters were composed of samples 43, 45, and 46. Sample 45 was separated from the other two due to its higher LHV caused by the presence of cork powder. All these samples were made up of four biomass mixtures and contain biochar. The remaining clusters consisted of essentially isolated samples.
A multiple linear regression (MLR) analysis was performed using elemental composition data to understand its relative contribution to the LHV value. The MLR models were generated using the “best subset” method, optimized with Adjusted R2. The Student t-test (p < 0.05) was used to evaluate the parameter estimates for all models. The resultant model was statistically significant (R = 0.84; F = 29.33; p < 0.001), accounting for 71% of the LHV variance as showed in (Table S1, Supplementary material).
The final regression model is expressed as follows:
L H V = − 1.446 E − 15 + 0.383 · N + 1.223 · C − 0.314 · H + 0.501 · O
where N, C, H, and O represent the nitrogen, carbon, hydrogen, and oxygen content of the samples, respectively. The intercept and coefficients quantify the relative contribution of each variable to the LHV.
Carbon content exhibited the greatest influence on the LHV, as indicated by its highest beta value and its zero-order regression coefficient (r = 0.712), accounting for 50.66% of its variability. This was followed by oxygen (r = − 0.433; 18.79%), hydrogen (r = − 0.115; 1.33%), and nitrogen (r = 0.053; 0.29%). The squared structural correlation coefficients further emphasize carbon’s predominant role ( r X , y = 0.845 ; 71%), with smaller contributions from oxygen ( r X , y = − 0.515 ; 26%), hydrogen ( r X , y = − 0.137 r; 2%), and nitrogen ( r X , y = 0.063 ; 0.4%). These results align with Figure 2 and Table S2 (Supplementary Material). The product β × r makes it possible to calculate the partition of the regression effect into non-overlapping parts based on the interaction of the β coefficients and the zero-order correlation coefficients with the dependent variable, [41], showing that, in this respect, the carbon content (59%) and oxygen content (14%) account for the large part of the variation in the regression equation, followed by hydrogen content (4%) and, finally, the nitrogen content (2%). These results clearly show that for these biomasses, the variation of the LHV is mostly explained by the variation of the carbon and oxygen content and, to a lesser extent, by the hydrogen and nitrogen content.

3.4. Analysis of the Endogenous Factors of the Livestock and Winery Industry in Portugal

The average annual production of pigs in Portugal exceeds 2 million head (Table 11). Considering the average production of manure per animal per day, approximately 2.25 kg, with about half of this amount consisting of water (as detailed in Subsection 2.4 along with references), more than 1600 tons of air-dried swine waste per day are produced in Portugal.
Portugal produces over 826,000 tons of wine grapes which result in over 82,200 tons of air-dried by-products (Table 12).

3.5. Energetic Valorisation

Three scenarios were considered based on key criteria for biomass utilization, including the use of all available biomass from wine and pig production, optimizing the value of the final product (biofuel), prioritizing mixtures with the highest LHV to maximize financial gains, minimizing ash content for cleaner combustion, and enabling the dilution of dangerous contaminating constituents (Table 13).
The first scenario involves the utilization of all wine by-products in conjunction with swine waste. Leftover seeds and skins are further combined with swine waste and cork powder, while the remaining swine waste is mixed with cork powder and biochar. This scenario aims to maximize energy production by leveraging all available resources, making it the most resource-intensive approach. In contrast, the second scenario focuses on minimizing ash content, which is achieved by blending wine by-products with swine waste, including stalks, seeds, and skins, alongside sawdust. After utilizing the stalks, the remaining seeds and skins are combined with swine waste and cork powder. Additionally, mixtures of swine waste, cork powder, and sawdust are incorporated. This approach prioritizes cleaner combustion while resulting in slightly lower energy potential compared to the first scenario. The third scenario strikes a balance between multiple criteria by replacing sawdust in the initial mixture with cork powder. The resulting blend of swine waste, stalks, seeds and skins, and cork powder achieves a higher LHV and reduced nitrogen content while maintaining compliance with ISO standards. Although this scenario slightly increases ash and sulfur contents compared to the second scenario, these values remain within acceptable limits, enhancing financial benefits while ensuring the production of standardized pellets.
Using the mixtures in Scenario 1, there are 529,695 tons of oil equivalent (toe) of energy available to be valued in Portugal. Using these biomasses for heat generation and assuming a combustion efficiency of 90%, 5,588,677 MWh of heat might be produced. A Rankine cycle with 30% efficiency may produce 1,676,603 MWh of electricity in power production. With an 85% utilization factor, a cogeneration system for heat and electricity would result in a total of 4,037,819 MWh of combined energy (Table S3, Supplementary Material). With the 2020 natural gas price of EUR 0.0527/kWh in Portugal, potential savings could reach EUR 294,523,282 (as detailed in Subsection 2.4 of the Materials and Methods section, along with the reference). Furthermore, based on the reference rates for wholesale electricity trading in Portugal for the same year (EUR 0.10/kWh as detailed in Subsection 2.4 of the Materials and Methods section, along with the reference), electricity generation could be valued at EUR 167,660,312, and EUR 268,966,261 in a cogeneration configuration (Table S4, Supplementary Material).
The selection of these combinations increases profits by selecting mixtures with higher LHV, as well as being the optimum method for utilizing wine biomass. However, this scenario implies the production of non-standardized pellets for having ash contents higher than 10%, which makes this scenario impracticable.
In Scenario 2, Portugal has the potential to obtain approximately 485,900 toe of energy. Due to the utilization of mixtures with lower LHV, the potential energy available for conversion in this scenario is 8.5% less than in the first scenario. Nevertheless, it is crucial to highlight that the fifth criterion is satisfied in this case.
With a substantial heat output of 5,115,775 MWh, these mixtures demonstrate their efficiency in converting biomass into thermal energy. Additionally, they contribute significantly to electricity generation, producing 1,534,732 MWh. The most striking aspect is their role in cogeneration, where these mixtures combine to yield a total of 3,696,147 MWh of versatile energy (Table S5, Supplementary Material). Portugal could be saving EUR 269,601,334 in natural gas if these mixtures were valued in heat production. In renewable electricity production, it might be worth EUR 153,473,245, and in cogeneration, it could be worth EUR 246,206,895 (Table S6, Supplementary Material).
Scenario 3 makes 485,463 toe of energy available for conversion in Portugal, which is more 589 toe then the second scenario. The substantial heat generation of 5,121,994 MWh demonstrates the efficiency of these biomass combinations, particularly in meeting heating demands.
Moreover, the electricity production output of 1,536,598 MWh is significant. This clean energy source contributes to reducing the carbon footprint and dependence on fossil fuels in Portugal’s energy mix. It aligns with the country’s goals to increase the share of renewable energy sources in its energy production. The cogeneration results, totaling 3,700,641 MWh, highlight the versatility and multifaceted benefits of these biomass mixtures (Table S7, Supplementary Material). Employing these mixtures for heat generation could result in substantial savings, potentially reaching EUR 269,929,092. When harnessed for electricity production, they could hold an economic value of approximately EUR 153,659,825, and in a cogeneration setup, their worth might extend to an impressive EUR 246,506,212 (Table S8, Supplementary Material).
These figures represent gross potentials, meaning that expenses related to the collection, transportation, and processing of these biomasses, as well as equipment costs, need to be considered. However, the anticipated energy valorization values suggest a promising future for utilizing this biomass. Incorporating this portion of energy into Portugal’s total primary energy consumption would result in an 8.07% increase in the share of renewable energy sources (Figure 3), a considerable amount that would undoubtedly help Portugal reduce its energy dependence. Furthermore, based on the average per capita electricity consumption in Portugal of 2348.4 kWh/year [29], this amount of electricity would be sufficient to meet the energy needs of 654,482 people, which accounts for approximately 6% of the total Portuguese population. Notably, this production would be enough to supply the entire population of the Algarve region, estimated at 438,864 inhabitants in 2018 [29].
To assess the practicality of this research, an analysis was conducted on a farm located in Portugal’s Douro region. This farm operates with a livestock production of 2000 pigs and an annual wine grape production of 500 tons. For privacy considerations, this establishment is referred to here as the “Case Study Farm”.
By implementing the third scenario while considering the availability of dry biomass resources, including 473,146.76 kg of swine slurry, 10,032.34 kg of stalks, and 32,322.87 kg of seeds and skins, along with the acquisition of 231.6 tons of cork powder and 199.2 tons of sawdust, resulted in a total biomass resource of 946.29 tons available for pellet production. This amounted to 365.31 toe of available energy at the Case Study Farm.
Using these biomasses for heat production might result in approximately 3854 MWh of heat output. When employed for electricity generation, it has the potential to produce 1156 MWh of clean energy. Considering a selling price of EUR 0.20/kg, pellet sales could generate a yearly financial benefit of EUR 189,258 or a potential gain of EUR 115,627 per year from electricity production.
To assess investment uncertainty, a financial analysis was conducted for pellet production. Some economic indicators were calculated based on an annual inflation rate of 3% and a project lifespan of 20 years. The capital expenditure (CAPEX) for the pellet production plant was estimated at EUR 283,937.80, based on market quotations for equipment and infrastructure, including preprocessing, drying, pelletizing, and storage facilities. The operational expenditure (OPEX) of EUR 137,214.99 per year included costs for energy, maintenance, licensing, consumables, labor, and the acquisition of cork powder and sawdust. These estimates were scaled to the plant’s production capacity of 946 tons per year and adjusted to reflect current market conditions. At the end of 20 years, the net present value (NPV) indicates that not only was the initial investment of EUR 283,937.80 recovered, but also an additional surplus of EUR 689,666.32 was generated. The capital investment at the Case Study Farm was valued at an annual rate of 17.78%, which was represented by the internal rate of return (IRR). Additionally, it was determined that the initial investment of EUR 283,937.80 had a payback period of 6.36 years.

4. Conclusions

This study explores the potential for utilizing biomass from swine waste and by-products of the wine industry to produce energy. The research involved a comprehensive analysis of the physical and chemical properties of these biomass sources, aiming to find suitable mixtures that balance energy content, ash content, and environmental considerations. The study then evaluated the energetic valorization of these biomass mixtures, considering the energy savings and economic benefits they could offer in Portugal. Three scenarios were proposed, and the third scenario, which included mixtures with cork powder, was found to be the most balanced, yielding a substantial amount of energy while maintaining the potential for standardized pellet production. The study also presented a case study of a farm in the Douro region, showing the practical feasibility and economic viability of implementing these findings.
Swine waste has a high potential for energy production, but its high ash and nitrogen concentration makes it difficult to be used individually. Wine-making biomass has nitrogen levels that are significantly lower than standard requirements, and while its ash content is occasionally low enough to produce a standardized pellet, it nevertheless has high values when compared to pellets made from woody biomass. This study demonstrated that it is possible to generate a pellet with an ash content low enough to meet ISO requirements for class B pellets. A total of 946 tons of biomass is available for pellet production in an agricultural production of 2000 pigs and 500 tons of grapes per year. The valorization of this biomass can result in a yearly financial benefit of EUR 189,259 from the sale of class B pellets, which is a high enough income to pay off the initial investment in less than 7 years.
Furthermore, Portugal would have 485,463 toe of energy available for conversion into heat or electricity if only class B pellets with the best levels of LHV, polluting elements, and ashes were used. In this scenario, 1,536,598 MWh of electricity may be produced annually. Enough to supply the energy needs of 6% of the Portuguese population.
These findings align with values reported in the literature for similar biomass sources. For instance, swine waste analyzed in other studies yielded values of C (35.59%), N (1.72%), H (4.84%), and S (0.44%) [36], closely matching the results of this study. Variations can be attributed to differences in animal species, diet, bedding, storage, and handling practices [37]. Similarly, results for grape stalks and pomace substrates from this study were consistent with previous elemental analyses [35], with discrepancies in proximate analyses linked to differences in moisture content (e.g., 20.2% reported in the literature).
In summary, this research highlights the potential for transforming waste from agriculture and livestock into a valuable energy source, contributing to environmental sustainability and reducing energy dependence on foreign sources.

Future Work

While this study focused on modeling LHV to optimize pellet formulations, similar approaches could be extended to include ash content. Modeling both LHV and ash content would provide a more comprehensive framework for optimizing biomass mixtures to meet energy and environmental criteria.
Additionally, alternative pre-treatment methods such as leaching should be explored to reduce ash content in raw materials. Leaching has shown promise in similar studies for reducing inorganic components, making it possible to achieve stricter pellet standards and expand the applications of Class B pellets. Further, the scalability of the pellet production process and its integration into existing agricultural operations should be evaluated to assess its broader feasibility. Finally, exploring the lifecycle emissions and environmental impacts of pellet production would complement this study’s findings and strengthen its contribution to sustainable energy strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cleantechnol7010001/s1, Figure S1: Eigenvalues of correlation matrix; Figure S2: Plot of Linkage Distances across Steps; Table S1: Test of SS Whole Model vs. SS Residual; Table S2: Collinearity statistics for terms in the equation; Table S3: Energy conversion on scenario one (MWh); Table S4: Energy valorization on scenario one (€); Table S5: Energy conversion under scenario two (MWh); Table S6: Energy valorization under scenario two (€); Table S7: Energy conversion under scenario three (MWh); Table S8: Energy valorization under scenario three (€).

Author Contributions

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

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

adAir-dried basis
dDry basis
ERSEEntidade Reguladora dos Serviços Energéticos
EUEuropean Union
GHGGreenhouse Gas
HHVHigh Heating ValueMJ/kg
LHVLow Heating ValueMJ/kg
MLRMultiple Linear Regression
NDNot Detected
PCAPrincipal Component Analysis
PCPrincipal Components
TFTouriga Franca stalks
UTADUniversity of Trás-os-Montes e Alto Douro

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Cleantechnol 07 00001 g001
Figure 1. (A) Projection of the variables on the factor-plane (1 × 2); (B) projection of the cases on the factor-plane (1 × 2); (C) tree diagram for 53 cases.
Figure 1. (A) Projection of the variables on the factor-plane (1 × 2); (B) projection of the cases on the factor-plane (1 × 2); (C) tree diagram for 53 cases.
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Figure 2. Pareto chart of t-values for coefficients.
Figure 2. Pareto chart of t-values for coefficients.
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Figure 3. Representativity of renewable energy sources in total primary energy consumption in Portugal.
Figure 3. Representativity of renewable energy sources in total primary energy consumption in Portugal.
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Table 1. Identification and composition of the 53 biomass samples.
Table 1. Identification and composition of the 53 biomass samples.
Sample NumberDescriptionComposition (%)
1Swine waste-
2Touriga Franca-
3Malvasia Fina-
4Tinta Barroca-
5Tinta Roriz-
6Viosinho-
7Manteúdo Branco-
8Seeds and skins-
9Cork powder-
10Sawdust-
11Biochar-
12Swine waste + Touriga Franca80% + 20%
1370% + 30%
1460% + 40%
1550% + 50%
16Swine waste + Malvasia Fina80% + 20%
1770% + 30%
1860% + 40%
1950% + 50%
20Swine waste + Tinta Barroca80% + 20%
2170% + 30%
2260% + 40%
2350% + 50%
24Swine waste + Tinta Roriz80% + 20%
2570% + 30%
2660% + 40%
2750% + 50%
28Swine waste + Viosinho80% + 20%
2970% + 30%
3060% + 40%
3150% + 50%
32Swine waste + Manteúdo Branco80% + 20%
3370% + 30%
3460% + 40%
3550% + 50%
36Swine waste + Cork powder80% + 20%
37Swine waste + Touriga Franca + Seeds and skins50% + 25% + 25%
38Swine waste + Touriga Franca + Cork powder50% + 25% + 25%
39Swine waste + Touriga Franca + Sawdust50% + 25% + 25%
40Swine waste + Touriga Franca + Biochar50% + 25% + 25%
41Swine waste + Seeds and skins + Cork powder50% + 25% + 25%
42Swine waste + Seeds and skins + Sawdust50% + 25% + 25%
43Swine waste + Seeds and skins + Biochar50% + 25% + 25%
44Swine waste + Cork powder + Sawdust50% + 25% + 25%
45Swine waste + Cork powder + Biochar50% + 25% + 25%
46Swine waste + Sawdust + Biochar50% + 25% + 25%
47Swine waste + Touriga Franca + Seeds and skins + Cork powder50% + 30% + 10% + 10%
48Swine waste + Touriga Franca + Seeds and skins + Sawdust50% + 30% + 10% + 10%
49Swine waste + Touriga Franca + Seeds and skins + Biochar50% + 30% + 10% + 10%
50Swine waste + Touriga Franca + Cork powder + Sawdust50% + 30% + 10% + 10%
51Swine waste + Touriga Franca + Cork powder + Biochar50% + 30% + 10% + 10%
52Swine waste + Touriga Franca + Sawdust + Biochar50% + 30% + 10% + 10%
53Swine waste + Touriga Franca + Seeds and skins + Cork powder + Sawdust + Biochar50% + 10% + 10% + 10% + 10% + 10%
Table 2. Quality thresholds for selected parameters of non-woody biomass pellets classified under ISO 17225-6:2014 [28].
Table 2. Quality thresholds for selected parameters of non-woody biomass pellets classified under ISO 17225-6:2014 [28].
Class AClass B
Ash Content≤6%≤10%
Nitrogen Content≤1.5%≤2.0%
Sulfur Content≤0.2%≤0.3%
Moisture Content≤12%≤15%
Low Heating Value≥14.5 MJ/kg
Table 3. Proximate analysis of biomasses.
Table 3. Proximate analysis of biomasses.
No.Moisture ad (%)Volatile Matter d (%)Ash d (%)Fixed Carbon d (%)
18.90 ± 0.14157.10 ± 0.01213.40 ± 0.01220.60 ± 0.178
210.50 ± 0.15859.30 ± 0.0128.20 ± 0.01722.00 ± 0.166
310.90 ± 0.19259.30 ± 0.01810.20 ± 0.02319.60 ± 0.102
410.90 ± 0.15660.00 ± 0.0157.20 ± 0.01121.90 ± 0.158
58.80 ± 0.12763.00 ± 0.0114.70 ± 0.03023.50 ± 0.193
69.70 ± 0.13760.30 ± 0.0149.70 ± 0.01420.30 ± 0.118
79.50 ± 0.14360.30 ± 0.0158.40 ± 0.02121.80 ± 0.138
81.60 ± 0.18960.00 ± 0.0180.40 ± 0.02338.00 ± 0.140
97.80 ± 0.10167.20 ± 0.0122.70 ± 0.02522.30 ± 0.126
104.50 ± 0.10576.10 ± 0.0150.50 ± 0.02818.90 ± 0.142
113.90 ± 0.12834.80 ± 0.01110.90 ± 0.02550.40 ± 0.197
ad—air-dried basis, d—dry basis.
Table 4. Elemental analysis and heating values of biomasses.
Table 4. Elemental analysis and heating values of biomasses.
No.N d (%)C d (%)H d (%)S d (%)O d (%)HHV ad (MJ/kg)LHV ad (MJ/kg)
15.14 ± 0.11836.48 ± 0.3072.45 ± 0.2550.13 ± 0.06942.40 ± 0.38015.89 ± 0.28915.88 ± 0.497
25.37 ± 0.05343.20 ± 0.2840.94 ± 0.339ND42.29 ± 0.57816.86 ± 0.15516.85 ± 0.349
35.04 ± 0.09840.23 ± 0.2840.90 ± 0.311ND43.63 ± 0.40716.08 ± 0.29116.06 ± 0.475
45.09 ± 0.08940.31 ± 0.3690.68 ± 0.311ND46.72 ± 0.38616.12 ± 0.34816.11 ± 0.253
54.83 ± 0.05938.95 ± 0.3320.69 ± 0.297ND50.84 ± 0.46716.70 ± 0.35016.69 ± 0.490
65.07 ± 0.05141.34 ± 0.4360.86 ± 0.335ND43.02 ± 0.47516.26 ± 0.29716.25 ± 0.497
75.14 ± 0.05141.84 ± 0.2790.80 ± 0.308ND43.82 ± 0.32716.35 ± 0.17416.34 ± 0.366
87.39 ± 0.13261.42 ± 0.4470.61 ± 0.329ND30.18 ± 0.22526.57 ± 0.24026.56 ± 0.305
95.98 ± 0.17148.93 ± 0.3091.42 ± 0.3550.02 ± 0.04240.95 ± 0.45921.85 ± 0.19321.83 ± 0.295
105.71 ± 0.14743.34 ± 0.2561.02 ± 0.352ND49.43 ± 0.41718.10 ± 0.21018.08 ± 0.469
111.88 ± 0.14648.72 ± 0.2932.06 ± 0.317ND36.43 ± 0.53133.73 ± 0.18233.73 ± 0.459
ad—air-dried basis, d—dry basis. ND—not detected.
Table 5. Proximate analysis of mixtures of swine waste and wine by-products.
Table 5. Proximate analysis of mixtures of swine waste and wine by-products.
No.Moisture ad (%)Volatile Matter d (%)Ashd (%)Fixed Carbon d (%)
129.10 ± 0.17960.20 ± 0.01413.50 ± 0.02917.20 ± 0.138
139.00 ± 0.10662.10 ± 0.01912.30 ± 0.02516.60 ± 0.157
148.60 ± 0.17660.00 ± 0.01412.70 ± 0.01818.70 ± 0.162
159.00 ± 0.14160.10 ± 0.01411.40 ± 0.02919.50 ± 0.142
169.80 ± 0.16061.20 ± 0.01713.70 ± 0.02515.30 ± 0.197
179.40 ± 0.18361.70 ± 0.01813.30 ± 0.01815.60 ± 0.162
189.30 ± 0.11760.20 ± 0.01513.70 ± 0.02016.80 ± 0.130
199.40 ± 0.19360.70 ± 0.01512.70 ± 0.02217.20 ± 0.107
208.90 ± 0.19961.20 ± 0.01812.90 ± 0.02917.00 ± 0.192
218.70 ± 0.12562.00 ± 0.01712.40 ± 0.01616.90 ± 0.197
228.60 ± 0.13662.30 ± 0.01611.10 ± 0.01818.00 ± 0.146
238.40 ± 0.19762.10 ± 0.01711.20 ± 0.01418.30 ± 0.123
249.30 ± 0.18161.30 ± 0.01613.20 ± 0.02916.20 ± 0.111
258.90 ± 0.13062.40 ± 0.01611.70 ± 0.02817.00 ± 0.160
268.90 ± 0.17361.70 ± 0.01410.60 ± 0.02118.80 ± 0.157
278.70 ± 0.12262.00 ± 0.01910.30 ± 0.02419.00 ± 0.102
289.30 ± 0.12559.80 ± 0.01314.50 ± 0.02616.40 ± 0.169
299.10 ± 0.17961.20 ± 0.02013.00 ± 0.01516.70 ± 0.147
309.20 ± 0.12760.10 ± 0.01512.00 ± 0.02918.70 ± 0.103
319.70 ± 0.12560.40 ± 0.01711.50 ± 0.01618.40 ± 0.145
328.70 ± 0.10460.30 ± 0.01115.30 ± 0.02115.70 ± 0.199
338.10 ± 0.18061.30 ± 0.01813.10 ± 0.02917.50 ± 0.140
348.20 ± 0.17561.60 ± 0.01913.00 ± 0.01717.20 ± 0.123
358.20 ± 0.14460.50 ± 0.01012.20 ± 0.02419.10 ± 0.145
368.50 ± 0.12264.60 ± 0.01712.20 ± 0.02214.70 ± 0.123
ad—air-dried basis, d—dry basis.
Table 6. Elemental analysis and heating values of mixtures of swine waste and wine by-products.
Table 6. Elemental analysis and heating values of mixtures of swine waste and wine by-products.
No.N d (%)C d (%)H d (%)S d (%)O d (%)HHV ad (MJ/kg)LHV ad (MJ/kg)
125.29 ± 0.09539.88 ± 0.4821.53 ± 0.2960.08 ± 0.04439.72 ± 0.55915.90 ± 0.19615.88 ± 0.264
135.25 ± 0.08440.56 ± 0.3341.40 ± 0.3390.04 ± 0.06940.46 ± 0.57916.02 ± 0.27016.01 ± 0.266
145.07 ± 0.06138.46 ± 0.4111.77 ± 0.3290.05 ± 0.07241.95 ± 0.61616.02 ± 0.18116.01 ± 0.241
155.20 ± 0.05038.60 ± 0.3591.75 ± 0.3900.06 ± 0.03142.99 ± 0.20716.07 ± 0.18316.06 ± 0.208
165.08 ± 0.12038.11 ± 0.4481.76 ± 0.2510.05 ± 0.05241.29 ± 0.60615.78 ± 0.30515.77 ± 0.238
175.47 ± 0.11039.02 ± 0.3611.73 ± 0.3780.07 ± 0.02640.42 ± 0.27915.82 ± 0.28815.80 ± 0.227
185.33 ± 0.11039.03 ± 0.2741.60 ± 0.3630.06 ± 0.02040.28 ± 0.43515.62 ± 0.20115.61 ± 0.474
195.06 ± 0.06339.66 ± 0.2601.41 ± 0.3080.04 ± 0.06241.13 ± 0.23015.62 ± 0.31515.61 ± 0.236
205.19 ± 0.09938.20 ± 0.3571.59 ± 0.3200.02 ± 0.05842.10 ± 0.63815.98 ± 0.26115.97 ± 0.243
214.94 ± 0.08937.44 ± 0.3841.32 ± 0.3820.06 ± 0.06543.84 ± 0.47016.09 ± 0.34816.08 ± 0.271
225.12 ± 0.15440.09 ± 0.4621.06 ± 0.3750.03 ± 0.09842.61 ± 0.36515.98 ± 0.31615.97 ± 0.404
234.93 ± 0.10138.76 ± 0.4671.17 ± 0.292ND43.94 ± 0.47215.73 ± 0.15615.72 ± 0.338
245.14 ± 0.08539.40 ± 0.4061.63 ± 0.3150.07 ± 0.04640.56 ± 0.54115.91 ± 0.28015.90 ± 0.271
255.22 ± 0.05539.88 ± 0.2641.42 ± 0.3440.05 ± 0.09741.72 ± 0.53416.20 ± 0.25016.19 ± 0.273
265.25 ± 0.16340.39 ± 0.4171.38 ± 0.3240.04 ± 0.03642.34 ± 0.38916.16 ± 0.33516.15 ± 0.365
275.01 ± 0.09439.22 ± 0.4321.33 ± 0.2660.04 ± 0.01244.10 ± 0.31616.04 ± 0.31716.03 ± 0.468
285.30 ± 0.08039.21 ± 0.3221.72 ± 0.3670.05 ± 0.02439.21 ± 0.42615.62 ± 0.18915.61 ± 0.300
295.20 ± 0.10738.62 ± 0.2951.62 ± 0.3930.05 ± 0.01641.50 ± 0.22515.58 ± 0.24615.56 ± 0.361
305.06 ± 0.07939.01 ± 0.3641.50 ± 0.3020.04 ± 0.03842.38 ± 0.53815.70 ± 0.21615.69 ± 0.249
315.00 ± 0.13538.86 ± 0.4691.48 ± 0.2690.03 ± 0.04643.13 ± 0.54615.85 ± 0.23115.84 ± 0.202
325.71 ± 0.07641.92 ± 0.4651.96 ± 0.2700.06 ± 0.08335.04 ± 0.65415.81 ± 0.20815.80 ± 0.430
334.87 ± 0.08037.93 ± 0.4951.52 ± 0.2520.04 ± 0.03642.55 ± 0.47515.79 ± 0.24015.78 ± 0.356
344.90 ± 0.13237.23 ± 0.4651.48 ± 0.3150.02 ± 0.03743.37 ± 0.49415.76 ± 0.19415.75 ± 0.214
354.98 ± 0.10939.59 ± 0.4251.25 ± 0.2800.02 ± 0.07441.96 ± 0.70016.01 ± 0.21916.00 ± 0.446
365.59 ± 0.08241.47 ± 0.3871.20 ± 0.2940.06 ± 0.07039.47 ± 0.37817.61 ± 0.22717.60 ± 0.306
ad—air-dried basis, d—dry basis, ND—not detected.
Table 7. Proximate analysis of samples made of three materials.
Table 7. Proximate analysis of samples made of three materials.
No.Moisture ad (%)Volatile Matter d (%)Ash d (%)Fixed Carbon d (%)
377.90 ± 0.15564.40 ± 0.0139.30 ± 0.01718.40 ± 0.186
388.30 ± 0.19562.00 ± 0.01111.60 ± 0.01218.10 ± 0.172
398.50 ± 0.15664.80 ± 0.0198.80 ± 0.01417.90 ± 0.122
408.20 ± 0.12653.10 ± 0.01812.10 ± 0.01826.60 ± 0.137
416.40 ± 0.18867.60 ± 0.0148.00 ± 0.01718.00 ± 0.151
426.80 ± 0.13768.50 ± 0.0197.70 ± 0.02717.00 ± 0.141
437.00 ± 0.19658.80 ± 0.01112.20 ± 0.02622.00 ± 0.135
447.00 ± 0.11667.30 ± 0.0138.70 ± 0.02917.00 ± 0.161
456.80 ± 0.10551.50 ± 0.01211.60 ± 0.01130.10 ± 0.177
467.90 ± 0.17553.30 ± 0.01610.40 ± 0.01228.40 ± 0.131
ad—air-dried basis, d—dry basis.
Table 8. Elemental analysis and heating values of samples made of three materials.
Table 8. Elemental analysis and heating values of samples made of three materials.
No.N d (%)C d (%)H d (%)S d (%)O d (%)HHV ad (MJ/kg)LHV ad (MJ/kg)
375.33 ± 0.17141.85 ± 0.4941.39 ± 0.2700.03 ± 0.03842.10 ± 0.39617.98 ± 0.21117.97 ± 0.276
385.29 ± 0.13739.66 ± 0.3931.57 ± 0.3440.03 ± 0.02841.86 ± 0.49417.56 ± 0.27017.55 ± 0.361
395.15 ± 0.07038.90 ± 0.3041.33 ± 0.3990.03 ± 0.08945.79 ± 0.36816.47 ± 0.27716.46 ± 0.397
404.78 ± 0.09843.80 ± 0.3121.64 ± 0.3860.03 ± 0.08537.64 ± 0.67317.81 ± 0.26517.80 ± 0.246
416.54 ± 0.13448.85 ± 0.4471.95 ± 0.3120.05 ± 0.04134.62 ± 0.58219.61 ± 0.26719.60 ± 0.267
425.70 ± 0.13344.03 ± 0.2991.20 ± 0.3460.02 ± 0.06441.35 ± 0.53418.28 ± 0.29118.27 ± 0.235
434.80 ± 0.08150.52 ± 0.3261.36 ± 0.3330.02 ± 0.07331.09 ± 0.21420.04 ± 0.30420.02 ± 0.206
445.40 ± 0.07341.78 ± 0.3761.56 ± 0.3520.03 ± 0.04442.54 ± 0.24217.77 ± 0.27817.76 ± 0.255
453.26 ± 0.09549.85 ± 0.3221.46 ± 0.2700.02 ± 0.07733.82 ± 0.47719.09 ± 0.30219.08 ± 0.466
463.97 ± 0.07050.78 ± 0.4791.41 ± 0.3710.01 ± 0.04833.43 ± 0.35317.68 ± 0.32817.67 ± 0.479
ad—air-dried basis, d—dry basis.
Table 9. Proximate analysis of samples made of 4 materials, along with one sample containing six materials (sample 53).
Table 9. Proximate analysis of samples made of 4 materials, along with one sample containing six materials (sample 53).
No.Moisture ad (%)Volatile Matter d (%)Ash d (%)Fixed Carbon d (%)
477.60 ± 0.12565.60 ± 0.0199.70 ± 0.01317.10 ± 0.196
487.80 ± 0.11663.30 ± 0.0189.40 ± 0.02519.50 ± 0.119
497.90 ± 0.15159.00 ± 0.01810.60 ± 0.01322.50 ± 0.132
508.20 ± 0.10765.80 ± 0.0119.40 ± 0.02116.60 ± 0.136
518.10 ± 0.15259.30 ± 0.02011.10 ± 0.01221.50 ± 0.193
528.30 ± 0.10459.10 ± 0.01211.00 ± 0.01521.60 ± 0.149
537.40 ± 0.12861.70 ± 0.01510.20 ± 0.01620.70 ± 0.195
ad—air-dried basis, d—dry basis.
Table 10. Elemental analysis and heating values of samples made of four materials, along with one sample containing six materials (sample 53).
Table 10. Elemental analysis and heating values of samples made of four materials, along with one sample containing six materials (sample 53).
No.N d (%)C d (%)H d (%)S d (%)O d (%)HHV ad (MJ/kg)LHV ad (MJ/kg)
475.51 ± 0.14142.29 ± 0.3201.37 ± 0.2660.02 ± 0.05941.12 ± 0.20817.39 ± 0.17317.38 ± 0.299
485.12 ± 0.11439.29 ± 0.4221.40 ± 0.3530.01 ± 0.06244.77 ± 0.66417.05 ± 0.32617.04 ± 0.282
494.86 ± 0.09842.99 ± 0.2531.31 ± 0.2690.02 ± 0.03140.22 ± 0.32317.48 ± 0.33017.47 ± 0.339
505.07 ± 0.07639.82 ± 0.3971.53 ± 0.2600.01 ± 0.07644.17 ± 0.34716.92 ± 0.16316.91 ± 0.427
514.49 ± 0.11641.30 ± 0.4631.49 ± 0.3250.01 ± 0.02641.61 ± 0.50117.10 ± 0.32617.09 ± 0.218
524.14 ± 0.05437.78 ± 0.3531.65 ± 0.379ND45.43 ± 0.55516.78 ± 0.22516.77 ± 0.278
535.47 ± 0.17644.59 ± 0.3581.86 ± 0.3110.02 ± 0.08437.86 ± 0.60218.29 ± 0.20418.28 ± 0.300
ad—air-dried basis, d—dry basis, ND—not detected.
Table 11. Pig and swine waste production in Portugal.
Table 11. Pig and swine waste production in Portugal.
Geographic Location (NUTS 2013)Swine Annual ProductionAir-Dried Swine Waste (Ton/Day)
Portugal2,112,800.001611.06
      Mainland2,076,600.001583.46
           North61,200.0046.67
           Center866,600.00660.80
           Metropolitan Area of Lisbon208,200.00158.76
           Alentejo918,800.00700.61
           Algarve21,800.0016.62
      Azores Islands30,200.0023.03
      Madeira Islands6000.004.58
Table 12. Wine grape and by-products production in Portugal.
Table 12. Wine grape and by-products production in Portugal.
Geographic Location (NUTS 2013)Wine Grape
ProductionStalksSkinsSeeds
TonTonTonTon
Portugal826,450.0019,508.7747,769.5515,085.12
      Mainland820,398.6019,365.9247,419.7814,974.67
           North290,086.806847.6416,767.285294.93
           Center241,868.005709.4213,980.194414.80
           Metropolitan Area of Lisbon63,307.601494.413659.241155.55
           Alentejo223,570.205277.4912,922.564080.81
           Algarve1566.0036.9790.5228.58
      Azores Islands1085.4025.6262.7419.81
      Madeira Islands4966.00117.22287.0490.64
Table 13. Available energy under the different scenarios (MWh).
Table 13. Available energy under the different scenarios (MWh).
Geographic Location (NUTS 2013)Scenario 1Scenario 2Scenario 3
Portugal6,209,641.195,684,194.265,691,104.61
      Mainland6,102,533.285,586,926.975,593,786.72
           North161,177.45159,652.33162,077.88
           Center2,553,160.112,334,528.432,336,550.81
           Metropolitan Area of Lisbon612,809.51561,191.61561,720.96
           Alentejo2,710,650.812,473,108.352,474,977.73
           Algarve64,735.4158,446.2558,459.35
      Azores Islands89,801.3680,899.5080,908.57
      Madeira Islands17,306.5516,367.7916,409.31
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Oliveira, M.; Nunes, F.H.F.M.; Borges, A.D.d.S. Harnessing Livestock and Vineyard Residues for Sustainable Energy Production in Portugal. Clean Technol. 2025, 7, 1. https://doi.org/10.3390/cleantechnol7010001

AMA Style

Oliveira M, Nunes FHFM, Borges ADdS. Harnessing Livestock and Vineyard Residues for Sustainable Energy Production in Portugal. Clean Technologies. 2025; 7(1):1. https://doi.org/10.3390/cleantechnol7010001

Chicago/Turabian Style

Oliveira, Miguel, Fernando Hermínio Ferreira Milheiro Nunes, and Amadeu Duarte da Silva Borges. 2025. "Harnessing Livestock and Vineyard Residues for Sustainable Energy Production in Portugal" Clean Technologies 7, no. 1: 1. https://doi.org/10.3390/cleantechnol7010001

APA Style

Oliveira, M., Nunes, F. H. F. M., & Borges, A. D. d. S. (2025). Harnessing Livestock and Vineyard Residues for Sustainable Energy Production in Portugal. Clean Technologies, 7(1), 1. https://doi.org/10.3390/cleantechnol7010001

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