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Article

Determination of Particle Size for Optimum Biogas Production from Ouagadougou Municipal Organic Solid Waste

by
Mahamadi Nikiema
1,2,*,
Narcis Barsan
3,*,
Amidou S. Ouili
2,
Emilian Mosnegutu
3,
K. Marius Somda
2,
Ynoussa Maiga
1,
Compaoré Cheik Omar Tidiane
2,
Cheik A. T. Ouattara
2,
Valentin Nedeff
3 and
Aboubakar S. Ouattara
2
1
Higher Institute of Sustainable Development (ISDD), University of Yembila Abdoulaye Toguyeni, Fada N’Gourma 75000, Burkina Faso
2
Research Center in Biological Food and Nutritional Sciences (CRSBAN), Joseph KI-ZERBO University, 03 BP 7131, Ouagadougou 10000, Burkina Faso
3
Department of Environmental Engineering and Mechanical Engineering, Vasile Alecsandri University of Bacau, 157, Calea Marasesti, 600115 Bacau, Romania
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(22), 9792; https://doi.org/10.3390/su16229792
Submission received: 21 August 2024 / Revised: 10 October 2024 / Accepted: 6 November 2024 / Published: 10 November 2024
(This article belongs to the Special Issue Application of Biotechnology in Waste Treatment and Valorization for Energy Recovery)
Browse Figures
Figure 1
<p>Different sizes of waste used in anaerobic digestion tests.</p> "> Figure 2
<p>Principal component analysis plot of variables biogas production, CO<sub>2</sub> and CH<sub>4</sub> proportions and distribution of combinations on 1 × 2 axis of principal components.</p> "> Figure 3
<p>Three-dimensional-view response surface plot corresponding to the chosen equation: (<b>a</b>) effect of particle size and incubation time on CH<sub>4</sub> content and (<b>b</b>) effect of particle size and incubation time on CO<sub>2</sub> content.</p> ">
Versions Notes

Abstract

:
Anaerobic digestion’s contribution to sustainable development is well established. It is a sustainable production process that enables energy to be saved and produced and efficient pollution control processes to be implemented, thereby contributing to the sustainable development of our societies. Optimizing biogas yields from the anaerobic digestion of municipal organic waste is crucial for maximum energy recovery and has become an important topic of interest. Substrate particle size is a key process parameter in biogas production and precedes other pretreatment methods for most organic materials. This study aims to evaluate the impact of particle size and incubation period on biomethane production from municipal solid waste. Sampling of municipal solid waste was carried out in waste pre-collection in the city of Ouagadougou, Burkina Faso. Waste characterization showed lignocellulolytic green waste (grass, dead leaves), waste composed of fruit and leafy vegetables and leftover food waste. TableCurve 3D v4.0 software was used to develop an optimal mathematical model to correlate particle size and biomethane productivity to describe optimal production parameters. Particle sizes ranging from 2000 to 63 µm high biogas production values, specifically 385.33 and 201.25 L·kg−1 of MSV. PCA analysis clearly showed a high correlation between particle size and biogas production, with optimum production recorded for size 250 µm with a biomethane production value of 187.53 L·kg−1 of MSV. The average relative errors and RMSE for CH4 content were improved by 24.31% and 44.97%, respectively. The data calculated with the developed mathematical model and the existing experimental data were compared and permutated to validate the model. This work enabled the identification of a mathematical model that describes the correlations between the input parameters of an experiment and the monitored parameters, as well as the definition of the particle size that allows for the optimal production of biomethane.

1. Introduction

The anaerobic digestion of energy crops, residues, and waste has been developed to reduce greenhouse gas emissions and facilitate the production of renewable energy [1]. Liu et al. [2] reported that in 2007, primary energy production from biogas in Europe was 8.3 Mtoe (million tons of oil equivalent), with France contributing 0.5 Mtoe. Landfill sites accounted for 58% of the production, while sewage sludge digestion and other sources such as effluent digestion and methanization of household or agricultural waste accounted for 18% and 24%, respectively. The relationship between sustainability, circular economy and anaerobic digestion long-established according to Mancini and Raggi [3]. The role of anaerobic digestion has also clarified in circular economy and environmental sustainability [4,5]. Nikiema et al. [6] emphasized the importance of incorporating biogas into the energy mix to achieve energy autonomy. Anaerobic digestion is a biological process that converts complex organic matter into biogas and digestate through the action of a microbial consortium [7]. The process is characterized by four main stages: hydrolysis, acidogenesis, acetogenesis, and methanogenesis [7]. Many organic residues are eligible for biogas production, including the organic fraction of municipal waste. The eligibility of these deposits depends on their biophysical–chemical properties, material structure, absence of undesirable materials, biochemical composition, and anaerobic biodegradability [2]. According to Chew et al. [8], to improve anaerobic micro-organisms with high metabolic activity, it is necessary to control environmental conditions. Among these environmental conditions, the temperature factor is crucial because it can affect the activity of the enzymes and coenzymes responsible for methane yield [9]. The pH controls the performance and stability of digester because a significant variation beyond the ranges (6.5 and 8.0) will affect the functioning of methanogenic archaea and lead to inhibition [10]. The optimum C/N ratio of 25–30 has been recommended to ensure maximum bacterial activity [11]. Another important control parameter is the accumulation of volatile fatty acids, which could inhibit the anaerobic digestion process, leading to a decrease in biogas yield [12]. Several studies have shown that hydrolysis is the limiting step in this process. This is due to the formation of toxic by-products, such as complex heterocyclic compounds, or non-desirable volatile fatty acids (VFA) formed during the hydrolysis step [13,14,15]. To enhance the hydrolysis rate and optimize the process, researchers have studied various pretreatments. These include biological transformations and physico-chemical pretreatments such as mechanical (grinding), physical (thermal) [16], and chemical (acid or alkali) [17] ones or a combination of these methods [18].
Characterization of solid particles constituting waste is very important in the process of biogas production [19,20]. Mechanical fractionation is a necessary step in lignocellulosic bioconversion to (i) decrease particle size and increase total accessible specific surface area, (ii) increase the pore size of particles and the number of contact points for interparticle bonding in the compaction process, and eventually (iii) decrease cellulose crystallinity [21].
Lignocellulosic biomass mainly consists of cellulose, lignin, and hemicelluloses [22]. Lignin and hemicelluloses stop enzymes from working, which stops cellulose being converted. Furthermore, lignin has the capacity to irreversibly adsorb cellulases and other enzymes during enzymatic hydrolysis as a consequence of its hydrophobic structural characteristics, including hydrogen bonds, methoxy groups, and polyaromatic structures. Grinding can break the physical barriers formed by lignin and hemicelluloses. This improves the efficiency of enzymatic hydrolysis of cellulose [23]. Indeed, Parra-Orobio et al. [24,25] recommended several particle sizes ranges, from 0.1 mm to 30 mm, for optimal biomethane production. Banks et al. [26] experimented with particle sizes between 10 mm and 30 mm, and optimal methane production was achieved with less excessive volatile fatty acid (VFA) production. Izumi et al. [17] found that excessive particle size reduction could lead to VFA accumulation, resulting in a decrease in biogas production rate and material solubility. Mshandete et al. [27] discovered that cutting sisal fiber waste to a 2 mm size increased methane yield by 23%, resulting in 0.22 m3 CH4/kg of volatile solids, compared to 0.18 m3 CH4/kg of volatile solids for untreated fibers. Hills and Nikano’s [28] research on tomato waste chopped to particle sizes in the range 1.3–20 mm reported an increased biogas yield that was inversely proportional to the average particle diameter. Similarly, Angelidaki and Ahring [29] reported a potential increase in methane yield of 16% for macerated manure biofibers with particle sizes between 1 and 2 mm, as compared to fibers of a 5 mm particle size. Sebola et al. [30] investigated the effect of particle sizes 500 μm, 250 μm, 100 μm and 25 μm on biogas production. According to the authors, at the optimal particle size (25 μm), the production of methane was 3–30% higher as compared to that under 100 μm, 250 μm and 500 μm particle sizes. According to Morgenroth et al. [31] particle size and particle composition determine the rate and mechanism of hydrolysis and degradation in wastewater treatment. Most of the biodegradable organic matter is in the range of 10–30 to 100 μm. Microorganisms can directly take up particles that are smaller than 10−3 μm [32]. In view of the work carried out, the speed and stability of anaerobic digestion depends mainly on the particle size of the input material. A substantial body of research indicates that varying particle sizes can facilitate optimal biomethane production. In order to ascertain the optimal particle size for the production of biomethane, the use of mathematical models would be a viable approach. Several studies, in addition to usual optimization methods like pretreatment and the use of a co-digestion system, used a mathematical model. Mechanical pretreatment, such as shredding, grinding and milling, transforms the biomass into fine particles, increasing the surface area of the cellulose and reducing the degree of crystallinity of the celluloses as well as the degree of polymerization of the celluloses and hemicelluloses [33]. Thermal pretreatment uses a combination of temperature and pressure to transform particles [34]. Chemical pretreatment, which can be acidic, alkaline or oxidative, uses the properties of its solutions to degrade the organic fraction of lignocellulosic substrates [35,36]. As for biological pretreatment, industrial enzymes, such as cellulase and xylanase or lignolytic enzymes (laccase, lignin and manganese peroxidase) as well as selected organisms, are used to break down all the components of lignocelluloses, including lignin, the polymer most resistant to microbial attack [37]. According to Yu et al. [38] alternatively, design and optimization of anaerobic digestion processes for biogas production can be enhanced via validated mathematical models developed from mechanistic studies that lead to a more in depth understanding of the very complex transport phenomena, microbial biochemical kinetics, and stochiometric relationships associated with anaerobic digestion.
Zheng [39]’s reported mathematical model is a convenient tool for understanding the process, defining its solution, and optimizing its design and operation. Several mathematical models were developed for anaerobic digestion, ranging from simple to complex. A dynamic description for methanogenic biomass is given by [40,41]. Under the above assumptions and conditions, the dynamical behavior of acidogenic biomass was described by Hill and Barth [40] and Moletta et al. [41]. According to Flores-Estrella et al. [42], the AD model (10) predicts washout, acidification, and normal operation, especially the acidification conditions, enabling the process and experimental strategies to be designed with fewer disturbances. Anaerobic Digestion Model No. 1 (ADM1) was proposed by the IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes [43]. Piatek et al. [44] proposed a new mathematical model describing a biotechnological process of simultaneous production of hydrogen and methane by anaerobic digestion. A surface response design was used to investigate the effects of multiple independent variables and their interaction on response variables [45,46]. Table Curve 3D is a non-linear surface fitting software that can provide an optimal model by fitting frequently encountered integrated solid models. In light of the aforementioned context, the objective of this study is to evaluate the impact of particle size and incubation period on biomethane production. To achieve this, we will use Table Curve 3D software to develop an optimal mathematical model.

2. Materials and Methods

2.1. Sampling and Characteristic of Waste

Sampling of municipal solid waste was carried out at three (03) waste pre-collection centers in the city of Ouagadougou, located in district 2 (12°22′28.168″ north latitude, 1°32′6.742″ west, altitude 335 m), 12 (12°19′48.061″ north latitude, 1°31’20.338″ west longitude, altitude 349 m) and 3 (12°23′51.3″ north latitude, 1°32′06.7″ west longitude, altitude 326 m). This waste comes from the city’s households, yards and markets. The waste was collected in 50 kg bags. Manual sorting was carried out to determine the composition of the organic fraction of waste.

2.2. Preparing Samples of Municipal Organic Waste

The organic fraction was selected from the waste collected from the pre-collection centers located in the city of Ouagadougou. Selected waste was mixed, sorted and dried in the sun. After sorting and drying, the organic fraction of municipal solid waste was ground using a mortar. The shredded material was sieved using a Retsch AS 200 digital electromagnetic sieve shaker (Retsch, Haan, Germany) with 08 successive sieves of the following sizes: 4000 µm, 2000 µm, 1000 µm, 500 µm, 250 µm, 125 µm, 63 µm and 45 µm. Figure 1 shows different images of the sizes of municipal organic waste.

2.3. Biogas Production Setup

Batch reactors were adapted using 120 mL bottles. A quantity of 0.8 g of waste powder was placed in a bottle containing 40 mL of phosphate buffer (K2HPO4 2 g·L−1 and NH4Cl 2 g·L−1). After this operation, the bottles were hermetically sealed using screw caps fitted with septa to ensure a perfect gas-tight seal. Anaerobiosis was then achieved in the medium by degassing under a flow of nitrogen. Inoculation was carried out using sterile syringes and bovine dung as the inoculum. The quantity of inoculum injected represented 10% (v/v) in a final volume of 40 mL. The flasks were then incubated at 37 °C for 25 days. Experiments were performed in triplicate. Biogas production was estimated using the liquid displacement method reported by Nikiema et al. [47]. The gaseous products (CO2 and CH4) were analyzed by gas chromatography (Girdel series 30 chromatograph, Girdel Instruments, Suresnes, France) with a catharometer fitted with a thermal conductivity detector (TCD) equipped with a SERVOTRACE-type Sefram Paris 1 mV potentiometric recorder).

2.4. Data Analysis

XLSTAT 7.5.2 was used for the statistical analysis of the data. An analysis of variance (ANOVA) was used to compare the mean values of the different variables, using Fisher’s t-tests with a probability threshold of p = 5%. ANOVA was also used to show the existence of statistical differences between mean values. TableCurve 3D v4.0 software was used to develop an optimal mathematical model. It facilitates the identification of the equation that most closely approximates a viable model. The generation of mathematical models, which correspond to the results, was achieved using TableCurve 3D software, a program that can generate different types of response surfaces, surfaces that correspond to the 450 million equations existing in the program database [48].

3. Results and Discussion

3.1. Characterization of Organic Fraction of Municipal Solid Waste from Ouagadougou City

The organic fraction of the municipal waste sampled consisted mainly of lignocellulolytic green waste (grass, dead leaves), waste composed of fruit and leafy vegetables and leftover food waste (Table 1). The lignocellulosic green waste was composed of a variety of materials, including lawn clippings, grass, branches and twigs, pawpaw leaves, and dead leaves. The category of fruit, vegetables and other waste included green beans, peppers, squash, parsley, okra fruits, mint, cassava rhizomes, Saba senegalensis fruits, cassava rhizomes, sweet potato, oranges, lemons, cabbage, banana peel, mangoes, onions and cucumbers. The residual food waste categories comprised bread and other perishable items. The waste stream from yogurts, markets and households contained organic matter. The mixture of these types of waste is a preferred substrate for anaerobic digestion process.

3.2. Biogas Production Depending on Particle Size

Table 2 presents the production of biogas and the proportions of CH4 and CO2 in relation to the size of the organic fraction of municipal solid waste. The results indicate that particle size has a significant impact on the production of biogas, CH4 and CO2 content (p ˂ 0.05). These findings are consistent with previous studies [33,49,50]. Particle sizes ranging from 2000 to 63 µm exhibit high biogas production values, specifically 385.33 and 201.25 L·kg−1 of MSV. Methane production did not significantly differ for particle sizes between 2000 and 45 µm. However, as particle size increased from 500 to 4000 µm, CH4 yields tended to decrease from 182.22 L (CH4)·kg−1 MSV to 40.58 L (CH4)·kg−1 MSV. Hajji and Rhachi [49] conducted an evaluation of the effect of different particle sizes (10 mm, 20 mm, 30 mm, and 100 mm) on biogas production. They found a strong correlation between particle size and biogas production. Sharma et al. [32] discovered that the highest quantity of methane (362 L·kg−1 of TS) was produced by 0.088 mm particles of Ipomoea fistulosa leaves, followed by cauliflower leaves (348 L·kg−1 TS) and banana peels (334 L·kg−1 TS) of the same particle size. Production was also affected by the relatively small size of the particles, which measured 45 µm on average, resulting in an average of 98.22 L (CH4) per kg of MSV. Ghizzi et al. [51] observed an increase in hydrolysis rate as particle size decreased from 720 to 48 µm when working on wheat straw. However, no improvement was reported when particle size decreased below 48 µm. Armah et al. [52] studied the effect of particle size on sugarcane bagasse and corn silage with industrial wastewater for biogas production. They found that the highest biogas yield of 125 mL/d was observed at a particle size of 0.4 mm. Mshandete et al. [27] found that particle size reduction significantly affected the degradability of sisal fiber waste. Treatment through particle size reduction destroys physical barriers and increases the specific surface area of particles, facilitating their initial attack during the hydrolysis phase [27,53,54]. Luo et al. [55] showed that the maximum methane production obtained with a 1 mm sieve size of whole rice straw was 176.47 mLCH4g−1 vs. Indeed, in the 1 mm range, the particle size led to significant effects on the bacterial diversity. The most dominant 1 mm sieve size group at 1 h was Firmicutes (61.5%), followed by Proteobacteria (9.3%), Chloroflexi (8.3%), Bacteroidetes (4.1%), Cyanobacteria/Chloroplast (4.6%), Atribacteria (1.1%), Planctomycetes (1.1%), and Actinobacteria (2.2%), which was similar to that in the inoculum [55].

3.3. Biogas Production Depending on Particle Size PCA of Biogas Production as a Function of Particle Size

Figure 2 shows how CH4 and CO2 production vary with substrate particle size. The F1xF2 factorial planes demonstrate the heterogeneous profile of the substrates. The correlation between variables and factors demonstrates a robust correlation between elevated CH4 and F1 values (r2 = 0.93), while the correlation at F2 is comparatively weaker (r2 = 0.37). Positive values on the F1 axis and negative values on the F2 axis indicate sizes strongly correlated with high CH4 production and low CO2 production. PCA confirmed that biomethane production depends on size and allowed data to be grouped into four groups. Group 1, with particle sizes of 4000 µm and 500 µm, showed low biogas production (CH4 and CO2). Group 2, with particle sizes of 1000 µm and 1000 µm, showed a correlation with high CO2 production. Group 3, consisting of particles with sizes of 45, 63, and 125 µm, exhibited high CH4 production and low CO2 production. Group 4, consisting of particles with a size of 250 µm, showed better CH4 and CO2 production. Biomethane production increases with smaller particle sizes, but productivity decreases at very small sizes. A particle size of 250 µm demonstrated the highest degradability, and therefore the highest production of biomethane. These results are in agreement with those of previous work [27].

3.4. Mathematical Models

By plotting the tabular data for the CH4 content in Table Curve 3D® v.4 software, multiple equations were generated, but only one equation was selected for each model, taking into consideration its high precision (Equations (1) and (2)). Parameters X and Y represent substrate size and incubation time, respectively. Z represents CH4 or CO2 production. Corresponding to Equation (1) and (2), for the two parameters studied, the values of the correlation coefficients R2 were 0.84 and 0.75, respectively. According to Mosnegutu et al. [48] and the mathematical models generated by the Table Curve 3D program, it can be observed that the value of the correlation coefficient is between 0.86 and 0.99, so the values obtained through the mathematical models coincide with the real values used for the model’s elaboration. Our R2 value was close to these values. The predicted values were obtained from model Equations that had the best results of regression analysis. Pishgar-Komleh et al. [56] found similar results, demonstrating a strong correlation (R2 value of 0.88) between actual and predicted values. The graphical representations of equations were generated by the Table Curve 3D program and are presented within Figure 3. Within this type of graph, the difference between the surface generated by the mathematical models, the surface that in this case coincides with the XOY plane, and the experimental values is realized.
Z C H 4 % = a + b X + c X 2 + d X 3 + e X 4 + f Y + g Y 2 + h Y 3 + i Y 4
a = 74.55; b = −26.26; c = 3.68; d = −0.17; e = −0.0027; f = −0.0069; g = 1.07 × 10−5; h = 1.01 × 10−8; i = −1.85 × 10−12
Z C O 2 % = a + b X + c X 2 + d X 3 + e X 4 + f Y + g Y 2 + h Y 3 + i Y 4
a = −161.459; b = 81.94; c = −10.05; d = 0.47; e =−0.007; f = 0.01; g = 3.27 × 10−5; h = −2.52 × 10−8; i = 4.13 × 10−12
Indeed, the evolution of biogas with respect to particle size indicates significant production of CO2 until days 5 and 10, followed by a gradual decrease until day 25 (Figure 3). The proportion of CH4 increases during this period, reaching 70 to 90% for particle sizes of 45, 63, 125, 250, 500, 1000 and 2000 µm. The production of CH4 varies with particle size, with an optimal range for production. Any increase or decrease beyond the specified size range causes a delay in methanogenesis. This delay is particularly noticeable in flasks with extreme sizes (>2000 µm and ˂63 µm). In fact, reducing particle size to ˂63 µm leads to a more advanced hydrolysis and acidogenesis phase, resulting in an accumulation of VFAs that inhibit methanisation [57,58]. Additionally, CO2 production increases significantly with a reduction in particle size. It is believed that the increased production of CO2 is a result of an extension of the hydrolysis and acidogenesis phase [57]. Conversely, low CO2 production is observed for particle sizes greater than 2000 µm due to the difficulty in degrading the substrate. The observed difference is attributed to the variation in the surface area of the substrate exposed to microbes. Studies on these have shown that larger particle sizes result in less surface area being exposed to microbes, which in turn reduces gas production during anaerobic digestion. Several researchers have supported this claim, including Chynoweth and Pullammanappalli [59] and Sharma et al. [32]. Additionally, a significant CO2 level decrease was observed over the 25-day incubation period. The data presented in Figure 3a,b show CO2 decrease and CH4 increase. This suggests that CO2 was reduced to CH4, which is consistent with the findings of Nikiema et al. [60]. Previous studies have demonstrated the presence of hydrogen-trophic methanogenic bacteria that use molecular hydrogen to reduce CO2 to CH4 [61]. Sebola et al. [30] showed that at the optimal particle size (25 μm), the production of methane was 3–30% higher as compared to that under 100 μm, 250 μm and 500 μm particle sizes in mesophillic batch digestion tests. The action of lignocellulosic complex stop enzymes results in the cessation of cellulose conversion. Indeed, Rahmati et al. [24] have reported the ability of lignin to irreversibly adsorb cellulases and other enzymes during enzymatic hydrolysis due to its hydrophobic structural features, including hydrogen bonds, methoxy groups, and polyaromatic structures. The mechanical treatment, which involves reducing the size of the material, results in the disorganization of the lignocellulosic complex, which is primarily composed of cellulose, lignin, and hemicelluloses [22]. This enhances the efficiency of the enzymatic hydrolysis of cellulose [24]. The soluble products released by the degradation of cellulose by cellulases typically exhibit a length of two to six sugars. These sugars undergo oxidation at one end of the chain and are subsequently converted by β-glucosidases into monomers, representing the final product of cellulose saccharification [24]. The aforementioned parameters enhance the digestibility and conversion of saccharides during enzymatic hydrolysis and bioconversion. Among the numerous parameters that can impact biomethane productivity, particle size is a critical determinant. According to Izumi et al. [14] particle size also affected pH evolution. These authors indicated the possibility that the excessive particle size reduction of the substrate accelerated hydrolysis and acidogenesis in the early stage of anaerobic digestion, resulting in the accumulation of volatile fatty acids (VFAs). Thiele and Zeikus [62] indicated that the formation and consumption of some VFAs, such as propionic and butyric acid, demonstrate their conversion to acetic acid by acetogenic bacteria; acetic acid is considered to be the major precursor of methane.

3.5. Mathematical Models Validation

The mathematical model shown validated, from one degree to the next, the measured and predictable values, the average relative errors and the root mean square error (RMSE). The data calculated with the developed mathematical model and the existing experimental data were compared in Table 3 and Table 4. The method used to evaluate the performances of the above correlations was the comparison of the average relative error and root mean square error (RMSE). The models’ predicted relative errors for all experimental conditions were transformed into positive values to reduce the mismatch phenomenon when both positive and negative errors existed. The root is square error (RMSE), a frequently used indicator of the differences between the values predicted by a model and those obtained from experiments, was calculated using the equation described by Pan et al. [63]. The average relative errors and RMSE for CH4 content were improved by 24.31% and 44.97%, respectively. Values of 26% and 43% were found with CO2 content. Therefore, the linear correlation between CH4 content, particle size, and incubation time was not accurate enough. It is recommended to seek more appropriate and complex correlations by measuring biogas production from day 10 onwards. Measurements taken from day 10 onwards showed an improvement in mean relative error and RMSE, which were 12.93% and 17.27%, respectively. Below day 10, biogas production did not follow a linear law as a function of particle size.

4. Conclusions

This study shows that a variety of organic substrates can be good candidates for anaerobic digestion and that substrate size is essential for optimum biomethane production. Biomethane production is low for very large substrate sizes above 1000 µm, but increases with size reduction down to 250 µm, below which production falls. A particle size of between 1000 and 45 µm means that the substrate is more readily available and biomethane can be produced more efficiently. The five groups shown in the PCA clearly indicate a group made up of size 250 µm particles, which shows a positive correlation in biomethane production. The mathematical model validated, from one degree to the next, the measured and predictable values, the average relative errors and root mean square error (RMSE). The data calculated with the developed mathematical model and the existing experimental data were compared and permutated to validate the model. This model could be used for different types of organic waste from farming and tested on the pilot scale to confirm the results.

Author Contributions

Conceptualization, M.N.; methodology, M.N. and K.M.S.; software, M.N.; validation, A.S.O. (Amidou S. Ouili), E.M. and Y.M.; formal analysis, C.C.O.T. and C.A.T.O.; data curation, A.S.O. (Aboubakar S. Ouattara) and E.M.; writing—original draft preparation, M.N.; writing—review and editing, N.B.; supervision, V.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors acknowledge the support of the University Agency of Francophonie (AUF), Vasile Alecsandri University of Bacau (Romania), Department of Environmental Engineering and Mechanical Engineering and University Joseph KI-ZERBO (Burkina Faso).

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 16 09792 g001
Figure 1. Different sizes of waste used in anaerobic digestion tests.
Figure 1. Different sizes of waste used in anaerobic digestion tests.
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Figure 2. Principal component analysis plot of variables biogas production, CO2 and CH4 proportions and distribution of combinations on 1 × 2 axis of principal components.
Figure 2. Principal component analysis plot of variables biogas production, CO2 and CH4 proportions and distribution of combinations on 1 × 2 axis of principal components.
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Figure 3. Three-dimensional-view response surface plot corresponding to the chosen equation: (a) effect of particle size and incubation time on CH4 content and (b) effect of particle size and incubation time on CO2 content.
Figure 3. Three-dimensional-view response surface plot corresponding to the chosen equation: (a) effect of particle size and incubation time on CH4 content and (b) effect of particle size and incubation time on CO2 content.
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Table 1. Characteristic of organic fraction of municipal solid waste from Ouagadougou.
Table 1. Characteristic of organic fraction of municipal solid waste from Ouagadougou.
Type of WasteComposition
Lignocellulosic green wasteMango tree leaves
Calotropis procera leaves
Leaves of Saba senegalensis
Pawpaw leaves
Branches and twigs
Grass
Lawn clippings
Fruit, vegetables and other wasteGreen beans
Peppers
Squash Parsley
Peppers
Okra fruits
Mint
Cassava rhizomes
Sweet potato
Saba senegalensis fruits
Oranges
Lemons
Tomato
Cabbage
Aubergines
Carrot leaves
Banana peel
Mangoes
Onions
Cucumbers
Leftover foodBread
Table 2. Production of biogas, CH4 and CO2 as a function of particle size.
Table 2. Production of biogas, CH4 and CO2 as a function of particle size.
Average (L·kg−1 de MSV)
Size (µm)BiogasCH4CO2
45184.87 bc98.22 ab52.82 c
63210.92 bc125.12 ab53.49 c
125201.25 bc107.08 ab60.33 c
250308.89 ab187.53 a96.28 bc
500385.33 a182.22 bc142.53 bc
1000238.24 abc115.93 ab116.71 a
2000249.58 abc129.33 ab106.72 ab
4000115.62 c40.58 c39.14 bc
In a column, values with different letters are significantly different according to Fisher’s LSD test at the 5% threshold.
Table 3. Observed and predicted values of CH4 content as function of particle size.
Table 3. Observed and predicted values of CH4 content as function of particle size.
X Observed
(Time Days)		Y Observed
(Size µm)		Z Observed
(%CH4)		Z Predicted
(%CH4)		Z ResidualAverage Relative ErrorRMSE
25400055.6567.34−11.690.170.42
25200088.5184.184.320.050.23
25100088.8979.719.180.120.34
2550076.3384.02−7.690.090.30
2525090.8486.764.080.050.22
2512589.7087.991.710.020.14
256388.2088.53−0.330.000.06
254589.0988.670.420.000.07
20400051.9557.28−5.330.090.31
20200081.2274.127.100.100.31
20100068.4269.65−1.220.020.13
2050065.3173.96−8.650.120.34
2025076.1476.70−0.560.010.09
2012571.1477.93−6.790.090.30
206387.0878.478.610.110.33
204585.4678.616.840.090.30
15400042.5843.69−1.110.030.16
15200058.5860.53−1.950.030.18
15100052.0756.05−3.980.070.27
1550044.3860.37−15.990.260.51
1512562.9264.34−1.420.020.15
156375.7664.8810.880.170.41
154556.1465.02−8.880.140.37
10400019.0813.395.690.420.65
10200021.4930.23−8.740.290.54
10100021.1525.75−4.600.180.42
1050035.8030.065.730.190.44
1025044.4832.8011.680.360.60
1012531.7334.04−2.310.070.26
104526.8534.72−7.870.230.48
540006.25−6.2212.472.011.42
520009.2810.62−1.340.130.36
5100012.756.156.601.071.04
550014.6410.464.180.400.63
52506.5113.20−6.690.510.71
512510.5814.43−3.850.270.52
56310.5814.97−4.390.290.54
5458.1415.11−6.980.460.68
Table 4. Observed and predicted values of CO2 content as function of particle size.
Table 4. Observed and predicted values of CO2 content as function of particle size.
X Observed
(Time Days)		Y Observed
(Size µm)		Z Observed
(%CO2)		Z Predicted
(%CO2)		Z ResidualAverage Relative ErrorRMSE
25400016.7412.064.680.390.62
25200010.3120.98−10.660.510.71
2510009.1127.09−17.980.660.81
2550023.6715.558.120.520.72
252509.169.34−0.180.020.14
2512510.466.843.620.530.73
256311.805.865.941.011.01
254512.075.616.461.151.07
20400020.7321.83−1.100.050.22
20200014.3230.75−16.430.530.73
20100031.5836.86−5.280.140.38
2050034.6925.329.370.370.61
2025023.8619.114.750.250.50
2012528.8616.6112.240.740.86
206312.9215.63−2.710.170.42
204514.5415.38−0.840.050.23
15400015.0021.11−6.110.290.54
15200029.9930.02−0.030.000.03
15100044.9936.138.850.240.49
1550033.2624.608.660.350.59
1525010.3718.39−8.020.440.66
1512515.0015.89−0.890.060.24
156312.1114.90−2.790.190.43
154515.0014.660.340.020.15
10400056.6755.990.680.010.11
10200076.0864.9011.180.170.41
10100084.3571.0113.330.190.43
1050059.5859.480.100.000.04
1025052.4853.27−0.780.010.12
1012539.3650.77−11.400.220.47
106337.4949.79−12.300.250.50
104548.7349.54−0.800.020.13
5400060.1458.281.850.030.18
5200083.0967.2015.890.240.49
5100074.9873.311.660.020.15
550033.7461.78−28.040.450.67
525059.9855.564.420.080.28
512556.2353.063.170.060.24
56352.4852.080.400.010.09
54552.4851.840.650.010.11
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Nikiema, M.; Barsan, N.; Ouili, A.S.; Mosnegutu, E.; Somda, K.M.; Maiga, Y.; Tidiane, C.C.O.; Ouattara, C.A.T.; Nedeff, V.; Ouattara, A.S. Determination of Particle Size for Optimum Biogas Production from Ouagadougou Municipal Organic Solid Waste. Sustainability 2024, 16, 9792. https://doi.org/10.3390/su16229792

AMA Style

Nikiema M, Barsan N, Ouili AS, Mosnegutu E, Somda KM, Maiga Y, Tidiane CCO, Ouattara CAT, Nedeff V, Ouattara AS. Determination of Particle Size for Optimum Biogas Production from Ouagadougou Municipal Organic Solid Waste. Sustainability. 2024; 16(22):9792. https://doi.org/10.3390/su16229792

Chicago/Turabian Style

Nikiema, Mahamadi, Narcis Barsan, Amidou S. Ouili, Emilian Mosnegutu, K. Marius Somda, Ynoussa Maiga, Compaoré Cheik Omar Tidiane, Cheik A. T. Ouattara, Valentin Nedeff, and Aboubakar S. Ouattara. 2024. "Determination of Particle Size for Optimum Biogas Production from Ouagadougou Municipal Organic Solid Waste" Sustainability 16, no. 22: 9792. https://doi.org/10.3390/su16229792

APA Style

Nikiema, M., Barsan, N., Ouili, A. S., Mosnegutu, E., Somda, K. M., Maiga, Y., Tidiane, C. C. O., Ouattara, C. A. T., Nedeff, V., & Ouattara, A. S. (2024). Determination of Particle Size for Optimum Biogas Production from Ouagadougou Municipal Organic Solid Waste. Sustainability, 16(22), 9792. https://doi.org/10.3390/su16229792

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