Energy Production from Cattle Manure within a Life Cycle Assessment Framework: Statistical Optimization of Co-Digestion, Pretreatment, and Thermal Conditions

Abstract

Waste to energy processes from anaerobic digestion (WtE-AD) from cattle manure (CM) have low CH₄ yields due to CM’s structural composition. The search for alternatives to increase the energy yields of these processes must consider the optimization of operating parameters within a framework of mitigating the environmental footprint. The goal of this paper is to provide a statistical optimization strategy based on experimental designs to improve CH₄ yields and reduce the environmental profile of CM valorization through a WtE-AD process. Biochemical methane potential tests were conducted to determine the energetic and environmental effects that alkaline pretreatments, different AD temperatures, and co-digestion formulations with fruit and vegetable waste (FVW) have on the WtE-AD process from CM. The evaluation was conducted following a life cycle assessment approach through energy balances. The results indicate that the highest CH₄ yield (384.3 mL CH₄ g VS⁻¹) and the lowest environmental impact (−0.06 kg CO₂ eq kWh⁻¹ of electricity production) were achieved with the co-digestion of CM with FVW 1:1, pretreatment with 10 g NaOH 100 g⁻¹ of VS of CM, and a temperature of 45 °C. It was found that the CM pretreatment with NaOH substantially increases the energy profile of the WtE-AD process without compromising the environmental impact since greenhouse gas emissions in chemical production are negligible.

1. Introduction

Anaerobic digestion (AD) as a waste-to-energy (WtE-AD) process could contribute to the transition towards clean electric power generation systems and the reduction of greenhouse gas (GHG) emissions [1]. Despite the environmental benefits of these processes, they tend to operate with lower economic profitability, making them less competitive compared to conventional energy generation systems [2]. The energy return rate (energy produced per unit of net energy invested) for electricity generation processes using AD from waste on an industrial scale is 1.46–7.29 [3]. In contrast, conventional electricity generation in countries where fossil sources predominate is higher than 20.0 [4].

Improving the energy profile of AD processes depends strongly on the availability and the structural characteristics of the waste [5]. Wastes with high content of lignocellulosic material have lower CH₄ yields due to low rates of hydrolysis and bioaccessibility, and long digestion times that hinder the energy competitiveness of the process [6]. One example of this type of waste is cow manure (CM) which has 0.26 g g VS⁻¹ [7] of lignocellulosic biomass. In Mexico, 1.5 Mt of CM has generated annually which makes it suitable for energy production because of its availability.

Strategies to improve the performance of lignocellulosic organic matter in AD have been proposed [8], where the most applied strategies are the use of anaerobic co-digestion (ACoD) [9], pretreatments to the lignocellulosic biomass, and modifying the operating temperature; however, their application entails using inputs that call into question the energy and environmental performance of the process [10]. Ramírez-Arpide et al. [11], evaluated the environmental impacts of the generation of electrical energy from the ACoD of CM with nopal cactus waste, finding that co-digestion reduces GHG emissions by 22.5% with respect to mono-digestion of CM, mainly due to the increased CH₄ yields and replacement of conventional electrical energy consumption. Miramontes-Martínez et al. [10] evaluated the ACoD of CM with fruit and vegetable waste (FVW) and found that the mixture of substrates favors synergistic substrate + co-substrate interactions. Adding FVW to the reaction medium helps the effective degradation of CM. However, the authors did not evaluate the environmental and energy performances of the process.

Various studies have focused on increasing the biodegradability of substrates with a high content of lignocellulosic material through pretreatment processes. Passos et al. [12], carried out AD schemes for CM with thermochemical pretreatments using HCl and NaOH at different concentrations. The authors concluded that the best pretreatment scheme is to use 10 g of NaOH for every 100 g VS of CM at a temperature of 100 °C for 5 min because this manages to increase CH₄ yields by more than 23%. Arnell et al. [13] evaluated the application of a chemical pretreatment prior to an AD stage in a wastewater treatment plant (WWTP). The work showed that the use of chemical attack by NaOH improves the environmental profile of the WWTP, reducing GHG emissions from 0.15 to 0.11 kg CO₂ eq m⁻³ of treated water, a benefit associated with a 14% increase in the production of CH₄, which was used as an energy source to replace electricity based on fossil consumption. Although the use of chemical pretreatment agents leads to greater biogas production and mitigation of GHG emissions, it is necessary to optimize their use. Carballa et al. [8] observed that through the thermochemical pretreatment of organic waste using HCl, CH₄ yields in AD processes can be increased by up to 50%; however, the use of HCl and energy for heating increases GHG emissions by 77% compared to the scenario without pretreatment.

Another strategy studied to reduce energy consumption and the environmental profile in WtE-AD is the operation of the digesters at different temperatures. The variation in this parameter affects the viability and activity of the anaerobic microbes [14]. Li et al. [15], studied different AD processes under mesophilic (35 °C) and thermophilic (55 °C) schemes through the life cycle analysis (LCA) methodology, finding that thermophilic conditions result in 44% less GHG emissions compared to mesophilic, due to the increase in the production of CH₄ from the process. On the other hand, Lanko et al. [16], evaluated the environmental profile of different thermal operation scenarios of an anaerobic digester in a WWTP. The operation at 35 °C reduces the climate change indicator (CCI) by 7% in contrast with 55 °C due to the decrease in energy consumption of the anaerobic digester. Based on this, there is no consensus to establish the best operating temperature. The energy and environmental performance of AD depend on the microbiota present in the substrates and inoculum, and in the environmental profile of the energy used to maintain isothermal conditions in the digester. Higher temperatures imply higher GHG emissions due to the energy consumption of the process; however, it is commonly associated with higher CH₄ yields.

Most of the studies identified in the literature evaluate technically and environmentally the application of pretreatment strategies, co-digestion, and different thermal operating conditions individually. However, the analysis of the joint incorporation of these strategies and the optimization of those conditions to achieve energetic and environmental competitiveness of the WtE-AD process has yet to be evaluated. Therefore, this work aims to increase the energetic and environmental performance of a WtE-AD from cattle manure, considering thermochemical pretreatments, co-digestion, and operating temperature, following an optimization scheme based on life cycle analysis and statistical tools. Thus, the main contribution of this research is to achieve clean energy production technologies that are competitive with conventional sources.

2. Materials and Methods

The methodological approach of the work is shown in Figure 1. Each activity is referenced to its respective section, starting with the obtaining, storage, and characterization of the substrates and inoculum (Section 2.1), followed by two experimental designs, where the results of the first led to the design of the second, (Section 2.2). The pretreatments to the CM, tests (Section 2.3 and Section 2.4), an environmental impact assessment, and an energy balance were conducted to assess the viability of the WtE-AD process (Section 2.5).

2.1. Substrate and Inoculum Characterization

The inoculum was obtained from a mesophylic anaerobic digester of a local brewing company and kept in demethanization prior to use, with the objective of degrading all the organic matter susceptible to anaerobic gasification. This was carried out using an Applikon^®® bioreactor with an operating volume of 4 L, isothermal conditions using a heating blanket operated by a temperature control, and a continuous agitation at 200 rpm. The demethanization process stopped when no appreciable biogas production was observed (~25 days). A more detailed diagram of the bioreactor for the demethanization process can be found in Figures S3 and S4 of Supplementary Materials. The CM was obtained from a dairy cow farm of the Faculty of Agronomy of the Autonomous University of Nuevo León (UANL), while the FVW were collected and categorized for 20 days directly of the organic fraction of urban solid waste from the members of the Center for Research in Biotechnology and Nanotechnology of the Faculty of Chemical Sciences of the UANL. Both substrates and the inoculum were stored in 4 L wide-mouth square HDPE bottles (Nalgene brand). The fresh substrates were ground with a blender, TAPISA^®® model T3L, until total homogenization, and kept refrigerated at 4 °C before use. The fresh substrates and inoculum were characterized by physicochemical tests specified in Table S1 of the Supplementary Materials section.

2.2. Design of Experiments and Statistical Analysis

To obtain the best scheme for the application of pretreatment strategies, co-digestion and different operating temperatures, an exploratory design of experiments (Stage 1) was carried out, corresponding to a factorial 2³, that investigates the impact of the following factors: concentration of chemical agent in pretreatment (g NaOH [100 g VS of CM]⁻¹), co-digestion ratio CM:FVW (% CM) and operating temperature (°C). In addition to this, a face-centered cuboidal model was employed, with the aim of adding central points in the interactions. For the analysis, the response surface methodology was used. This is a statistical tool that makes use of mathematical techniques for the development and optimization of processes. The results of Stage 1 make it possible to delimit the values of the operating parameters and serve as the basis for a second experimental design (Stage 2) with the aim of improving CH₄ yields and the environmental profile of these processes. The design methodology was analogous to that shown for Stage 1. The experimental designs for both stages are shown in Table 1.

2.3. Thermo-Alkaline Pretreatment

A preliminary study was carried out to determine the best chemical agent for the pretreatment of CM, where the cumulative yield of CH₄ from CM pretreated with NaOH and HCl was evaluated at the concentrations proposed by Passos et al. [12], where it was found that pretreatment with NaOH promotes higher CH₄ yields. The results for this analysis are found in Figure S1, in the Supplementary Materials section. From the previously mentioned study, a thermal-alkaline pretreatment was carried out using a NaOH solution, between 1 and 20 g NaOH (100 g VS of CM)⁻¹ to the CM and subjecting it to 90 °C for 5 min, to delignify the lignocellulosic fibers of the CM and increase their bioaccessibility. In addition, the pretreatment effluents were characterized by the tests shown in Table S1 of the Supplementary Materials section.

2.4. Biochemical Methane Potential Tests (BMP)

BMP tests were carried out using the experimental conditions listed in Table 1 in triplicate, as recommended by [17]. The tests were carried out with a substrate mixture containing 60 g VS L⁻¹ in 120 mL serological bottles that were hermetically sealed with butyric rubber septa for a duration of 25 days, with an operation volume of 60 mL, and an inoculum: substrate ratio of 1:1 (% vol). The quantification of the accumulated CH₄, per gram of VS in the substrate mixture was carried out using the biogas density technique proposed by Justesen et al. [18]. The BMP effluent was characterized by the physicochemical tests specified in Table S1.

2.5. Life Cycle Assessment (LCA)

2.5.1. Objectives and Limits Definition

The LCA aims to quantify the environmental impact of CM management through a laboratory-scale WtE-AD process to produce energy and biofertilizer. The system boundary is shown in Figure 2. Within the boundaries, the production of all necessary energy and mass supplies at each stage of the life cycle was considered. The activities associated with the manufacture and final disposal of waste, infrastructure, and laboratory material were not considered in the analysis. Transportation of CM and FVW from the source to the laboratory was considered outside the objectives and limits of the system. The functional unit (FU) was defined as the management of 1 kg of CM.

2.5.2. Life Cycle Inventory Analysis

The energy consumption of the pretreatment and by-product generation modules was obtained from the technical specifications of the equipment. The electrical consumption of the digester was calculated using the methodology of Passos et al. [12]. It was considered that the electrical energy produced is supplied to the national electrical distribution network and replaces conventional electrical energy in the same proportion. The distilled water required in the BMP tests and in the pretreatments was estimated using a mass balance (Section 2.3 and Section 2.4). The biofertilizer replaces the commercial urea fertilizer based on its total N content (ammoniacal and organic).

GHG emissions generated by the pretreatment module were quantified using the Ecoinvent database v 3.3. Fugitive C–CO₂ emissions from electricity generation were not considered when evaluating the CCI due to their biogenic origin [19]. The production of electricity and biofertilizer, and the quantification of their GHG emissions, was carried out according to the methodology of Miramontes-Martínez et al. [20] which is described below. The quantification of electricity is carried out by means of a heat and electricity cogeneration equipment with electrical and thermal efficiency of 38% and 48%, respectively, the emissions from electricity generation are obtained from the Ecoinvent 3.3 database. For the biofertilizer production, the extracted digestate receives a drying treatment through a decantation process and a rotary dryer to reduce biological activity and increase the concentration of N–NH₃. Direct N–N₂O emissions are released in the settler [21]. The effluent from the decanter is fed to a rotary dryer, where the temperature and the contact surface are high which causes a considerable amount of N–NH₄⁺ to be emitted as NH₃ [21]. In this work it was assumed that 85% of the available NH₄⁺ is emitted as NH₃. Indirect N₂O emissions from NH₃ volatilization were determined using the Guidelines for National Greenhouse Gas Inventories, Chapter 11 [22]. A leakage percentage of 1% of the total CH₄ production was assumed for all BMP tests, as done by our research group in prior works [20].

2.5.3. Green-House Gas Emission Assessment

The life cycle impact assessment was carried out under an attributional approach, and the analysis model was implemented using SimaProR 7.3.3 software (Amersfoort, The Netherlands) [23]. The data concerning the production of materials and energy considered in the life cycle inventory were taken from the Ecoinvent database v 3.3 [24]. The environmental performance of the WtE-AD was determined using the midpoint CCI using the Intergovernmental Panel on Climate Change 2013 GWP 100y method, as suggested by Eriksson et al. [25].

Next, the equations used to calculate the overall heat transfer coefficient of each process U (Equation (1)), the energy consumption for the pretreatment processes Q_pt (Equation (2)), anaerobic digestion Q_DA (Equation (3)), and biofertilizer production Q_bf (Equation (4)) are presented, while Table 2 shows the values of the parameters used and assumed in the equations. In Equations (1) and (2), the first term of the equation corresponds to the heat transfer through the surface of the digesters with the thermal bath, while the second term corresponds to the latent heat due to the temperature change at constant pressure. In Equation (3), the heat of drying corresponds to the product of the drying efficiency by the mass of water removed. In this work, an eco-technical quotient (Equation (5)) was proposed, where the mass of CO₂ eq emitted or avoided is quantified for each unit of energy produced in the evaluated processes (kg CO₂ eq kWh⁻¹). The results for the GHGE assessment for the proposed strategies were compared to a base-scenario consisting in the energetic valorization of CM in a mesophilic mono-digestion, which has GHGE of 0.038 kg CO₂ eq kWh⁻¹ (Figure S2 of Supplementary Materials). In terms of power generation, the electrical energy generation in our WtE-AD systems was compared with the conventional electric generation in Mexico, which is mainly fossil fuel based (0.61 kg CO₂ eq kWh⁻¹).

(1)$U=\frac{1}{L/\lambda +1/h}$

(2)$Q_{pt}=U_{pt}{A}_{pt}\left(T_{op}-T_{amb}\right)t+m_{pt}C{p}_{pt}\left(T_{op}-T_{amb}\right)$

(3)$Q_{DA}=U_{DA}{A}_{DA}\left(T_{op}-T_{amb}\right)t+m_{DA}C{p}_{DA}\left(T_{op}-T_{amb}\right)$

(4)$Q_{bf}={\eta }_{sec}{M}_{H_{2}O}$

(5)$EcoTec{h}_{ICC}=\frac{M_{C{O}_{2-eq}}}{E_{prod}}$

Figure 2

Boundary diagram of each experimental trial framed in Table 3. The operations and parameters in red indicate the activities that vary according to each trial.

[Figure omitted. See PDF]

3. Results and Discussion

3.1. Stage 1

3.1.1. Substrates, Inoculum, and Pretreatment Effluents Characterization

Table 3 shows the characterization results for CM, the FVW co-substrate and the inoculum used for the WtE-AD process. As the high content of cellulose, hemicellulose, and lignin present in CM can be noted as being 50, 64, and 20% higher compared to FVW, respectively. These results are consistent with the literature since, due to its structural composition, CM has a higher content of complex carbohydrates [29], while FVW have 17 times more concentration of total sugars.

In the same Table 3, the characterization results for the CM alkaline pretreatment effluents are presented. The concentration of NaOH in the pretreatment increases, the content of hemicellulose and lignin shows a trend to decrease, while cellulose seems to not have a notable effect. In addition, there is a notable increase in the concentration of total sugars when using 10% NaOH. This may be due to the alkaline attack that mainly affects the ester bond, which joins the hemicellulose molecule and lignin and carries out a saponification reaction [29]. This reaction results in the disintegration of hemicellulose and the solubilization of lignin, causing the proportion of volatile solids on a dry basis to decrease as the concentration of NaOH increases in the pretreatment. The increase in the content of total phenolic compounds reflects the degree of lignin solubilization; however, the inhibitory concentrations reported in the literature (2 g L⁻¹) are not reached [30].

3.1.2. Biomethane Potential Tests (BMP)

Figure 3 and Figure 4 show the cumulative CH₄ yields and the response surfaces (quadratic model, Table 4) for the BMP processes, respectively. As the concentration of NaOH increases in the pretreatment, the accumulated yield of CH₄ is favored. This phenomenon may be associated with the fact that at higher concentrations of NaOH, a higher concentration of fermentable sugars is released that can be used by anaerobic microorganisms due to the solubilization of the lignocellulosic fibers of the CM (Section 3.1). On the other hand, higher CH₄ yields can be seen when the digesters are operated at a temperature of 45 °C. Table 4 shows the statistical significance of the proposed quadratic model for the data obtained from Stage 1. The influence of temperature is outstanding, since it has a p value of 2 × 10⁻⁴ (p < 0.05), evidencing a significant influence on the CH₄ yield; however, said influence does not follow linear behavior. These results differ from several works reported in the literature which recommend that the operating temperature range for mesophilic conditions be 30 to 37 °C, and for thermophilic conditions be 50 to 57 °C. Although few works have studied AD in the intermediate range between mesophilic and thermophilic regimes (37 to 50 °C), better results have been found in terms of CH₄ yield and other compounds. The reported results are consistent with those obtained in this work.

Komemoto et al. [31] evaluated three temperatures (35 °C, 45 °C, and 55 °C) for biogas production from food waste and found that processes at 45 °C increase cumulative production up to 70% more than the process at 55 °C. The authors conclude that temperature not only affects the reaction rate of the biochemical processes involved in the process, but also modifies the diversity of the microbiota in the reactive medium. On the other hand, Hupfauf et al. [32] evaluated the yield of CH₄ from CM at different temperatures and found a maximum of CH₄ production at a temperature of 45 °C. From statistical analysis, the authors determined that 78% of the variation in CH₄ yield is due to changes in the diversity of the microbiota due to a change in temperature. They found that at a temperature of 35 °C the growth of Methanosarcinaceae and Methanobactericeae archaea, which generate CH₄ through acetoclastic pathways. At 55 °C, the growth of Methanomicrobiaceae archaea is promoted, which produce CH₄ through hydrogenotrophic pathways. The authors indicate that at a temperature of 45 °C there is a balance between the different families of acetoclastic and hydrogenotrophic archaea, promoting a syntrophic synergy between the CH₄—producing archaea.

The processes of co-digestion, pretreatment, and the operating temperature have possible synergistic effects on the rate of CH₄ generation in the process. This is because co-digestion provides the amount of organic matter and micronutrients necessary for the optimal functioning of the process [10], it’s temperature favors the growth of the Firmicute phyla of microorganisms [14], WHICH ARE responsible for the degradation of the cellulose present in the CM that is exposed by the pretreatment process.

3.2. Stage 2

Biomethane Potential Tests (BMP)

Figure 5 and Figure 6 show the cumulative CH₄ yields and the response surfaces (full factorial, Table 5) for the BMP processes of Stage 2, where a maximum in CH₄ yield at a temperature of 40 °C and a 1:1 ACoD (CM:FVW) is observed. These results are consistent with those obtained in Stage 1, since temperatures close to 45 °C promote the growth of various phyla of methanogenic and thermophilic microorganisms and co-digestion favors a nutritional balance, as described in Section 3.1.2. For these specific operating conditions, the pretreatment does not have a statistically significant effect on CH₄ yield compared to co-digestion and temperature. Table 5 confirms the influence of temperature on CH₄ production in the second stage, which is also significant. However, for the experimental conditions of Stage 2, the other factors do not have a statistically significant effect. Nevertheless, this is valuable information, since it suggests that the levels of certain factors such as pretreatment are to be reduced within the limits of the levels for the second stage, without affecting the CH₄ yield.

It is notable that the yield of CH₄ in Stage 2 decreased up to 56% compared to the results obtained in Stage 1. This may be due to an inhibition by accumulation of volatile fatty acids, which are up to 30 times higher compared to the concentration in Stage 1 (Table 6). A higher proportion of FVW, which contain high concentrations of easily biodegradable organic matter, increases the generation of these acids due to their rapid degradation [9].

3.3. Environmental Impact Assessment of the BMP

The results of the environmental impact analysis for all the schemes evaluated in both experimental designs are shown in Figure 7A,B. Figure 7A shows that the main environmental impacts are produced in the process stages which require energy consumption, specifically the pretreatment and heating stages of the WtE-AD process. This is mainly due to the environmental impact of electricity generation in Mexico (based on fossil fuels), which generates 0.61 kg CO₂ eq kWh⁻¹ [24]. However, for processes at a temperature of 35 °C and 45 °C and a percentage of 50% and 75% of CM in the substrate, environmental credits are obtained in the CCI for the substitution of conventional electrical energy, equivalent to −21 kg CO₂ eq kg CM⁻¹. At these operating conditions, we obtained the highest yields of CH_4, and the environmental impacts of the process are completely mitigated.

Figure 7B shows that the Stage 2 experiments have high environmental impacts and little substitution of conventional electrical energy, due to the low yields obtained. These results indicate that the operating conditions measured in Stage 2 are energetically and environmentally unfeasible.

The results for the eco-technical ratio are shown in Figure 8A–C. Temperatures of 35 and 45 °C, as well as percentages of 50% to 75% of CM in the substrate mixture, and the GHG emissions of the proposed scenarios are lower than the emissions from the mesophilic mono-digestion process of CM (0.031 kg CO₂ eq kWh⁻¹). The highest GHG emissions were observed in the experiments with an operating temperature of 55 °C, exceeding the emissions of electricity production in Mexico by 8.5%. This is due to the low yield of CH₄ (<150 mL CH₄ g VS⁻¹) and the high energy requirements in the WtE-AD process to maintain the operating conditions (Figure 7A). In Figure 8A,B at temperatures of 35 °C and 45 °C, environmental credits of −0.049 to −0.063 kg CO₂ eq kWh⁻¹ are obtained (full factorial, Table 7). When a higher ACoD condition between 50 and 75% of CM is operated, this may be due to the synergistic effects substrate:cosubstrate where FVW help the effective degradation of CM [9]. It is important to note that the concentration of NaOH in the pretreatment stage does not has a notable influence on the GHG emissions and the eco-technical quotient (Figure 8A,B). The concentration of NaOH can be increased to increase CH₄ yields without compromising the environmental impact of the process. This is because the use of NaOH is a low GHG emissions activity, with an average of 3.75% of the total emissions.

4. Conclusions

In this work, a study was performed to optimize energy recovery of cow manure through a waste to energy process of anaerobic digestion, considering different conditions of alkaline pretreatment, co-digestion, and temperature regime. At a temperature of 45 °C, the highest cumulative yields of CH₄ (350.96 mL CH₄ g VS⁻¹) occurred possibly due to the promotion of a syntrophic synergy between the different groups of hydrolytic and methanogenic microorganisms of mesophilic and thermophilic regimes. An increase in NaOH concentration in the pretreatment process increases CH₄ yields perhaps due to CM delignification. In addition, carrying out co-digestion processes also helps to increase the yield of CH₄, perhaps because the incorporation of FVW helps the effective degradation of CM, through the incorporation of an easily biodegradable substrate which helps the growth and maintenance of anaerobic biomass. Similar results were found in the environmental technical analysis using the eco-technical quotient where environmental credits of up to −0.61 kg CO₂ eq kWh⁻¹ were found operating at pretreatment conditions of 10 g NaOH (100 g VS of CM)⁻¹, co-digestion 1:1 (CM:FVW), and temperatures of 45 °C due to the high substitution of conventional electrical energy in the process.

Increasing the proportion of fruit and vegetable waste results in a decrease in cumulative CH₄ yield (55%) and environmental credits (from −0.063 to 0.667 kg CO₂ eq kWh⁻¹) compared to 1:1 ratios and higher proportions of manure. This decrease may be due to the inhibition by accumulation of volatile fatty acids from the degradation of this waste and the resulting reduction in the CH₄ yield. The best scheme, among those studied here, for energy recovery of cattle manure through a waste to energy process of anaerobic digestion is through co-digestion with fruit and vegetable waste in a 1:1 ratio, with a pretreatment of 10 g NaOH (100 g VS of CM)⁻¹ and operating at a temperature of 45 °C.

Author Contributions

Conceptualization: P.R.-G. and A.A.-R.; methodology: A.A.-R., B.N.L.-H. and A.E.-B.; software: A.E.-B.; validation: P.R.-G., L.R.M.-M., M.M.A.-R. and A.P.-R.; formal analysis: A.A.-R. and P.R.-G.; writing: A.A.-R.; review and editing: P.R.-G. and L.R.M.-M.; visualization; visualization: A.P.-R.; supervision: P.R.-G.; funding acquisition: P.R.-G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

This is not applicable for this study, not ethical approval was required to carry out this research.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable. All information is included in the paper or Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AD	Anarobic digestion
WtE-AD	Waste-to-Energy from Anaerobic Digestion
GHG	Greenhouse gas
CM	Cattle manure
ACoD	Anaerobic co-digestion
FVW	Fruit and vegetables wastes
VS	Volatile solids
CCI	Climate change indicator

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Figures and Tables

Figure 1. Diagram of the energy recovery methodology of dairy cow manure (CM) in co-digestion with fruit and vegetable waste (FVW).

Figure 3. Cumulative methane production for Stage 1 experiments.

Figure 4. Response Surface for CH4 yield at (A) T = 35 °C, (B) T = 45 °C y and (C) T = 55 °C. The Y-axis indicates the % of CM in the co-digestion formulation, and the X-axis indicates the NaOH concentration in the pretreatment.

Figure 5. Cumulative methane production for Stage 2 experiments.

Figure 6. Response Surface for CH4 yield at (A) T = 40 °C, (B) T = 45 °C y (C) T = 50 °C. The Y-axis indicates the % of CM in the co-digestion formulation, and the Y-axis indicates the NaOH concentration in the pretreatment.

Figure 7. (A) Climate change indicator (kg CO2 eq kg CM−1) for each experimental treatment in Stage 1. (B) Climate change indicator (kg CO2 eq kg CM−1) for each experimental treatment in Stage 2.

Figure 8. Response Surface for the eco-technical ratio for Stage 1. Where The Y-axis indicates the % of CM in the co-digestion formulation, and the Y-axis indicates the NaOH concentration in the pretreatment. (A) T = 35 °C, (B) T = 45 °C y (C) T = 55 °C.

Table 1

Experimental matrix for the design of experiments in Stages 1 and 2.

ExperimentsStage 1	Pretreatmentg NaOH(100 g VS of CM)−1	Co-Digestion(% CM)	Temperature(°C)	ExperimentsStage 2	Pretreatmentg NaOH(100 g VS of CM)−1	Co-Digestion(% CM)	Temperature(°C)
1-1	1.0	50	35	2-1	20	50	40
1-2	10.0	50	35	2-2	10	50	40
1-3	1.0	100	35	2-3	20	20	40
1-4	10.0	100	35	2-4	10	20	40
1-5	1.0	50	55	2-5	20	50	50
1-6	10.0	50	55	2-6	10	50	50
1-7	1.0	100	55	2-7	20	20	50
1-8	10.0	100	55	2-8	10	20	50
1-9	1.0	75	45	2-9	20	35	45
1-10	10.0	75	45	2-10	10	35	45
1-11	5.5	50	45	2-11	15	50	45
1-12	5.5	100	45	2-12	15	20	45
1-13	5.5	75	35	2-13	15	35	40
1-14	5.5	75	55	2-14	15	35	50
1-15	5.5	75	45	2-15	15	35	45

Table 2

Process energy balance parameters.

Parameter	Variable	Value	Units	Reference
Conduction coefficient	λ	9.76	W m−1 K−1	[26]
Glass thickness	Lglass	0.003	m	Measured
Convection coefficient	$h$	10	W m−2 K−1	[27]
Heat transfer area for pretreatment process.	$A_{pt}$	0.006	m2	Measured
Heat capacity at constant pressure for pretreatment (CM + H2O).	$C{p}_{pt}$	4.17	kJ kg−1 K−1	[28]
Heat transfer area for anaerobic digestion process.	$A_{DA}$	0.002	m2	Measured
Heat capacity at constant pressure for substrate mixture for anaerobic digestion (CM + FVW + H2O)	$C{p}_{DA}$	4.15	kJ kg−1 K−1	[28]
Drying efficiency	${\eta }_{sec}$	31.0	kJ kgH2O−1	[20]

CM: Cattle manure; FVW: Fruit and vegetable wastes.

Table 3

Substrates, inoculum, and pretreatment effluents characterization.

	Fresh Substrates			Pretreatment Effluents
Parameter	Inoculum	FVW	CM	CMNaOH 1%	CMNaOH 5.5%	CMNaOH 10%	CMNaOH 15%	CMNaOH 20%
Moisture content (%)	94.85 ± 0.09	89.7 ± 0.09	85.23 ± 0.12	94.82 ± 0.01	95.52 ± 0.39	91.53 ± 0.27	91.02 ± 0.14	90.61 ± 0.35
Total solids (%)	5.15 ± 0.09	11.3 ± 0.09	14.77 ± 0.12	5.18 ± 0.01	4.48 ± 0.39	8.47 ± 0.27	8.98 ± 0.14	9.39 ± 0.35
Volatile solids (% ST)	60.03 ± 0.26	90.48 ± 0.16	82.86 ± 0.21	81.82 ± 0.49	77.61 ± 0.63	54.93 ± 0.61	52.49 ± 0.29	51.94 ± 0.41
Ash (% ST)	39.97 ± 0.26	9.52 ± 0.16	17.14 ± 0.21	18.18 ± 0.49	22.39 ± 0.63	45.07 ± 0.61	47.51 ± 0.29	48.06 ± 0.41
Alkalinity (g CaCO3 L−1)	5.01 ± 0.27	14.76 ± 0.75	16.67 ± 1.02	ND	ND	ND	ND	ND
Volatile fatty acids (g L−1)	0.28 ± 0.06	6.10 ± 0.10	3.93 ± 0.08	ND	ND	ND	ND	ND
Cellulose (g g VS−1)	0.15 ± 0.01	0.16 ± 0.01	0.32 ± 0.03	0.32 ± 0.01	0.36 ± 0.00	0.37 ± 0.02	0.37 ± 0.01	0.27 ± 0.00
Hemicellulose (g g VS−1)	0.35 ± 0.00	0.14 ± 0.01	0.39 ± 0.00	0.39 ± 0.01	0.33 ± 0.02	0.32 ± 0.01	0.31 ± 0.03	0.30 ± 0.01
Lignin (g g VS−1)	0.09 ± 0.01	0.296 ± 0.03	0.36 ± 0.00	0.36 ± 0.01	0.33 ± 0.02	0.29 ± 0.01	0.26 ± 0.00	0.21 ± 0.01
Total sugars (mg mL−1)	0.28 ± 0.07	8.19 ± 0.24	0.46 ± 0.08	0.46 ± 0.03	0.37 ± 0.02	0.72 ± 0.02	1.02 ± 0.01	1.63 ± 0.03
Total phenolic compounds (mg g−1)	0.29 ± 0.02	1.05 ± 0.08	0.19 ± 0.00	0.19 ± 0.01	0.21 ± 0.00	0.35 ± 0.00	0.41 ± 0.01	0.43 ± 0.01

ND: Not determined.

Table 4

Coefficient values for statistical model and p-values for experimental design Stage 1.

Parameters Model	Coefficient	p-ValueProb < 0.05
A	25.61	0.033
B	0.793	0.184
C	105.4	0.332
AB	−0.259	0.042
C2	−1.18	0.0002
Intercept	−2109	-

R² = 0.86, A: pretreatment; B: co-digestion; C: temperature.

Table 5

Coefficient values for statistical model and p-values for experimental design Stage 2.

Model	Coefficient	p-ValueProb < 0.05
A	−8.92	0.816
B	17.95	0.242
C	4.45	0.042
AB	−0.02	0.894
AC	0.20	0.695
BC	−0.37	0.058
Intercept	−140.56	-

R² = 0.72, A: pretreatment; B: co-digestion; C: temperature.

Table 6

Comparison of volatile fatty acids concentration in the BMP effluents of Stages 1 and 2.

Experiment	VFA Stage 1(g L−1)	VFA Stage 2 (g L−1)
E1	0.72	3.24
E2	0.67	2.52
E3	0.69	12.00
E4	1.01	12.1
E5	0.78	9.0
E6	0.96	9.29
E7	1.10	13.08
E8	0.84	11.05
E9	0.48	13.99
E10	0.55	10.80
E11	0.49	10.13
E12	0.58	10.20
E13	0.93	10.1

Table 7

Coefficient values for statistical models and p-values for eco-technical ratio corresponding to experimental design Stage 1.

Model	Coefficient	p-ValueProb < 0.05
A	−0.032	0.145
B	−0.011	0.001
C	−0.168	0.0003
AB	1.99 × 10−4	0.418
AC	−9.64 × 10−4	0.149
BC	−1.17 × 10−4	0.300
A2	4.75 × 10−3	0.085
B2	1.45 × 10−4	0.098
C2	2.23 × 10−3	0.004
Intercept	3.587	-

R² = 0.97, A: pretreatment; B: co-digestion; C: temperature.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su142416945/s1, Figure S1: Biochemical methane potential tests to assess the influence of CM pretreatment; Figure S2: Boundary diagram of CM recovery scenario in mono-digestion under mesophilic conditions; Figure S3: Representative diagram of the Applikon^® bioreactor for the inoculum demethanization process; Figure S4: Image of the Applikon^® bioreactor for the inoculum demethanization process; Table S1: Characterization parameters for substrates and effluents; Table S2: Categorization of the fruit and vegetable waste collected at the supply center; Section S1: Life cycle inventory.

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