experimental biogas production

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An overview of biogas production from anaerobic digestion and the possibility of using sugarcane wastewater and municipal solid waste in a south african context.

1. Introduction

2. possibility of generating renewable energy from biogas using sugarcane processing wastewater, 3. municipal solid waste.

Click here to enlarge figure

4. Anaerobic Digestion Process

4.1. hydrolysis, 4.2. acidogenesis, 4.3. acetogenesis, 4.4. methanogenesis, 5. factors affecting the anaerobic digestion process and biogas production, 5.1. substrate type, 5.2. anaerobic digestion ph, 5.3. temperature, 5.4. organic loading rate, 5.5. hydraulic retention time (hrt), 5.6. effect of inoculation on ad process parameters, 5.7. co-digestion of two substrates, 6. microorganism selection, culturing, and inhibition, 7. types of digesters used, 8. discussion, 9. conclusions, author contributions, data availability statement, conflicts of interest.

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Tshemese, Z.; Deenadayalu, N.; Linganiso, L.Z.; Chetty, M. An Overview of Biogas Production from Anaerobic Digestion and the Possibility of Using Sugarcane Wastewater and Municipal Solid Waste in a South African Context. Appl. Syst. Innov. 2023 , 6 , 13. https://doi.org/10.3390/asi6010013

Tshemese Z, Deenadayalu N, Linganiso LZ, Chetty M. An Overview of Biogas Production from Anaerobic Digestion and the Possibility of Using Sugarcane Wastewater and Municipal Solid Waste in a South African Context. Applied System Innovation . 2023; 6(1):13. https://doi.org/10.3390/asi6010013

Tshemese, Zikhona, Nirmala Deenadayalu, Linda Zikhona Linganiso, and Maggie Chetty. 2023. "An Overview of Biogas Production from Anaerobic Digestion and the Possibility of Using Sugarcane Wastewater and Municipal Solid Waste in a South African Context" Applied System Innovation 6, no. 1: 13. https://doi.org/10.3390/asi6010013

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• Published: 06 July 2023

Optimization of biogas production from anaerobic co-digestion of fish waste and water hyacinth

• Hortence Ingabire 1 , 2 ,
• Milton M. M’arimi 3 ,
• Kirimi H. Kiriamiti 3 &
• Boniface Ntambara 1 , 2  

Biotechnology for Biofuels and Bioproducts volume  16 , Article number:  110 ( 2023 ) Cite this article

3298 Accesses

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Many fresh water bodies face a great challenge of an invasive weed called water hyacinth (WH) which has great impacts on the environment, ecology, and society. Food and Agriculture Organization (FAO) estimates that over nine million tons of Fish wastes (FW) are thrown away each year. The fish waste generated poses environmental and health hazards because in most cases it is either disposed into pits or discarded onto the open grounds. Both WH and FW are potential substrates for biogas production. However, utilization of FW substrate alone has a limitation of producing a lot of amounts of volatile fatty acids (VFAs) and ammonia. Their accumulation in the digester inhibits substrate digestion. Consequently, as stand-alone it is not suitable for anaerobic digestion (AD). This can be overcome by co-digestion with a substrate like WH which has high carbon to nitrogen (C/N) ratio prior to biodigestion. Experimental variable levels for biogas were substrate ratio (WH:FW, 25–75 g), inoculum concentration (IC, 5–15 g/250 mL), and dilution (85–95 mL). Design-Expert 13 was used for optimization and results analysis. Response surface methodology (RSM) was used to examine the effects of operating parameters and identify optimum values for biogas yield. Optimum values for maximum biogas with the highest methane yield of 68% were found to be WH:FW ratio, 25:75 g, 15 g of IC, and 95 mL for dilution. The yield was 16% and 32% greater than FW and WH mono-digestion, respectively. The biogas yield was expressed as a function of operating variables using a quadratic equation. The model was significant ( P  < 0.05). All factors had significant linear and quadratic effects on biogas while only the interaction effects of the two factors were significant. The coefficient of determination ( R 2 ) of 99.9% confirmed the good fit of the model with experimental variables.

Biogas is produced from various organic wastes and used as energy source worldwide. It aids in attaining sustainability by providing access to modern, clean energy that is inexpensive, and dependable and fights climate change and its effects by limiting emissions [ 1 , 2 , 3 ]. According to FAO, approximately 9.1 million tons of fish waste are thrown annually. Consequently, fish by-products are now a global problem to the long-term viability of fish aquaculture [ 4 ]. WH is one of the most invasive water weeds in the world that thrives in freshwater bodies and has spread to most nations, has detrimental impacts on the environment, the ecology, and society [ 5 , 6 , 7 , 8 , 9 ]. It creates mats that obstruct waterways, makes fishing impossible, limits water flow, degrades water quality by obstructing sunlight from penetrating the water and lowering oxygen levels in the water, wipes out aquatic life like fish, and significantly reduces biodiversity. The waste generated is either disposed into pits or discarded onto the open ground which result in environmental pollution and health hazards [ 10 , 11 , 12 , 13 , 14 ]. AD of FW and WH can be used to enhance biogas generation. WH has high cellulose, low lignin contents, and high C/N ratio while FW is rich in lipids, proteins and contains easily biodegradable organic matter [ 2 , 5 , 12 ]. When digested alone, FW produce a lot of ammonia and volatile fatty acids (VFAs). Their accumulation in the digester inhibits substrate digestion. This makes FW not a suitable substrate for biogas production through anaerobic digestion (AD). A plausible way to overcome the limitation is through co-digestion with WH which has a high C/N ratio. Furthermore, optimization of the operating variables is necessary to overcome the biogas inhibition of FW [ 5 , 6 ]. The application of co-digestion to balance the C/N ratio, improve gas and methane generation has been reported by previous studies [ 7 ]. The process enhances good synergy which encourages bacteria activity [ 11 ]. In addition to promoting co-digestion, the biogas yield can be improved by optimizing the process variables such as organic loading rate (OLR), inoculum concentration (IC), pH, dilution, carbon to nitrogen (C/N) ratio, substrate ratio, retention time, dilution, and temperature [ 6 ]. Nalinga and Legonda [ 15 ] demonstrated that anaerobic co-digestion of FW and WH feedstock increased the materials' digestibility and biogas yield. Tasnim et al. [ 16 ] conducted a study on anaerobic co-digestion of kitchen waste, cow manure, and WH. Their research revealed that kitchen waste combined with WH and cow manure was a source of biogas energy for both residential and commercial energy needs. Katima [ 8 ] studied biogas generation from WH by investigating the impact of substrate concentration (5 to 30 g/L), particle size (1–3 mm), and incubation period (1–6 days). The highest methane (72.53%) was generated within 5 days of incubation at a substrate concentration of 25 g/L and particle size less than 1 mm of WH. Usman [ 17 ] conducted a test on optimum biogas production from sugar cane and rice husk with the cellulolytic fungus by varying factors such as water, fungus concentration, and temperature. The optimum biogas of 500 cm 3 was produced at the optimal values of 25 cm 3 of water, 0.6 g of fungus, and a temperature of 33 ℃. Chanathaworn [ 18 ] has researched optimization conditions for biogas production from WH and earthworm bedding wastewater by varying particle size (0.3–1.5 cm), TS (4–12%), and pH. Optimum biogas of 35.50% was obtained at 8% of TS, 0.3 cm particle size, and 7.0 initial pH. Sandhu and Kaushal [ 19 ] applied the response surface technique to optimize the variables of co-digestion such as temperature, pH and concentration of wastes. It was also observed that the rate of biogas yield is greatly affected by many factors such as temperature and total solid concentration. There is no documentation in literature for the optimization of the anaerobic co-digestion of FW and WH. Some of the applications for biogas include; lighting, cooking, heating, etc. [ 19 , 20 ]. The FW and WH are produced in large volumes in many countries. They are affordable, available, sustainable, and renewable; consequently, the successful utilization of WH and FW for biogas generation can have a significant impact. The main goal of this research study was to determine the optimal conditions for optimizing the production of biogas in AD by evaluating the impacts of inoculum concentration (IC), substrate (WH:FW) ratio, and dilution (water content) on biogas production by design of Expert (DOE) using RSM approach.

Materials and methods

Substrate collection and preparation.

The water hyacinth used was sourced from Lake Victoria in Kisumu County. It was washed to remove unwanted impurities, cut into small pieces, and mashed using laboratory mortar to increase its biodegradability for microbial activity. Thereafter, they were put in a plastic collector and stored in a refrigerator for further use. The fish wastes (mostly fish intestines) used in this experiment were collected from the fish point, Eldoret, Kenya, and chopped into small pieces. The inoculum used in the experiment was freshly digested cow dung which was collected from the Moi University biogas plant, in Eldoret, Kenya as shown in Fig.  1 . Fresh bio-digested cow dung was used as an inoculum because it contains active bacteria.

Illustration of fish waste ( a ) and water hyacinth ( b ) as feedstocks for biogas production

Analytical methods

The FW, WH, and inoculum were analyzed for Moisture content (MC), Total solids (TS), Volatile solid (VS), Ash content, pH, and carbon to nitrogen (C/N) ratio using standard methods [ 12 , 13 , 14 , 18 , 21 ]. The characteristics of the feedstock are presented in Table 1 .

Operating procedure

Conical flasks of 250 mL were used for batch digestion tests of biogas generation. According to the experimental plan, the substrates were fed into the reactor at varied ratios of IC (1.6–18.4 g/250 mL), substrate ratio (8–92 g), and dilution (81.6–98.4 mL). The biogas production set up is shown in Fig.  2 .

Illustration of the experimental setup

The co-digestion was quantified by substrate ratio (based on 100 g). Biogas production was measured by water displacement method as illustrated in Fig.  3 . The entire investigation was conducted at mesophilic temperature (37 °C). A gas detector was used to measure the methane content from the gas sampling bags.

Overview of biogas production setup

Experimental design and optimization

Design Expert 13 software which contains Central Composite Design (CCD), Analysis of Variance (ANOVA), and Response surface Methodology (RSM) was used for optimization. The CCD was used to determine the level of variable inputs and establish the optimum number of experimental runs, ANOVA was used for the analysis of the regression coefficient and the prediction equation, and to show how the variables interacted and RSM was used to examine the relationship or interaction between variables and the response and to estimate the optimum surface area of optimal values of the response. The polynomial equation was illustrated in 2D (two-dimensional) contour plots and 3D (three-dimensional) using response surface plots. Three factors: ( X 1 : Substrate ratio (WH:FW, 25–75 g), X 2 : Inoculum concentration (IC, 5–15 g/250 mL), and X 3 : Dilution (85–95 mL)) were investigated. The experimental design levels and anaerobic digestion parameters are shown in Table 2 . According to the experimental design, 17 runs were carried out. The anaerobic digesters were set up in triplicates for each treatment, and the findings were presented as means. Each factor was coded at five distinct levels and given the letters − α, − 1, 0, + 1, and + α as shown in Table 2 . Biogas yield was used as the response of the experiment. The effectiveness of the second-order polynomial equation fit was expressed using the coefficient of determination ( R 2 ). Model terms were assessed using P -value [ 18 , 20 , 22 , 23 ].

Results and discussion

Statistical analysis and model fitting.

The CCD of experimental variables in the actual and coded values and experimental results of the biogas yield is shown in Table 3 . The analysis of variance (ANOVA) is shown in Table 4 . All linear terms ( X 1 , X 2 , X 3 ) and quadratic terms ( X 1 2 , X 2 2 , X 3 2 ) for all factors significantly affected the biogas yield because the P -value is less than 0.05 ( P  < 0.05) as shown in Table 4 . The interaction between substrate ratio and IC ( X 1 X 2 ), and substrate ratio and dilution ( X 1 X 3 ) also significantly affected the biogas yield, while only the interaction between IC and dilution ( X 2 X 3 ) insignificantly affected the biogas yield because the P -value is greater than 0.05 ( P  > 0.05) as shown in Table 4 . The model equation (Eq 1 ) was obtained based on multiple regression analysis for biogas production, and yielded the following quadratic model:

where Y : estimated Biogas Yield (response), X 1 : Substrate (WH: FW) ratio, X 2 :IC, and X 3 :Dilution. The model was significant ( P  < 0.05), this means that the quadratic model equation significantly affected the biogas yield. The lack of fit was insignificant ( P  > 0.05), this implies that the quadratic model significantly predicted the biogas yield. The coefficient of determination ( R 2 ) of 99.9% confirmed the good fit of the model with experimental variables.

A strong model fit is indicated by an R 2 value between 0.75 and 1.0 for a good statistical model. The quadratic equation could be used to obtain a precise estimate for biogas production because of the high value of R 2 . According to Chanathaworn [ 18 ], the adjusted R 2 of 0.9978 indicated that the response surface model created for this biogas study prediction was completely appropriate. A value greater than 4 is desirable for the "Adequate precision," which measures the signal-to-noise ratio, the ratio of 79.8627 from this study indicated an adequate signal. The Predicted R 2 value of 0.9928 showed a good agreement between the predicted and observed values. The low coefficient of variation (CV) of 2.20 showed the high reliability and precision of experimental outcomes. The trustworthiness of experimental results decreases with an increased coefficient of variance (CV) [ 20 , 23 ].

A strong model fit is indicated by an R 2 value between 0.75 and 1.0 for a good statistical model. The quadratic equation could be used to obtain a precise estimate for biogas production because of the high value of R 2 [ 24 ]. According to Chanathaworn [ 19 ], the adjusted R 2 of 0.9978 indicated that the response surface model created for this study’s biogas prediction was completely appropriate. A value greater than 4 is desirable for the "Adequate precision," which measures the signal-to-noise ratio, the ratio of 79.8627 from this study indicated an adequate signal. The Predicted R 2 value of 0.9928 showed a good agreement between the predicted and observed values. The low coefficient of variation (CV) of 2.20 showed the high reliability and precision of experimental outcomes. The trustworthiness of experimental results decreases with an increased coefficient of variance (CV) [ 20 , 21 ]. The experimental biogas production results were close to the predicted results as shown in Fig.  4 .

Plot of predicted response vs. actual value from response surface

Analysis of response surfaces

The 2D (two-dimensional) contour and 3D (three-dimensional) response surfaces plots for biogas production optimization were represented using the polynomial model Eq.  1 to show the interaction effect of biogas production variables on the biogas yield. Figures  5 , 6 , 7 , 8 , 9 and 10 all display the 3D and 2D surface response plots.

Effect of substrate ratio and IC on biogas production for response surface

Effect of substrate ratio and IC on biogas production for contour plot

Effect of substrate ratio and dilution on biogas production for response surface

Effect of substrate ratio and dilution on biogas production for contour plot

Effect of IC and dilution on the production of biogas for response surface

Effect of IC and dilution on the production of biogas for contour plot

Effect of substrate ratio and IC on the production of biogas

Effect of substrate ratio and inoculum concentration on biogas produced were determined as shown in Figs.  5 and 6 . Biogas yield increased to its maximum when the IC increased. However, biogas production decreased when the substrate ratio increased as shown in Figs.  5 and 6 . ANOVA showed that the interaction effect between substrate ratio and IC on biogas production was significant ( P  > 0.05) as shown in Table 4 . Biogas production increased when the substrate ratio (WH:FW) was 25:75 g, however, when the substrate ratio (WH:FW) exceeds 25:75 g, respectively the biogas production decreased rapidly as shown in Fig.  4 and also when the substrate ratio (WH:FW) was less than 25:75 g, respectively a slight or very little inhibition was observed on the response surface plot. This might be explained by the presence of an insufficient amount of methanogens. This inhibition was due to the formation of intermediate products which are inappropriate for conversion by methanogenic bacteria to biogas and when there is overloading, the production of organic acids increases quickly, then inhibition of methanogens activity [ 11 ]. Similar findings were reported by Shen et al., Jnr et al., Labatut et al, Rabii et al., [ 25 , 26 , 27 , 28 ], overloading caused the microbial activity to be inhibited, which decreased the rate of biogas generation. The optimum biogas production of 690 mL with the methane content of 68.15% was obtained at 25:75 g of substrate ratio (WH:FW) when IC and dilution were 15 g and 95 mL, respectively as shown in Figs.  5 and 6 .

Effect of substrate ratio and dilution on biogas production

The relationship between substrate ratio and dilution in biogas production is shown in Figs.  7 and 8 .The results revealed that the interactive effect of substrate ratio and dilution on biogas production is significant ( P  < 0.05) as shown in Table 4 . The biogas production increased as dilution increased and decreased when the substrate ratio increased. This is because a higher ratio of the substrate may cause an acidic environment in the AD system, which leads to methanogenesis inhibition, thus decreasing biogas production [ 25 , 26 , 27 , 28 ]. The optimum dilution for biogas production was 95 mL which obtained at substrate ratio (WH:FW) of 25:75 g and IC of 15 g.

Effect of IC and dilution on the production of biogas

The interaction effect of IC and dilution on biogas yield was insignificant ( P  > 0.05) as shown in Table 4 . However, ANOVA indicated that the quadratic and linear terms of IC and dilution were significant ( P  < 0.05). Figures  9 and 10 show the relationship between IC and dilution in biogas generation. Biogas production was higher when IC was 11 g, however, when IC was less than 11 g the biogas production decreased rapidly, and also when IC exceeds 11 g a slight inhibition was observed as shown in Fig.  9 . Biogas production was higher when IC was 11 g, however, when IC was less than 11 g the biogas production decreased rapidly, and also when IC exceeds 11 g a slight inhibition was observed as shown in Fig.  9 . The biogas yield was very low when the IC was 1.5% and 18.4%. This might be explained by the presence of an insufficient amount of methanogens. Similar observations were reported by Dar and Phutela [ 14 ], at lower IC, there aren't enough bacteria present to start the methanogenesis. The results agree with Filer et al. and Girmaye et al. [ 22 ], the low inoculum concentration in the reactor could result in the microorganisms' low metabolic activity which leads to inhibition of the methanogenesis process resulting in low biogas yield. It was noted that the generation of biogas was slightly reduced as a result of the significant increase in IC (18.4%). This might have happened as a result of modifications to the substrates characteristics, which may have had an impact on the bioavailability during hydrolysis [ 11 ]. The addition of the required IC in the AD process is very important as it will enhance biogas yield and methane content, speed up the process, and improve the stability of anaerobic digestion [ 11 , 26 ].

Conclusion and future works

The biogas yield was expressed as function of operating variables using a quadratic equation. The model was significant ( P  < 0.05). All factors had significant linear and quadratic effects on biogas while only the interaction effects of the two factors were significant. The coefficient of determination ( R 2 ) of 99.9% confirms the good fit of the model with experimental variables. Optimum values for RSM were within the range of experimental results. Biogas yield decreased as substrate ratio increased. According to the high value of R 2 , the model could be effectively utilized for the prediction of biogas generation from anaerobic co-digestion of FW and WH.

Further research works

FW had a lower C/N ratio, further study needs to consider co-digestion with other higher C/N ratio substrates. Because the CO 2 is hazardous to humans and corrodes motors and pipes, its removal is crucial. The biogas was not upgraded, research is still needed in purifying or upgrading the biogas for CO 2 removal and improved methane content to be used directly for cooking or as fuel for vehicle.

Availability of data and materials

The data sets supporting the conclusions are included in this article. This paper also comprises all obligatory evidences. Upon demand, the corresponding author will deliver any supplementary data.

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Acknowledgements

The authors are thankful to Moi University for the use of the Laboratory facilities during this research. The World Bank and the African Center of Excellence in Phytochemical, Textile, and Renewable Energy (ACE II PTRE) are acknowledged for sponsoring the study.

This work was funded by the World Bank Group through African Centre of Excellence in Phytochemicals, Textile and Renewable Energy (ACE II PTRE) under Moi University, P. O BOX 3900-30100 Eldoret-Kenya.

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Ingabire, H., M’arimi, M.M., Kiriamiti, K.H. et al. Optimization of biogas production from anaerobic co-digestion of fish waste and water hyacinth. Biotechnol Biofuels 16 , 110 (2023). https://doi.org/10.1186/s13068-023-02360-w

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Experimental investigation of biogas production by co-digestion of local vegetable market wastes

• Original Article
• Published: 27 February 2024

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• Saji Raveendran Padmavathy 1 ,
• Rajendran Prabakaran 2 ,
• Murugan Paradesi Chockalingam 8 ,
• Godwin Glivin 3 ,
• Joseph Sekhar Santhappan 4 ,
• Binoj Joseph Selvi 5 ,
• Panith Malai Sekar 1 ,
• Nithyanandhan Kamaraj 8 ,
• Sung Chul Kim 2 ,
• Saravanan Pandiaraj 6 &
• Salim Manoharadas 7  

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The excessive production of vegetable waste near the vegetable market poses a significant threat to the environment. The cost associated with collecting, gathering, and transporting these wastes for disposal over long distances has become prohibitively high. To address this issue, one feasible approach is to utilize anaerobic digestion of biodegradable solid waste, such as cow dung, poultry waste, food waste, and vegetable dumping, to produce methane gas as an environmentally friendly energy source. This study aims to assess the viability of biogas production from vegetable waste in the Perundurai market, located in Erode, India. The experiment was conducted using a lab-scale reactor (0.75 L) equipped with appropriate feeding, gas collection, and residue drainage mechanisms. Biogas generation from the reactors was monitored daily using the water displacement method. For a period of 45 days, the digester setup was fed a mixture of cow dung and vegetable waste with different mixing ratios of 1:1 (R1), 1.5:1 (R2), and 2:1 (R3), at a mesophilic temperature of 35 °C. The results indicated that the highest biogas yield was achieved with the R3 samples, which were approximately 3.9 and 3.0 times higher than those of R1 and R2, respectively. Similarly, the peak methane levels were found to be 66%, 58%, and 53% for R3, R2, and R1, respectively. Moreover, the degradation rate was significantly better for R3 (3.32) compared to R2 (2.93) and R1 (2.24). Based on these findings, it can be concluded that co-digestion of the considered mixtures (vegetable waste + cow dung) in a mesophilic environment could be a promising strategy to enhance biogas production while maintaining nutrient balance and digester stability. The generated biogas can be utilized for various applications, including heating, electricity generation, and as fuel for internal combustion engines, by employing a suitable biogas plant.

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The authors acknowledge the Researchers Supporting Project for funding this work through Researchers Supporting Project number (RSPD2024R708), King Saud University, Riyadh, Saudi Arabia.

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Department of Mechanical Engineering, Builders Engineering College, Nathakadaiyur, Kangayam, Tirupur, Tamil Nadu, 638 108, India

Saji Raveendran Padmavathy & Panith Malai Sekar

School of Mechanical Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk, 712-749, South Korea

Rajendran Prabakaran & Sung Chul Kim

Department of Energy and Environment, National Institute of Technology, Tiruchirappalli, 620 015, Tamil Nadu, India

Godwin Glivin

Department of Engineering, University of Technology and Applied Sciences-Shinas, Al-Aqar PC 324, Shinas, Oman

Joseph Sekhar Santhappan

Institute of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, 602105, Tamil Nadu, India

Binoj Joseph Selvi

Department of Self-Development Skills, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia

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Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box 2454, Riyadh, Saudi Arabia

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Conceptualization, PSR and RP; methodology and experimentation, PSR, PMS, GG, BJS, and NK; formal analysis, MPC, JSS, SP, and SM; writing—original draft preparation, PSR and RP; writing—review and editing, MPC, JSS, SCK, SP, and SM; lead; PSR and RP.

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Correspondence to Saji Raveendran Padmavathy , Rajendran Prabakaran , Sung Chul Kim or Saravanan Pandiaraj .

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Padmavathy, S.R., Prabakaran, R., Chockalingam, M.P. et al. Experimental investigation of biogas production by co-digestion of local vegetable market wastes. Biomass Conv. Bioref. (2024). https://doi.org/10.1007/s13399-024-05447-y

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Received : 05 April 2023

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Published : 27 February 2024

DOI : https://doi.org/10.1007/s13399-024-05447-y

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Techno-economic evaluation of biogas production from food waste via anaerobic digestion

• Abeer Al-Wahaibi 1 ,
• Ahmed I. Osman 2 ,
• Ala’a H. Al-Muhtaseb 1 ,
• Othman Alqaisi 3 ,
• Mahad Baawain 4 ,
• Samer Fawzy 2 &
• David W. Rooney 2  

Scientific Reports volume  10 , Article number:  15719 ( 2020 ) Cite this article

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• Energy science and technology
• Engineering
• Environmental sciences

Food waste is a major constituent in municipal solid wastes and its accumulation or disposal of in landfills is problematic, causing environmental issues. Herein, a techno-economic study is carried out on the potential of biogas production from different types of food waste generated locally. The biogas production tests were at two-time sets; 24-h and 21-day intervals and results showed a good correlation between those two-time sets. Thus, we propose to use the 24-h time set to evaluate feedstock fermentation capacity that is intended for longer periods. Our approach could potentially be applied within industry as the 24-h test can give a good indication of the potential substrate gas production as a quick test that saves time, with minimal effort required. Furthermore, polynomial models were used to predict the production of total gas and methane during the fermentation periods, which showed good matching between the theoretical and practical values with a coefficient of determination R 2  = 0.99. At day 21, the accumulative gas production value from mixed food waste samples was 1550 mL per 1 g of dry matter. An economic evaluation was conducted and showed that the case study breaks-even at $0.2944 per cubic metre. Any prices above this rate yield a positive net present value (NPV); at $0.39/m 3 a discounted payback period of six years and a positive NPV of $3108 were calculated. If waste management fee savings are to be incorporated, the total savings would be higher, increasing annual cash flows and enhancing financial results. This economic evaluation serves as a preliminary guide to assess the economic feasibility based on the fluctuating value of methane when producing biogas from food waste via anaerobic digestion, thus could help biogas project developers investigate similar scale scenarios .

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Introduction.

The high population growth rate and uncontrolled urbanization have created critical problems of solid waste disposal. A study performed by Baawain et al. 1 confirmed that food waste is usually a major portion of any municipal solid wastes (MSWs) which are commonly disposed of in landfills or dumping sites, causing environmental problems. However, landfilling is expensive, requires space and can have a negative environmental impact if not well managed due to the production of leachate, methane and carbon dioxide and other nuisances like flies, odour, and vermin like birds and rodents. Leachate could also pollute underground water and soil along with the release of methane which is a potent greenhouse gas with a short-term global warming potential that is 84 times more powerful than carbon dioxide 2 , 3 , 4 . On the other hand, using food waste as a potential source for the production of sustainable fuels will complete the full cycle of this waste stream sustainably and thus, directly support and facilitate the concept of the circular economy in the form of open-loop recycling 5 , 6 , 7 , 8 . One of the promising ways of dealing with such waste stream is through processing via anaerobic digestion (AD) to produce biogas 9 , 10 . The AD is a series of processes in which microorganisms break down biodegradable material in the absence of oxygen for industrial or domestic purposes to manage waste and/or to release energy. Biogas is mainly composed of methane and carbon dioxide, with trace elements of gases such as hydrogen sulfides, ammonia and water vapour. There are several possible uses of biogas such as in cooking, heating, electricity generation, etc. The establishment of sustainable waste management practices that are effective, affordable, promote health and safety benefits to the public, prevent soil, air and water contamination, conserve natural resources, and provide renewable sources of energy that are generally environment friendly must be the priority.

The microbial population and type of microbes play a significant role in AD and affect the composition of biogas, which is produced due to four groups of microorganisms, fermentative, syntrophic, acetogenic and methanogenic bacteria 11 , 12 , 13 . These microorganisms normally occur in a natural environment and play different roles in the process of waste anaerobic degradation. Different microorganism types have different suitable environmental conditions to survive. The mesophilic bacteria is a type of organism that grows in a moderate temperature range of 20–45 °C with an optimum temperature of 35 °C 14 . On the other hand, thermophilic bacteria is a type of organism that optimally grows and survives in relatively hot temperatures (temperature range 41–122 °C), while the typical thermophilic condition is between 50 and 65 °C and 55 °C is optimum 15 . Microorganisms have a critical role in the degradation of organic substances, and it plays an important role in the anaerobic degradation process 16 . The volumetric amount of biogas produced in different digesters throughout the digestion time showed that mesophilic AD is more stable than thermophilic digestion 17 . Also, it required less process heat and hence less operating cost. However, thermophilic digestion allows a higher amount of feed loading with lower retention time, due to its higher conversion efficiency. The disadvantages of thermophilic AD are the degradation of enzymes and deficiency of the elements which are caused by the high temperature.

The efficiency of AD in biogas production is highly dependent on the process of biodegradation, where operating at an optimum condition increases the process efficiency 18 . There are important factors that influence biogas production such as temperature, hydraulic retention time, organic loading rate, inoculum, pre-treatment, feeding pattern and pH which play a major role in the AD process. Where pH influences the microorganisms’ growth through the process. Each bacteria type has a specific range of pH where it becomes active. For example, a suitable pH for methanogens bacteria is more than 6.5, however, the optimum pH value for acidogenic bacteria is in the range of 5–6.5. Sitorusa et al. 19 performed a study on AD of mixed fruit and vegetable wastes to investigate the amount of biogas produced and variation of temperature and pH throughout the process. The experiments took place on 160 kg feedstock for 15 weeks. The fluctuation of temperature and pH during the process in the digester showed that the digestion process is running mostly at the mesophilic condition. There is a slight increase in the temperature of the digester during the first two weeks of the process from 28 up to 32 °C. After that, it increased sharply and reached the highest value in week 5 (~ 46 °C), this may have happened due to an ambient condition or due to the activity of the microbes. Then, for the following weeks, it dropped to the range of 32–37 °C, thus the temperature variation should be controlled so that it does not exceed a certain range. The reduction of pH in the AD process after the first three weeks can be explained mostly due to the formation of a high concentration of fatty acids in the digester and hence the accumulation of acid. At this stage, acidogenic bacteria start working and produce organic acid which leads to a decrease in the pH of the digester. After 9 weeks, the methanogens phase starts in the digester where the methanogens consume the acids. In general, controlling the pH inside the anaerobic reactor is not easy; hence, a basic solution may be required to be injected to maintain the pH within the optimum range (Sitorusa et al. 19 ).

The substrate composition is crucial in the AD process, where the degradability of the feedstock varies and hence the optimum condition varies with the diversity of the composition in the feedstock 20 , 21 . Generally, the concentration of lipids, proteins and carbohydrates in the substrate gives a general idea about its behaviour in the AD process. Carbohydrates, due to its high degradability and rapid transformation, resulted in higher biogas yield 22 . However, although lipids give biogas with higher quality, they have a lower biodegradability rate, thus a longer residence time in the AD process is required. Li et al. 18 studied twelve different types of food waste samples to investigate the effect of proteins, carbohydrates and lipids on AD and the amount of methane produced. Different food waste possesses different organic compositions which leads to a change in the methane yield. There was a remarkable difference between the peak pattern of methane production within samples, where 73 wt% of the carbohydrate composition showed the peak value of the methane yield within the first 11 h. At that peak, up to 98 wt% of the total methane was produced and this was explained since carbohydrates are a rapidly and easily degradable substance. On the other hand, the time taken to reach the peak methane production for the samples with higher protein and lipids and lower carbohydrates was in a range between 196 and 409 h, this is mainly due to a lower hydrolysis rate of proteins and lipids compared to carbohydrates. As an effective biological pre-treatment, rumen fluid was utilised which increased the biogas production by 66.5%, where the optimum time achieved was at 24 h 23 . This is in agreement with the work of Cattani et al. 24 . Furthermore, Kulivand and Kafilzadeh reported that 80% of gas production was achieved within 21 h using the rumen fluid 25 . Li et al. 26 reported an optimum methane production of 14.9 mL after around 72 h, and Bachmann et al. 27 reported that the optimum gas production for beans samples was at around 16 h. Dagaew et al. 28 performed 96 h of biogas production in the AD process and found out that the optimum gas production was achieved at around 24 h.

The retention time in an anaerobic digester is specified based on the feedstock 29 , 30 . The biogas production varies throughout the digestion period 31 . This is mainly due to the variation of the pH which is a result of acid concentration increase/decrease. A digestion experiment ran for 10 days by Ziauddin and Rajesh 32 on mixed kitchen waste to investigate the variation of biogas production from the first day to the last day of the experiment’s period. The biogas production sharply increased during the first 3 days from 80 to 120 mL from the first to the third day. After that, due to acid production and hence the increase in the acid concentration, pH was reduced in the process medium. As a result, biogas production declined to 50 mL and then increased but never reached the maximum value of 120 mL that was previously reached after 3 days of production. Apart from food waste, cow dung also contains biodegradable materials that can be converted to biofuel, yet gas yield from cow dung is low due to the low organic load and high nitrogen concentration 33 . However, food waste with its high nutrient content is a promising source for producing bioenergy 34 , 35 .

Herein, we investigated biogas production potential from food waste that is produced locally within an Omani oil company (Fahud cluster in Petroleum Development Oman) as a case study for the waste management. The study included various samples of food waste; two types of mixed food wastes (fruit and vegetable waste), bread waste, potato peels waste, meat waste, rice waste, dates fruit, legume beans, leafy vegetables and fish waste. Firstly, we measured the dry matter and nutrient composition in each food waste sample. Then we investigated the invitro true digestibility gas production to calculate the amount of gas produced from each sample. Furthermore, methane gas determination was carried out using DAISY incubator and gas chromatography. Finally, we performed a techno-economic study for biogas production derived from food waste provided in the case study. The utilisation of such waste stream in the production of sustainable biogas fuel will aid in the upcycling of problematic food waste by adding value and other techno-economic potential routes for application in the energy sector.

Results and discussion

Characterization results.

Water content in food waste varies widely depending on the food source, and in some cases it reaches 75% 36 . Thus measuring the moisture content of each food waste sample is crucial when calculating the total quantities produced and the nutrient content in each sample 37 . Table 1 shows the dry matter content for the samples studied herein. The highest moisture content and hence the lowest dry matter present was the fruit and vegetable food waste sample, showing 80.4 and 19.6 wt%, respectively. The highest dry matter 81.7 wt% was observed in the date fruit sample, followed by bread waste sample with 77.2 wt% dry matter. The mixed food waste samples and potato peel sample contained a similar amount of dry matter of (± 25 wt%). Approximately 43 wt% of the meat waste sample was the dry matter, while for legume beans, rice, leafy vegetables, cow dung and fish waste (arranged in order from highest to lowest) were in the range between ~ 30 and 40 wt% dry matter.

To establish a relationship between biogas production performance and biochemical components, proximate analysis was performed for the samples studied herein as shown in Table 1 . The analysis of the samples was performed on a dry basis and in duplicate for data reproducibility. The biogas formation rate increased with the increase in fibre content along with the decrease in fat content within the sample. Fats are considered as complex compounds that lead to an overall decrease in the biodegradation rate for the feedstock. For instance, the date fruits sample showed relatively higher fibre content along with the lowest fat content among the studied samples with values of 2.08 wt% and 0.16 wt%, respectively. Besides, the sample with the highest fat content had the lowest potential for biogas production, which was due to the complexity of the fat compound that required more residence time for degradation and biogas formation (i.e. fish waste). Furthermore, the sample with the highest protein content had a lower potential for biogas production. This is a result of the ammonia released during the degradation of protein, which caused an increase in pH and decreased the biodegradation rate by inhibiting the microorganisms in the AD system (i.e. meat sample). To sum up, for a better biogas production rate, a combination of various factors, such as high fibre content, high carbohydrate, low fats and protein contents, is required. Consequently, samples of date fruit, rice waste, legume beans and mixed food waste samples possessed a greater potential for high biogas production rate.

Biogas production (24 h-time intervals)

The total gas production from each sample was recorded in 3 h intervals for 24 h as shown in Figs.  1 and 2 . The mixed food-1 sample showed a sharp increase in the gas produced during the first 3 h (61 mL/1 g DM), followed by a slight increase after 6 h (88 mL/1 g DM), then a sharp increase after 12 h (127 mL/1 g DM). Then up to 24 h, the increasing rate was almost stable, with total gas production of 157 mL/1 g DM. Similarly, the fruit and vegetable sample showed a sharp increase in the first 3 h up to 89 mL of gas per 1 g of dry matter of the sample. Then, from 3 to 24 h, the gas production rate slightly increased at a stable rate with maximum gas production of 166 mL of gas per 1 g of dry matter of the sample, similar to the conclusion obtained from Deressa et al. 38 . Regarding bread waste samples, as discussed earlier, a high dry matter content was noted and hence a high organic matter content. In general, bread waste contains a high amount of sugars, fibres and fats. Such organic-rich waste materials are a promising substrate to be used in the AD process with a high potential for biogas production. The bread sample gas production profile is shown in Fig.  1 . The gas production rate slightly increased during the first 3 h (41 mL/1 g DM), then, sharply increased between 3 and 12 h to reach an approximate amount of 202 mL of gas per 1 g of dry matter. After 12 h, the gas production rate showed a slight increase up to 256 mL at 24 h.

The gas production profile for ( a ) mixed food-1, ( b ) fruit and vegetables, ( c ) bread, ( d ) potato peel, ( e ) mixed food-2 and ( f ) meat samples over 24 h period.

The gas production profiles for ( a ) rice, ( b ) cow dung, ( c ) date fruit, ( d ) legume beans, ( e ) leafy vegetables and ( f ) fish waste samples over 24 h period.

Potato peel waste is rich in carbohydrates and hence is easily biodegradable 39 . Figure  1 shows a slight increase in the gas production profile at the first 6 h (70 mL/1 g DM), followed by a sharp increase in gas production up to 24 h with a maximum of 201 mL/1 g of dry matter. The results herein agree with the recent publication on potato peel waste that produced 217 mL/g 40 , where the physicochemical properties of potato peels indicated that it has a high potential for biogas production through AD 40 . The mixed food-2 sample showed almost a proportional relationship between retention time and gas production rate. The gas production volume steadily increased to reach 190 mL/1 g DM. Cattani et al. 24 used similar technology and reported that food waste produced 168 mL/1 g DM of biogas. The slight difference in results may be due to the difference in the nutrients contained in each feedstock (i.e. different types of mixed food). Due to its high protein content, the meat waste can adversely affect the AD process by inhibiting the microbes through the production of ammonia which results in the digestion process. Figure  1 shows that the gas production rate slowly increased during the first 3 h (25 mL/1 g DM). Then, a slow production rate up to 24 h is noted with a maximum gas volume of 83 mL/1 g DM. It is not surprising that the meat sample showed poor gas production as it requires a long retention time, which could reach 80 days 41 , where during the first 10 days, slow biogas production is observed. This behaviour is due to the high protein content in the meat, which requires more time for degradation. Furthermore, carbohydrate content, which is the main source for energy supply for fermenting microbes, was the lowest in meat and fish samples, which may in part explain the low gas production in these two samples. Similar to protein meat content evaluated herein, Alqaisi et al. 42 found a low gas yield for plant-rich protein sesame meal (i.e. crude protein content = 37.7%) compared to a significantly greater gas yield in rich carbohydrate feeds such as potato peels.

Figure  2 shows the gas production profile of the rice sample with a slight increase in the first 6 h (109 mL/1 g DM), followed by a sharp increase between 6 and 24 h, with total gas production of 421 mL per 1 g of dry matter of the sample. Therefore, herein the results showed that rice waste (cooked rice) could be considered as a promising source for biogas production. The above results are similar to Glivin and Sekhar 43 results, where a comparison between the feasibility of biogas production between rice and vegetable waste showed that higher biogas was produced from rice waste compared to vegetable waste. That, as stated above, is due to the higher carbohydrate content within the rice waste. The gas production from cow dung shows a similar production to the mixed food sample, which is, in part, attributed to the similarity in organic matter content. Figure  2 shows that the gas production volume slightly increased and is stable in the first 6 h of the incubation process. Thereafter, gas production sharply increased in the period between 6 and 24 h (203 mL/1 g DM). Numerous studies were conducted on cow manure to investigate the biogas production rate. Putria et al. 44 conducted an AD experiment on cow dung where 200 mL/g of biogas was produced in 24 h, which agrees with the results obtained in the current study. Ziauddin and Rajesh 32 performed a study to compare the amount of biogas derived from food waste and cow dung. Two sets of samples were collected, set-1 contained cow dung and set-2 contained kitchen waste, where AD experiments were conducted on both samples for 8 days. The study revealed that food waste produced more gas than cow dung during eight days with average values of 89.37 and 23.75 mL, respectively. Another research was conducted by Chibueze et al. 45 to evaluate the efficiency of biogas production from cow dung versus food waste and the same conclusion was obtained. The AD experiment was performed for 15 days on 150 g of two samples (cow dung and food waste feedstock). The results showed that after 15 days, 19.2 mL of biogas was produced from cow dung digestion, however, 30.58 mL was produced from food waste digestion. The result was predicted due to two factors: firstly, the nutrients contained in food waste are greater than those in cow dung. As per proximate analysis results, the cow dung contains less carbohydrate than that of food waste with values of 20 wt% and 61.9 wt%, respectively. Secondly, due to pH variation during the AD process in each sample. As pH is an important factor that affects digestion efficiency, pH values were measured throughout the process. It was observed that pH decreased more rapidly for the cow dung sample due to production of acids (ex. fatty and amino acids) which was a result of the high protein content, so became more acidic at the fourth day of the experiment. On the other hand, this took place on the 12th day for food waste sample (lower protein content). The acidity leads to depression in pH and hence reduces the efficiency of the anaerobic digestion. Thus, gas production volumes (mL) of food waste and cow dung after 15 days of continuous production were 30.58 and 19.2 mL, respectively.

The date fruits are cellulosic compounds which mainly contain sugars with a minor contribution from minerals and fats. Hence, it has considerable potential for biogas or biofuel production through the AD process, where date fruits produced double the amount of gas produced from cow dung. The gas production profile of the date fruits sample showed a sharp increase during the first 3 h (153 mL/1 g DM). Then, between 3 and 12 h, it increased to double the amount of gas (307 mL), then at 24 h, the total gas production was 386 mL per 1 g of dry matter of the sample.

Regarding legume beans, due to its high organic content, it can be used as a substrate for biogas production. Herein, a mixture of different types of legume beans (i.e. lentils, broad beans, chickpeas and beans) has been used. Figure  2 shows a sharp increase in the gas production rate at the beginning of the digestion process (between 0 and 3 h). Then, a slight increase was observed from 3 to 6 h. Beyond the 6 h retention time, the gas production rate sharply increased (from 122 mL at 6 h up to 343 mL at 24 h). Whereas for leafy vegetables, a mixture of several types of leafy vegetables which were commonly produced domestically (i.e. lettuce, coriander, mint and bay leaf) was used as feedstock for the AD experiment to explore their potential for biogas production 46 . The leafy vegetables gas production profile shows a slow increase in gas volume during the first 3 h (42 mL/1 g DM). Then, between 3 and 24 h, gas production increased almost proportionally and slightly with retention time, with a maximum amount of gas of 104 mL/1 g DM. Finally, the fish waste sample showed a slow increase in gas production at the beginning of the AD process (44 mL of gas produced up to 3 h). Then, gas production slightly increased to reach 76 mL after 12 h. After that, no change was observed in the amount of gas between 12 and 24 h. Kafle and Hun Kim 47 performed an AD experiment on fish waste, but with a longer retention time (60 days). The results showed a similar trend of higher gas productivity at the beginning of anaerobic digestion, then a slowdown in biogas production rate.

Interestingly, the samples with similar fat contents behaved similarly with regards to biogas production volume during the first 3 h. For instance, samples of fruits and vegetables and legume beans showed gas production of 89 and 91 mL/1 g DM, respectively. While samples of meat and cow dung with fat contents of 0.51 and 0.53 wt% showed gas production of 25 and 27 mL/1 g DM, respectively. The results herein agree with Li et al. 18 study where the effect of carbohydrate, lipids and protein on AD was investigated. They reported that the feed with the highest carbohydrate content resulted in a higher biodegradation rate. On the other hand, the lipid content required more residence time to degrade because of its complex structure. Figure  3 shows the comparison between the samples studied herein in terms of the total gases along with the methane gas produced within the 24 h test. It is obvious that the highest three biogas production rates were for rice, date fruit and legume beans with total gas production of 421, 386 and 343 mL/1 g DM, respectively as seen in Fig.  3 a. Methane gas production for those samples showed 17, 13 and 8 mL/1 g DM, respectively as shown in Fig.  3 b. Based on their composition that favours biogas production and local availability, we decided to run biogas production tests for a longer period (21 days) for those samples (rice, date fruit and legume beans) along with mixed food waste as an abundant waste material in this case study.

The total gas production profiles ( a , b ) the methane gas production over the waste samples studied herein, both for 24 h incubation period.

Biogas production and methane concentration (21 days-time intervals)

The biogas production along with methane concentration over 21 days is shown in Fig.  4 a–d for the selected samples (the date fruit, rice waste, legume beans and the mixed food waste). Overall, in each sample, there was a daily increase in the accumulated produced biogas. At day 21, the highest gas production values from the rice waste and mixed food waste samples were of ~ 1600 and 1550 mL/1 g DM, respectively. Figure  4 a–d show fluctuation in the methane concentration in all samples. The fluctuation in methane concentration was attributed to the large fluctuation in the levels of methanogenic population bacteria, as volatile fatty acids were accumulated and then subsequently consumed. Similar performance (i.e. fluctuation in methane concentration) was reported by Griffin et al. 48 . The study focused on analysing the performance of a mesophilic anaerobic digester where they observed a variation in the amount of produced biogas and methane concentration throughout the digestion process. Different digestion processes took place in the incubation period which resulted in a change in the pH of the incubator environment. The microorganisms’ growth through the AD process is influenced by the variation in the pH value. Each bacteria type (i.e. methanogenic) has a specific range of pH to be active and that is reflected by the fluctuation in methane concentration in each sample. Furthermore, the temperature fluctuation in the digester affects the type/population of the microorganisms. Normally, there were two sources of energy (temperature change) inside the incubator, the surrounding condition and the microorganism’s activity. On the other hand, the nutrients content (i.e. composition) of the feedstock played a vital role in the amount/dynamics of biogas produced as shown in Table 1 .

The total gas production profiles and % methane gas production over 21 days using ( a ) date fruit, ( b ) rice waste, ( c ) legume beans and ( d ) mixed food waste samples.

In the date fruit sample, the methane concentration was less than 20% in day 1, and then it significantly increased to 55% in day 3. Thereafter, the methane concentration was reduced to less than 20% and remained at that low value until day 21. However, the rice waste sample showed a gradual decrease in the methane concentration from day 3 up to day 16, then it increased dramatically to reach ~ 64% on day 21. The legume beans sample started with a high methane concentration (52%) on day 1 from the AD and then fluctuated to reach 40% by day 21. The methane concentration of the mixed food waste sample fluctuated at around 30% throughout the AD process.

Modelling results of biogas production using food waste

Table S1 (Supplementary Information) shows the total gas and methane production of different waste samples. There was a significant variation (p < 0.05) in gas and methane production between wastes. Gas production varied between 76 and 421 mL/g DM in fish waste and rice waste samples, respectively. Moreover, methane production varied between low production level of 1 mL/g DM and high production of 16.6 mL/g DM in fish waste and rice waste samples, respectively. The 24 h gas and methane production evaluations could be interesting to map potential gas production in waste samples, as it provides preliminary results on fermentation potential. In our study, there was a strong correlation (corr = 0.92) between the in vitro gas and methane production at 24 h. The considerable variation in gas and methane production could be partly explained by the variations in nutrient contents of wastes. Such variations provide nutrient supply to the microbes thereby resulting in different gas production levels. The evaluated waste samples herein represent a wide range of industrial and farming wastes, therefore variations in their nutrient contents are expected. Overall, the 24 h test might be used to evaluate feedstock fermentation capacity that is intended for longer periods. Thus, our approach could have a potential application in industry as the 24 h test can give a good indication of the potential substrate gas production as a quick test that saves time, with minimal effort required.

Table 2 presents a summary of the polynomial models used to predict the production of total gas and methane during the fermentation periods. Recent studies have suggested that polynomial models are particularly suited for examining gas production over the fermentation period in different fermentation systems 23 , 49 , 50 , 51 . From a fermentation standpoint, gas production is a continuous process and might show a growth relationship over a period of time in which such relationship can be tested by a polynomial model. The gas production models (Figs.  5 , 6 ) could explain the majority of data points, this conclusion is supported by the high goodness of fit. The total gas models showed an excellent fit between the theoretical and practical data. It is not surprising that the methane model did not show a good matching between the theoretical along with the practical data, except for only two samples, the date fruit and food waste samples. This is maybe due to the fluctuations observed in the methane production as shown in Fig.  4 . Methane production is proportional to gas production, this proportion changes over fermentation days depending on the nutrient contents of the biowastes. Therefore, in our study, the polynomial model used might only be a good fit for methane production data in two samples (date fruit and food waste). Further parameters such as nutrient contents and the interaction between total gas production and methane production level can be introduced in future studies to improve the fit.

Polynomial graphs of gas production (mL/g DM) concerning fermentation time in four food waste samples. Lines represent the predicted gas and dots represent practical values.

Polynomial graphs of methane production (mL/g DM) concerning fermentation time in four food waste samples. Lines represent the predicted gas and dots represent practical values.

The economic evaluation of biogas production from food waste

A preliminary economic study was performed herein to evaluate the economic feasibility of installing an anaerobic digester unit to process food waste produced from the case study (Fahud camp). The main concept is the replacement of LPG with biogas, where savings can be achieved. Bhatt and Tao studied the economic perspectives of biogas production via AD at different facility scales 36 . It is important to note that if food waste is processed at higher plant scales this ultimately reduces the overall cost through economies of scale. This can be achieved through the development of a centralized food waste collection and processing approach. The estimated biogas production rate used in this economic evaluation is based on the value determined from the AD experiment, as shown below in Table 3 and Table S2 at a scale of 3280 kg/month of food waste.

The economic analysis is based on the discounted cash flow (DCF) approach whereby projected future cash flows are discounted at a rate that represents the cost of capital. The analysis includes identifying the discounted payback period as well as the net present value (NPV) of the case study. The analysis investigated the impact of various gas prices on the viability of the project to provide investment guidance to decision-makers. Table 4 presents the assumptions on which the analysis is built.

Table 5 presents the annual cashflow calculations on which the DCF model is built.

Discounted Payback Period, DPP (years) is calculated according to Eq. ( 1 )

where I = initial investment, r = discount rate and CF = cash flow.

Where the net present value (NPV) is calculated according to Eq. ( 2 ) 52 .

Economic analysis results

Herein, Table 6 presents the discounted payback period and net present value (NPV) of the case study under different methane prices per m 3 , for a project lifetime over 10 years. Both analyses methods take into account the time value of money. The calculated net present value is the sum of all discounted future cash flows less the initial total investment made in capital expenditure and working capital. A positive NPV means that building a biogas unit is to be considered and that value is being created. If the NPV result is negative then the project in the case study should be discarded and if the result is zero, then no value is being created but also no loss is incurred. The calculated value is the total financial contribution of the case study over its lifetime to its owners, taking into account the time value of money. It can be noted that gas prices of $0.22/m 3 and $0.26/m 3 yield a negative NPV, indicating the project in the case study will be incurring losses at such rates. Furthermore, the discounted payback period calculated at these rates is > 10 years, i.e. longer than the expected lifetime of the equipment. It is not advised to carry out the project at such prices/m 3 . Furthermore, all the other rates investigated indicate that value is being created by developing this project since they all carry a positive NPV, however, payback periods vary accordingly. Furthermore, for the project to break-even, i.e. to yield an NPV of 0, a gas rate of approximately $0.2944/m 3 is required. Any prices under this rate would yield losses, and any prices above this rate would create value. The investment in a Fahud biogas production plant should be carefully considered based on the current and anticipated future gas rates. Please note that the annual cash flows used in the analysis are based on gas savings only. If waste management fee savings are incorporated, the total savings would be higher, increasing annual cashflows and enhancing project results.

Furthermore, this amount of biogas can replace approximately 28.6% of LPG gas currently consumed in Fahud (for cooking purposes), as shown in Table 7 .

There are also drawbacks associated with biogas production which include the release of greenhouse gas emissions (GHG) due to the ineffective handling within the process. 53 . A life cycle assessment should be carried out to evaluate and ensure the environmental sustainability of such process 54 . It is worth noting that food waste could be used for the production of value-added chemicals such as carboxylic acids (C 2 –C 6 acids) that may provide a better value proposition 5 , 55 , 56 .

In conclusion, herein, we used different types of food wastes to produce biogas and carried out a techno-economic evaluation to investigate the financial viability of setting up a small scale biogas plant. Our results show varying gas production rates between 76 and 421 mL/1 g DM. The feed composition is responsible for the variations in biogas yields with wastes that are rich in carbohydrate and fibre content, such as rice waste, carry remarkable potential for biogas and methane gas yields. Furthermore, during the 21 incubation days of anaerobic digestion, a fluctuation in methane concentration was observed in all samples which ranged between 20 and 60%. A good matching was observed between the theoretical and practical data based on the polynomial models with R 2  = 0.99. The economic evaluation results indicated that the project breaks-even at approximately $0.2944/m 3 , any prices above this rate yield a positive NPV. If waste management fee savings are incorporated, the total savings would be higher, increasing annual cash flows and enhancing project results. This economic evaluation serves as a preliminary guide to assess case study feasibility based on the fluctuating value of methane.

Experimental

Food waste samples were collected from Fahud Camp in PDO in Oman. The anaerobic digestion experiment was conducted on 11 different types of food waste in addition to a cow dung sample, to evaluate the biogas production performance from each sample. The following food waste samples were used: two types of mixed food wastes (fruit and vegetable waste), bread waste, potato peels waste, meat waste, rice waste, dates fruit, legume beans, leafy vegetables and fish waste.

Characterisation techniques

Dry matter determination.

The samples were weighed at ambient conditions and then placed in the oven and left at ~ 105 °C for 24 h. Then the dried samples were transferred into a desiccator. When the samples were cooled, the sample weight was recorded. Then the dry matter was calculated according to Eq. ( 3 )

After the drying procedure was completely done, 100 g of each sample is weighed in plastic containers and are labelled according to the dates of sample collection. The samples were ground in a coffee grinder until it was a fine powder.

Crude fibre (CF) content determination

1 g of dry sample was weighed into a crucible and the CF was measured with the aid of Fiber Tec system using100 mL of 0.128 M Sulphuric acid (first reagent). A few drops of octanol were added to prevent foaming, then the sample was heated to boiling by turning the effect control to max. The heat and boil were adjusted for 30 min by turning the timer. After 30 min the sample was filtered by turning the valve to vacuum position. The sample was washed three times with hot water. Then, 100 mL of sodium hydroxide solution (second reagent) was added to each sample. A few drops of octanol were added and boiled, as above, for another 30 min. After 30 min the samples were filtered and washed, as above, 3 times with hot water. The samples were washed 3 times with acetone and vacuum dried at 100 °C overnight. Then the samples were calcined in a muffle furnace at 500 °C overnight, then allowed to cool down and weighed. The percentage (wt%) of fibre in the test sample is given by Eq. ( 4 ):

Ash determination

A specific weight of sample was added into a porcelain crucible, then calcined at 550 °C for 6 h. Then, the sample was cooled, and the crucible was weighed. The ash content was calculated according to Eq. ( 5 )

Fat content determination

The samples were ground into the size of < 1 mm, then 1 g of sample (in duplicate) was weighed into an extraction thimble, plugged lightly with cotton wool and placed in the extractor. Followed by,100 mL of petroleum spirit added into a distillation flask and placed on the heaters. The solvent was heated at 50 °C and extracted for 8 h. After the extraction was complete, the heaters were stopped, and the flask was allowed to cool. Thimbles were removed from the soxhlet with the help of a pair of long forceps and placed in a beaker under the fume hood. Most of the solvent was distilled from the flask into the extractor. The flask (which now contains the fat) was de-attached and dried in an oven at 100 °C for 2 h to evaporate the remaining solvent. The flasks were cooled in a desiccator (for 45 min). The flasks were weighed again with the extracted oil and the fat content was calculated as in Eq. ( 6 ):

Determination of crude protein and nitrogen

Firstly, the digestion step was performed, 0.5 g (in duplicate) of the sample was weighed, then 10 mL of sulphuric acid was added. The tube was then placed in a digestion rack and digested for 1 h. After digestion, the tubes were allowed to cool. Then, this was followed by the distillation and titration steps and then calculated as per Eqs. ( 7 ) and ( 8 )

Rumen liquor collection

Rumen liquor used in the experiments was collected from a fistulated cow in the “Agricultural Experiment Station, Sultan Qaboos University”. Water containers (preheated to 39 °C) were used to place the collected rumen fluid. The collecting devices prepared, funnel and filter were used. At all times the rumen liquor sample was covered and flushed with CO 2 at 39 °C water bath. The illustration of the anaerobic experimental digester is shown in Fig.  7 .

The illustration of the anaerobic experimental digester using different types of food waste samples.

Invitro true digestibility manual gas production

The invitro digestible organic matters were determined in triplicate samples at lab-scale. 100 mL calibrated glass syringes were used to measure gas produced by fermentation. The syringes contained 200 mg of feed and were kept for 21 days. After mixing the fresh-made reducing solution the artificial saliva changed colour from navy blue to pink and finally, it became colourless. Approximately 30 mL of rumen liquor and artificial saliva mixture (with a ratio of 1–2) were added to the syringes, the plunger was adjusted. Finally, the syringes were kept in a water bath at 39 °C for the desired amount of time. Readings of the calibrated syringes were taken every 0, 3, 6, 12, 18, 24 h and then daily for 21 days.

Invitro true digestibility and methane gas determination

For ruminal methane determination, the daisy incubation method was used. The ground feed samples were weighed to 0.250 g and filled in nylon filter bags, which were previously rinsed with acetone to remove any traces of surfactant that might inhibit microbial digestion and dried in 100 °C in an oven. The collected rumen liquor was filtered through two layers of muslin cloth under continuous flushing of carbon dioxide gas to maintain the anaerobic condition and the buffer solution was prepared and kept in the one-way valve ankom daisy incubation jars for 24 h in vitro at 39 °C.

Determination of methane gas

Methane concentration in the gas produced was measured using the gas chromatography (GC) technique. Daisy jars were transferred from the incubator and brought to the GC, the rubber plug has a needle inserted in the headspace of the jar and injected in the GC. GC–MS analysis was performed on a Perkin Elmer Clarus 600 GC System, fitted with an Rtx-5MS capillary column (30 m × 0.25 mm i.d. × 0.25 μm film thickness; maximum temperature, 350 ºC), coupled to a Perkin Elmer Clarus 600 C MS. Ultra-high purity helium (99.99%) was used as a carrier gas at a constant flow of 1.0 mL/min. The injection, transfer line and ion source temperatures were 280, 290 and 290 °C, respectively. The ionizing energy was 70 eV. Electron multiplier (EM) voltage was obtained from autotune. All data were obtained by collecting the full-scan mass spectra within the scan range 40–550 amu. The injected sample volume was 1 μL with a split ratio of 10:1. The oven temperature program was 60 °C (held for 1 min) with a heating rate of 80 °C /min up to 280 °C, then held for 25 min. The unknown compounds were identified by comparing the spectra obtained with mass spectrum libraries (NIST 2011 v.2.3 and Wiley, 9th edition).

Modelling methods

Data on methane and biogas production at 24 h were analysed using the linear model (lm) procedure of R (R Core Team 2018). The Wald Chi-Squared test (Type II) was performed to obtain the least-square means of the tested variables.

The model fitted to the 24 h gas and methane data was as follows:

where the response variable was the gas and methane production in 24 h (mL/g DM), differences in results were considered significant if p value  < 0.05.

A polynomial model was used to fit the 21 days biogas and methane production data. The quartic model used to predict biogas and methane production was as follows: Y = b 0  + b 1 x + b 2 x 2  + b 3 x 3  + b 4 x 4 , model parameters were Y the response variable of total gas and methane gas production (mL/g DM), b 0 is the intercept and b 1 to 4 are the model coefficients.

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Acknowledgements

Authors would like to acknowledge the support given by the Petroleum Development Oman (PDO) and Sultan Qaboos University. A.I.O and D.W.R would like to acknowledge the support given by the EPSRC project “Advancing Creative Circular Economies for Plastics via Technological-Social Transitions” (ACCEPT Transitions, EP/S025545/1). A.I.O, S.F and D.W.R wish to acknowledge the support of The Bryden Centre project (Project ID VA5048) which was awarded by The European Union’s INTERREG VA Programme.

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Ahmed I. Osman, Samer Fawzy & David W. Rooney

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Experiment test, A.A.-W. and O.A. while writing—original draft, A.O. and A.A.-M., economic evaluation, S.F. writing—review and editing M.B. and D.R.

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Al-Wahaibi, A., Osman, A.I., Al-Muhtaseb, A.H. et al. Techno-economic evaluation of biogas production from food waste via anaerobic digestion. Sci Rep 10 , 15719 (2020). https://doi.org/10.1038/s41598-020-72897-5

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1 Introduction

2 materials and methods, 3 biogas production tests, 4 results and discussions, 5 conclusion, conflicts of interests.

• List of tables
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Regular Article

Experimental study of biogas production from water hyacinth

Djomsi Brillant Wembe 1 * , Rolland Djomi 1 , Noel Konai 1 , Gilles Nkadeu 1 and Guy Edgar Ntamack 2

1 Laboratory of Civil Engineering and Mechanics, National Advanced School of Engineering of Yaoundé, University of Yaoundé 1, P.O. Box 8390, Yaoundé, Cameroon 2 Department of Physics, Faculty of Science, Group of Mechanics, Materials and Acoustics, University of Ngaoundere, Post Box 454, Ngaoundere, Cameroon

* Corresponding author: [email protected]

Received: 7 October 2022 Accepted: 6 April 2023

In the Littoral and East regions of Cameroon, the proliferation of the water hyacinth threatens the conservation of biodiversity. Indeed, its rapid multiplication asphyxiates fish and promotes malaria. Integrated pest management and many other methods have been used to eliminate this plant, but it persists, endangering the eco-systemic balance of marine environments. The efficient management of this plant remains a challenge. The aim of this study is to contribute to the implementation of a water hyacinth management protocol. The idea was to use this plant not only for natural fertilizer for soil improvement but also as energy production (biogas). The tests were carried out at the HIMA application farm in the Abong-Mbang area. Experiments were conducted using 60-Liters batch digesters. In all three sets of experiments, combinations of water hyacinth, cow dung and chicken droppings were used. For water hyacinth alone, an average production of 70 L of biogas was recorded. The best yield was obtained for the mixture of water hyacinth (5 kg), cow dung (1.5 kg) and chicken droppings (1 kg) at 1/4 dilution, i.e. 179 L/kg of substrate. The results obtained are encouraging and are being effectively used. This technology can therefore be applied in areas infested by Eichhornia crassipes for the production of energy, compost and control of water hyacinth proliferation.

Key words: Water hyacinth / Biodigester / Biogas / Fertiliser / Cameroon

© The Author(s), published by EDP Sciences, 2023

The water hyacinth, with its scientific name Eichhornia crassipes , is an invasive aquatic plant originating from the Amazon and spread by man through horticulture in tropical and subtropical regions. In Africa, it is thought to have been introduced in the Congo Basin in the early 20th century as an ornamental pond plant by Belgian settlers ( ADEME, 2011 ), but it is only in the last twenty years that its populations have exploded ( Gopal, 1987 ). In recent years, many rivers in Cameroon such as the Wouri, Nkam, Moungo, Nyong and their tributaries have been invaded by the water hyacinth ( Viginie and Jules, 2016 ). These rivers are vital compartments containing many natural resources (fauna, flora, microorganisms, and mineral elements). One of the inconveniences generally mentioned when discussing the proliferation of Eichhornia crassipes is its propensity to completely cover the surface of the water it colonizes, hence its negative impact on navigation, irrigation, fisheries, electricity production and on the conservation of biological diversity as it causes the disappearance of many species of flora and fauna ( Holm et al. , 1977 ; Bote et al. , 2020 ). It is in a context of preservation and protection of aquatic ecosystems that the presence of E. crassipes appears as a danger to be curbed ( Téllez et al. , 2006 ). One way of using this plant is the production of biogas ( Tize et al. , 2015 ), which can be used as a cooking fuel or for electricity production and Also, the waste produced by the biogas plant was used to build the briquette-making equipment. ( Almoustapha et al. , 2008 ).

The objective of this study is to contribute to the establishment of a management protocol for water hyacinth in Cameroon through the use of this plant as a substrate for biogas production. The aim of this work is to evaluate the biogas potential of different associations between water hyacinth ( FAO, 1997 ; Rathod et al. , 2018 ) and livestock residues. The conduct of field experiments on biogas and fertiliser production will enable applications to be envisaged within the framework of a full-scale project, for the satisfaction of collective domestic energy needs.

2.1 Study site

This study was carried out on the HIMA application farm. The farm is located on the outskirts of Abong-Mbang, a town with an area of 11,250 km 2 (Longitude, 3°58′60″ N; Latitude, 13°10′60″ E.; Alt. 708 m). It is a large integrated farm of 5 ha, combining livestock (poultry and cattle), crop production and agro-processing activities. For the study, excreta were collected from the cow pens and chicken houses. Water hyacinth samples were taken along the of the Nyong River, in a swampy area with a series of pools and ponds with abundant aquatic vegetation including E. crassipes . The river Nyong is a freshwater river, which originates 40 km east of the town of Abong-Mbang in the great equatorial rainforest. It runs parallel to the lower reaches of the Sanaga River, following an East–West direction like the latter. It is crossed by an erosion valley, which receives household waste, sewage and rainwater draining into the beds of various rivers in the area, which in turn flow into the Nyong River ( Rtabagaya, 2017 ).

3.1 Experimental set-up

The experiments were conducted in a batch digester consisting of a 200-L plastic drum sealed with a clamp. The lid is fitted with a valve that controls the outlet of the biogas to the water trap. The sides are fitted with two pipes. One is connected to a loading chamber and the other, mounted at the base of the drum, allows liquid and/or solid effluent to be taken for various measurements during fermentation without having to open the digester ( Fig. 1 ).

3.2 The experimental protocol

Once collected, the fresh hyacinth is cut with a machete to a particle size of 5–8 cm ( Fig. 2 ). For biogas production, this material is either tested separately or mixed with cow purse and/or chicken droppings.

Experiment 1: Water hyacinth + cow dung

Three digesters with a cow dung and water hyacinth substrate were tested. The first is labelled B1, the second B2 and the third B3. After collection, pre-treatment and quantification of the cattle dung samples, they were diluted in three different doses (1/1, 1/2 and 1/3). Table 1 shows the dilution rate and the amount of waste contained in each digester. In all experiments, the digesters shown in Figure 3 were used.

Dilution rates.

Experiment 2: Water hyacinth + chicken droppings

Three digesters with chicken droppings and water hyacinth were tested. The first is labelled F1, the second F2 and the third F3. The droppings collected from the poultry houses were treated to remove large elements such as feathers and shavings, then mixed with the hyacinth and diluted at different proportions (1/3, 1/4 and 1/5). Table 1 shows the dilution rate and the amount of waste in each digester.

Experiment 3: Water hyacinth + cow dung + chicken droppings

In order to improve the fermentation of water hyacinth, cow dung and chicken droppings were combined and mixed with the hyacinth. The first is labelled BF1, the second BF2 and the third BF3. Table 1 shows the dilution rate and the amount of waste in each digester.

Production was monitored regularly and quantified throughout the anaerobic digestion cycle. The auto flammability test of the gas produced was carried out at each quantification of the production.

3.3 Evaluation of the biogas produced

The different mixtures were introduced into the digester, closed with the gasometer. The device is 95% embedded in the ground. During the anaerobic digestion, the volume of biogas was measured daily. This gas was analysed with a gas analyzer.

3.4 Valorization of the anaerobic digestion residue

After biogas production, the remaining heavy fraction called digestate is recovered and dried to a water content of less than 15%. The objective is to recycle the digestate’s constituent elements in order to have a transformed organic matter, rich in humic compounds, with maximum effects on soil fertility.

The quantitative and qualitative production was recorded every day during the hydraulic residence of the substrate in the digesters. The results below show the quantity and quality of the biogas produced during the experiments.

4.1 Raw biogas analysis

At the end of the production cycles of the different water hyacinth treatments, the volumes of raw gas produced were measured. Table 2 summarises the quantities of raw biogas collected, taking into account all the gas produced, including the flammable fraction.

Evolution of biogas production.

In general, the biogas potential and the digestion time vary according to the different substrates. According to Table 2 , the mixture of water hyacinth, cow dung and poultry manure has the best production potential ( Bhui et al. , 2018 ). The biogas potential of the different substrate combinations varies according to the dilution ratio. For water hyacinth + cow dung, the 1/3 ratio provides good potential with 175 l of biogas per kg of top substrate. Which is higher than those of the 1/1 and 1/2 ratios, which are respectively 92 and 171 L/kg of substrate. This similar result of the work of ( Aboubakar et al. , 2016 ) which shows that for the substrate made up of cow dung, the 1/3 ratio has the best yield. With regard to the mixture of water hyacinth and hen droppings, this potential also varies according to the water content and is quite low. This result can be explained by the fact that during the methanation reaction, ammonia is produced, which inhibits the process. This is corroborated by the work of Tize et al. (2011) , when they argue that an increase in nitrogen supply can lead to increased ammonia production, which can harm microorganisms and inactivate methanation. Regarding the hydraulic retention time, it should be noted that the fermentation process was stopped as soon as the daily production was less than 1 L/day.

4.1.1 Daily production

In order to monitor the production of gas at the level of the digesters, a gasometer is installed. The evolution of the gross biogas production of the different tests has been illustrated. Thus, the gross daily productions of biogas recorded in experiment 1 (B1, B2 and B3) are appreciated through Figure 4 .

Figure 5 represents the evolution of the volume of biogas produced according to the hydraulic retention time for the substrate consisting of water hyacinth and cow dung. In general, the 3 curves have the same appearance. The methanization reaction of water hyacinth takes place in three phases. During the first eight days, gas production is low at all the bioreactors. Subsequently, it increases to a peak around 92l/day for the 1/2 dilution, followed by the peak for the 1/3 and 1/1 dilutions.

In reactors with a substrate consisting of water hyacinth and chicken droppings, the best yield per kg of substrate was obtained for the 1/3 dilution. The daily gas production at the level of the pilot digesters with water hyacinth + manure substrate is shown in Figure 6 .

During the production of biogas from the substrate consisting of water hyacinth and poultry droppings, a very short retention time is observed. Indeed, the first peak of production (1/5) occurs on the 11th day, to fall very quickly because of the stoppage of the methanogenic fermentation.

For the substrates composed of water hyacinth, poultry manure, cow purse, the daily gross production of biogas recorded, and the results are recorded in Figure 7 . The average values recorded after indicate a production of 105 L of biogas for 15 kg of Water hyacinth, i.e. 70 L/kg/MH.

Figure 5 shows the evolution of the volume of biogas produced as a function of time in digesters BF1 (ratio 1/3), BF2 (ratio 1/4) and BF3 (ratio 1/5). The gas production starts to increase in a variable way after the tenth day, where a maximum daily production of 81 L of biogas is recorded for the BF3 digester on the 23rd day, a maximum daily production of 79 L of biogas is recorded for the BF2 digester on the 24th day, and a maximum daily production of 69 L of biogas is recorded for the BF1 digester on the 21st day.

For all the different substrates, a general trend can be observed: the average biogas production values recorded during the experiment show that the decomposition reaction takes place in three phases. The first phase takes place between the 1st and the 9th day after the start of the experiment. The gas that is produced during this phase is non-combustible. The second phase lasts 22 days, with gas production gradually increasing until day 29. Finally, the last phase where the methane production decreases to the lower limit of 5l/day.

4.2 Cumulative biogas production

The cumulative biogas production profiles are illustrated by the curves in Figures 8 – 10 .

The cumulative productions of biogas from the three types of experiments are all characterized by a low production of biogas during the first week of digestion (latency phases), then an acceleration of production was observed from the 8th to the 26th day (exponential phase), then a slowing down/stopping of production during the last week of digestion (bearing phase). The duration of these different phases depends on the nature of the substrate. The largest biogas production is revealed for experiment 1 [B1(1/1), B2(1/2) and B3(1/3)] profile in Figure 8 , ranging up to 1700 L of biogas production. Biogas during the 42 days of digestion of the substrates.

The cumulative production of biogas from cow dung substrates in a dilution ratio (11 kg of hyacinth, 4 kg of cow dung and a 1/1 ratio of water dilution), (7 kg of hyacinth, 3 kg of cow dung and a 1/2 water dilution ratio), and (5 kg of hyacinth, 2.5 kg of cow dung and a 1/3 water dilution ratio), in the different digesters (B1, B2 and B3), are respectively: B1 (1381 L), B2 (1719 L) and B3 (1316 L). For a total cumulative production of 4416 L. It appears from these results that the substrates with a dilution ratio close to 1/2 remain the most favorable in anaerobic digestion for the optimal production of biogas.

4.3 Fertilizer production

Once fermentation is complete, a material called digestate is recovered and subjected to controlled dehydration. For the anaerobic digestion of the water hyacinth + cow dung (B1) substrate, the digested matter represents only 6.5 kg of organic matter. According to ( Oumarou et al ., 2008 ), this material contains nitrogen, phosphate and organic carbon compounds.

Oumarou Almoustapha gives us the content of nitrogenous and phosphorus compounds (mg/kg/DM) of water hyacinth composts by anaerobic fermentation on a sample of 49 kg of fresh digestate, samples of which, analyzed in the laboratory, have shown that the compost obtained contains approximately 0.75 kg of nitrogen compounds, 10.6 kg of phosphate compounds and 1.1 kg of organic carbon ( Table 3 ).

The below results support our choice of using digestate as an agricultural fertilizer.

These results support our choice of using digestate as an agricultural fertilizer.

Sample preparation: Take representative samples of digestate at different depths and mix them to obtain a homogeneous sample.

Physico–chemical measurements: measure physico–chemical properties such as pH, conductivity, humidity, density, organic matter content, nutrient content (nitrogen, phosphorus, potassium), element content traces (heavy metals, etc. ) and the content of microorganisms.

Gas analysis: measure the gas content of the digestate, i.e. the composition of methane, carbon dioxide, hydrogen sulphide and other gases.

Biomass analysis: analyze the composition of residual biomass such as fibres, particles and fines.

Microbiological analysis: study the microbial communities present in the digestate to understand their role in the fermentation process and their interactions.

Digestibility analysis: measure the ability of the digestate to produce biogas, i.e. measure the residual biodegradability of organic matter.

Economic analysis: assess the costs associated with the production and management of digestate, as well as the economic value of the digestate and its applications.

4.4 Economic evaluation and material balance

The experimental results obtained are encouraging and in the process of being put to effective use. The mixture of water hyacinth, cow purse and poultry droppings offer the best yield.

Initial biomass: 7 kg of hyacinth + 3 kg of cow purse = 10 kg

Cumulative biogas production: 1719 L

Biogas yield: 1719 L/15 kg = 114.6 L/kg

Cost of hyacinth: 0.16 USD/kg × 7 kg = 1.16 USD

Cost of the cow purse: 0.33 USD/10 kg × 3 kg = 0.09 USD

Cost of water: 0.04 USD/10 L = 0.41 USD/L

Total cost of the biomass: 1.16 USD + 0.33 USD + 0.0041 USD = 1.52 USD

Potential revenue from 1719 L of biogas: 1 USD × 1719 L = 1719 francs

Potential profit: 1719 USD–1.52 USD = 1717.48 USD

That is 1717.48/15 kg of biomass = 114.49 USD/kg of biomass for a cycle of 42 days.

It should be noted that this economic study is based on simplifying assumptions, such as the cost of raw materials and the selling price of biogas, which may vary depending on various factors such as market demand, government subsidies and production costs. Further cost and revenue analysis is needed to determine the economic viability of a biogas project from water hyacinth and cow’s purse.

The HIMA farm intends to develop this technique by building a 20 m 3 biogas plant. This will allow the recovery of about 475 kg of water hyacinth per cycle with a production of 120 m 3 of biogas. This biogas will be used to heat the chicks and to cook their meals. In addition, this gas can be used to produce electricity ( ADEME, 2010 ). This transformation will not only reduce the energy costs of the farm but will also prevent the invasion of the Nyong River by water hyacinth. In addition, the assessment of the growth rate of this plant in the region shows that it is possible to generate more than one million people’s cooking gas ( Moletta-Denat et al. , 2010 ). This will cover the needs of 160,000 six-person households.

In the Nyong River area, E. crassipes invasion is important and the rate of renewal is very fast, so it is a source of raw material for biogas production. Indeed, the study shows that a continuous type digester of 20 m 3 volume using water hyacinth, cow dung and chicken droppings produces an average of 120 m 3 of biogas. About 1.2 tons of compost can be recovered from this process. In Cameroon, as in most countries colonized by the water hyacinth, the application of this technology offers several solutions, including the production of energy and fertiliser in a decentralized manner, and above all the control of the proliferation of the water hyacinth, which, in addition to threatening biodiversity conservation, has harmful effects on certain economic activities.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Elevated biogas production from the anaerobic co-digestion of farmhouse waste: Insight into the process performance and kinetics

Associated data.

Supplemental material, Supplementary_material for Elevated biogas production from the anaerobic co-digestion of farmhouse waste: Insight into the process performance and kinetics by Spyridon Achinas and Gerrit Jan Willem Euverink in Waste Management & Research

The biodegradable portion of solid waste generated in farmhouses can be treated for energy recovery with small portable biogas plants. This action can be done across the Netherlands and all around the planet. This study aims to appraise the performance of anaerobic digestion of different wastes (cow manure, food waste and garden waste) obtained from a regional farmhouse. Batch reactors were established under mesophilic conditions in order to investigate the impact of ternary mixtures on the anaerobic digestion process performance. Different mixing ratios were set in the batch tests. The upshots from the experiments connoted that ternary digestion with cow manure:food waste:garden waste mixing ratio of 40:50:10 yielded higher biogas amount. The kinetics’ results showed quite good congruence with the experimental study. The results from the kinetic analysis appeared to be in line with the experimental one.

Introduction

Depletion of non-renewable energy sources, overpopulation, food security issues, and environmental problems have accelerated the urge for sustainable energy production ( Achinas et al., 2017 ; Bezama and Agamuthu, 2019 ; Fraga et al., 2019 ; Hönig et al., 2019 ; Theuerl et al., 2019a ). Research efforts focus on the bioenergy deployment from agricultural and farming waste ( Manni et al., 2017 ; Matsakas et al., 2017 ; Oreggioni et al., 2017 ; Valenti and Porto 2019 ). Several treatment methods are applied to treat organic waste, with the anaerobic digestion (AD) technology having, among others, economic value in large-scale applications ( Franco et al., 2019 ; Ghanimeh et al., 2018 ; Lemões et al., 2018 ; Maroušek et al., 2018 ; Nelson et al., 2017 ; RedCorn et al., 2018 ; Rosero-Henao et al., 2019 ). AD is considered an alternative fuel production option for bioenergy production, as it is a biochemical process that converts organic waste into valuable products ( Chen, 2017a ; Đurđević et al., 2019 ; Efferth, 2019 ; Hildebrandt and Bezama 2018 ; Makarichi et al., 2019 ; Ruggero et al., 2019 ). Biogas is an energy-carrier and its composition consists of approximately 66% CH 4 , 33% CO 2 , 0.5% N 2 , 0.1% O 2 , and 103 mg H 2 S (L biogas) −1 ( Achinas et al., 2019 ; Bienert et al., 2019 ; Sahajwalla, 2018 ). Based on the application, the biogas may undergo post-treatment (upgrading) to reach the natural gas specifications ( Florio et al., 2019 ; Macedonio and Drioli, 2017 ; Santos-Clotas et al., 2019 ; Solarte-Toro et al., 2018 ). The versatile use of biogas for heat and electricity generation or vehicle fuel (upgrade biogas) can underpin the drive for its application ( Achinas and Achinas, 2017 ; Chatzikonstantinou et al., 2018 ; Lyng and Brekke, 2019 ; Wang et al., 2018 ). It is also implicit that use of other energy sources (e.g. wind, nuclear, shale gas) may hinder the AD competitiveness ( Cook, 2017 ; Davis, 2018 ; Koçer and Özçimen, 2018 ; Toselli et al., 2019 ; Zhang et al., 2019 ).

Nonetheless, mighty AD applicability can be perceived from both socioeconomic and environmental standpoints. Sustainable engineering has paved the way for AD technology to be widely applied in the European Union (EU) and, thus, biogas is a key component for the transition to the bioeconomy ( Chen, 2017b ; Chen et al., 2018 ). Government subsidisation has catalysed the inexorable growth in the number of biogas plants around the globe. To date, it is crucial to sustainably improve the rural areas life by materials recovery and reduced energy consumption ( Lamidi et al., 2019 ). The use of highly lignocellulosic waste streams may be a constraint for the applicability of AD technology owing to their recalcitrance ( Achinas and Euverink, 2016 ; Dalmo et al., 2019 ; Martínez et al., 2019 ; Smuga-Kogut et al., 2019 ).

Plenty of factors collude to establish an efficient bioreactor performance correlated to process conditions and microbiome dynamics. However, the vicissitudes during the operation of the wastes-treating bioreactors have procreated interest in investigating the co-digestion technique.The co-digestion technique has been previously pointed out as an alternative option to treat two or more substrates ( Alatriste-Mondragón et al., 2006 ; Esposito et al., 2012c ; Luo et al., 2019 ; Rabii et al., 2019 ; Theuerl et al., 2019b ). The carbon to nitrogen (C:N) ratio (ideal ratio ranges from 20–30) is a key player for the efficient simultaneous treatment of different substrates ( Esposito et al., 2012a ). Maroušek et al. (2014) reported the conventional methods of nutrient management, namely total organic carbon/total nitrogen (TOC/TN) and total carbon/total nitrogen (TC/TN), are not sufficient to be applied to the advanced phytomass residue processing.

This report enunciates the importance of the small biogas units in farmhouses for methane capturing and a sustainable waste management. The experimental study attempted to investigate the digestion of ternary wastes mixtures and their effect on the AD performance. Cow manure (CM), food waste (FW), and garden waste (GW) were chosen as substrates for the experimental test. CM has been widely used as a substrate for the biogas production. FW consists mainly of remains of eggs, nuts, vegetables, pasta, fruits and potatoes (raw or prepared), and sweets. GW contains garden clippings, cut grass, leaves, and plants. The specific milestones of this study were to (1) determine the biogas yield of the ternary mixtures, (2) examine the impact of ternary digestion on the AD performance and stability, and (3) predict biogas production using a first-order model and cone model.

Materials and methods

Inoculums and substrate.

The inoculum used in this study was obtained from a long-term operating anaerobic digester from the wastewater treatment plant (WWTP) of Garmervolde in Groningen, the Netherlands. The inoculum was stored at 6°C to maintain freshness and microbial activity. It was reactivated at 37°C for 3 days prior to use. CM, FW, and GW were garnered from a farm in Groningen province, the Netherlands. The organic fraction of household FW was selected manually and ground into small particles (<10 mm). All substrates were stored in the freezer prior to digestion.

Batch tests

Biogas tests were conducted in batch mode to assess the impact of ternary mixtures on the AD performance ( Esposito et al., 2012b ). Laboratory glass bottles with a total volume of 500 mL (400 mL working volume) were used as anaerobic digesters. The inoculum-to-substrate ratio (ISR) was set at two based on former studies ( Fabbri et al., 2014 ; Gunaseelan, 1995 ). The ratios of the ternary mixtures applied during the tests are shown in Table 1 . The glass bottles were filled with the appropriate amount of microbial inoculum, substrate(s), and distilled water. All the bottles were flushed with N 2 for 5 min, sealed with butyl rubbers and thereafter placed in a rotating incubator in 150 r min −1 at 36°C. Triplicate bottles were used in all experiments, and all values reported are means of triplicate ± standard deviation.

Process conditions applied in the batch tests.

CM: cow manure; FW: food waste; GW: garden waste.

Analytical methods

Total solids (TS; g kg −1 ) and volatile solids (VS; g kg −1 ) were estimated according to the recommendations of the Standard Methods of APHA et al. ( 2005 ). PH was measured using a pH meter (HI991001, Hanna Instruments, USA). Total alkalinity (g CaCO 3  L −1 ) was determined using the Nordmann titration method ( Lossie and Pütz, 2008 ). The methane content was determined with a micro gas chromatography (GC) device (single channel 2-stream selector system, Thermo Fisher Scientific Inc, USA) equipped with a chromatographic column (PLOT-U). Helium was used as a carrier gas at a total flow of 10 mL min −1 . A gas standard consisting of 50% (v/v) CH 4 , 20% (v/v) CO 2 , and 30% (v/v) N 2 was used to calibrate the results from the micro GC device. The method used to estimate the biochemical biogas potential was based on a volumetric test, which considered the displacement of a liquid into gas to measure the biogas production ( Morosini et al., 2016 ). The water displacement equipment used in this work was capable of providing biogas data within an accuracy of 5% ( WRC, 1975 ).

The daily biogas volume (mL g VS substrate −1  day −1 ) was measured with the water displacement method and was standardised according to DIN 1343 (standard conditions: temperature (T) = 0°C and pressure (P) = 1.013 bar) ( VDI, 2006 ). The biogas volume was normalised according to the equation ( Dinuccio et al., 2010 ):

where V N is the volume of the dry biogas at standard temperature and pressure (mL N ), V is the recorded volume of the biogas (mL), p w is the water vapour pressure as a function of ambient temperature (mmHg), and T is the ambient temperature (°K). The water vapour pressure (p w ) was estimated according to the modified Buck equation ( Buck, 1981 ):

where P(T) is the vapour pressure in mmHg and T is the temperature at the ambient space (°C).

Kinetic study

The first-order kinetic model and cone model were applied for the hydrolysis of organic matter using Microsoft Office Excel (Microsoft Office 2010) and their equations are ( Lay et al., 1998 ; Luna-del Risco et al., 2011 ):

where G(t) is the cumulative biogas yield at digestion time t days (mL biogas g VS substrate −1 ), G O is the maximum biogas potential of the substrate the biogas potential (mL biogas.g VS substrate −1 ), n is the shape factor, K is the biogas production rate constant (d −1 ), and t is the time (days).

Technical digestion time was used to apply the models and is regarded as the time needed to produce 80% of the maximal digester biogas production ( Palmowski and Müller, 2000 ). The predicted data were plotted with the experimental biogas data. For the validation of the models, the statistical indicators root mean square error (RMSE) and correlation coefficient (R 2 ) were calculated from the equations ( Bhattarai et al., 2012 ):

where d j is the deviation between the j th measured and the predicted values and m is the number of data points; and

where X j is the j th predicted value.

Statistical analysis

Statistical significance of the data was determined by one-way ANOVA using Microsoft Office Excel (Microsoft, USA) with a threshold p -value of 0.05.

Results and discussion

Characterisation of inoculum and feedstock.

The characteristics of the anaerobic inoculum, CM, household FW, and GW are summarised in Table 2 . All the substrates had contiguous content of total carbon, but varied in the contents of total nitrogen. Cellulose is encapsulated by hemicellulose and lignin, rendering a complex release of sugars. The recalcitrant nature of lignin hampers the deconstruction of the substrate. In addition, the high content of lignin elongates the digestion time and concomitantly results in lower biogas yields. It was anticipated that GW will show lignin values (31.4%) as it contains woody components and the upshot was similar to that in previous studies. CM also showed similar lignin content (12.6%) compared with that formerly cited.

Physical and chemical characteristics of the inoculums and substrates used in the batch tests.

Values are the average of three determinations and numbers in parentheses are the standard deviations.

Garden waste: Flowers, grass clippings, leaves, small branches, small prunings, twigs, weeds.

ND: not determined; TS: total solids; VS: volatile solids.

Daily biogas production

During the AD of the individual substrates, R1 and R3, with 100% CM and GW, respectively, began to produce ⩾10 mL g VS substrate −1  day −1 on the third and sixth day, respectively ( Figure 1 ). Low hydrolytic performance was observed owing to the presence of lignin and its derivatives. The highest daily biogas production rates for R1 and R3 were 15.5 and 12.7 mL g VS substrate −1 on the seventh day, respectively. The reactor with 100% FW (R2) began rapidly to produce a high amount of biogas reaching 55.1 mL g VS substrate −1 on day three. It remained for the first eight days in the range of 28.1–55.1 mL g VS substrate −1  day −1 and afterwards gradually declined to a lower level until the biogas production dropped to zero on day 25.

Daily biogas production for the mono-digestion (R1→3) and co-digestion (R4→12) tests.

Rapid biogas production began in the reactors treating ternary mixtures ( Figure 1 ) even though it did not show any clear dependence on the substrate mixing ratio. The reactors containing 20% CM (R4→6) reached the maximum daily biogas production rate of 44.5, 36.8, and 20.0 mL g VS substrate −1 on days six, four, and four, respectively. The treatments with 40% CM (R7→9) showed similar trend reaching 45.9, 27.9, and 22.6 mL g VS substrate −1 on the fourth day of the digestion period. In contrast, the maximum daily biogas derived from the reactors (R10→12) containing 60% CM was 14.6, 16.2, and 14.6 mL g VS substrate −1 , respectively. The daily biogas amount remained above 10 mL g VS substrate −1 for the first ten days and thereafter dropped to a lower level (<6 mL g VS substrate −1  day −1 ). The overall performance was at low ebb due to fast hydrolysis and the subsequent volatile fatty acids (VFAs) acidification that inhibits the methanogenic activities.

Cumulative biogas production

The cumulative biogas and methane yield from all the treatments are displayed in Figure 2 and Figure S1 . From the mono-digestion, the highest cumulative biogas yield was obtained from the reactor treating 100% FW (429.9 mL g VS substrate −1 ), which was 2.4- and 3.3-fold higher than those reactors treating 100% CM and GW, respectively ( Table S1 ). FW is a promising organic substrate in the AD owing to its easily digestible containing material. Nevertheless, the digestion of FW as sole substrate can induce the accumulation of VFAs or ammonia and thereupon lead to bioreactor instability. The reactor operation treating 100% GW may be interrupted owing to the high lignin content and a low cumulative biogas yield of 129.8 mL g VS substrate −1 was observed ( Figure S2 ). Chiumenti et al. (2018) reported lower biogas yields from the treatment of high lignin-based waste. The use of CM as sole feedstock resulted in low biogas yield (180.8 mL g VS substrate −1 ) and is within the range that has been formerly cited.

Cumulative biogas yields from the mono-digestion (R1→3) and co-digestion (R4→12) tests. Depiction is based on Tables 1 and ​ and3 3 (later in this article).

The cumulative biogas yields of the ternary mixtures were also varied with the ratios of three substrates. Treatments with 20% CM (R4→6) reached biogas yields of 371.9, 329.5, and 155.8 mL g VS substrate −1 ( Figure S2 ). The increment of the fraction of GW attenuated the degradation showing a decline on the biogas yield. One contingent reason for the lower biogas production yield could be the hardly degradable lignocellulosic material contained in the GW. Among all ternary mixtures, the highest biogas production yield was obtained from the mixture with 40% CM, 50% FW, and 10% GW, which was 2.5-, 1.1-, and 3.5-fold higher compared with digestion of CM, FW, and GW, respectively ( Table S-1 ). Animal manures have a high alkaline capacity, which turns them into suitable substrates for AD. Most notably, CM might reinforce the degradation activity of FW as it has active archaea endowed with an excellent capacity to produce biogas. A preceding study states that cow dung is superior to sheep manure as a microbial inoculum to produce biogas ( Achinas et al., 2018 ).

Leung and Wang (2016) , by reviewing the anaerobic treatment of FW, claimed that biogas generation can significantly depend on the process parameters of the bioreactors and this is ascribed to the complex biodegradability of FW. Masourek ( 2013 , 2014 ) examined the two-fraction anaerobic fermentation of grass waste allowing faster and cost-efficient fermentation into methane. Furthermore, blending FW and CM is an economically viable approach as it allows the digestion of high organic loads ( Li et al., 2009 ). The low C/N ratio of FW can inhibit the AD and lead the digester to a ‘sour’ situation. In the microbiome level, numerous challenges may induce changes in bioreactor behaviour, as it is construed by the physiological and biochemical interactions of microorganisms within the bioreactor. Former scientific reports refer ammonia being the principal reason of digester inhibition as it penetrates the bacterial cells causing proton imbalance, altering intercellular pH, and inhibiting specific enzyme responses. Thus, co-digestion with different waste is an efficient technique to balance the C/N ratio in the digester and avoid resurgence of NH 3 .

Another type of common co-substrate for FW is the lignocellulosic waste with a high C/N ratio and relatively high recalcitrance, for example yard waste and straw. This kind of feedstock can supplement the necessary amount of carbon for the nitrogen-rich FW and help to overcome the rapid acidification in AD using FW as the sole feedstock.

pH, alkalinity, and VS removal

Figure 3 depicts the pH values at the beginning and end of the experiments. The pH values ranged from 6.98 to 7.55, rendering a suitable environment for the substrate degradation. Reactors resulted in a final pH lower than the starting pH with the reactor treating only FW reaching a final pH 6.98. AD is efficiently facilitated in a pH range 6.8–7.4. However, the range of 5.5–6.5 is more favourable for the activity of hydrolytic and acidogenic bacteria. The pH of the bioreactor is a critical factor for the decomposition of the anaerobic digester as it may cause perturbations on the microbiome dynamics and the subsequent metabolomic pathways ( Carotenuto et al., 2016 ). Microbial activity is inhibited when the microbiome is exposed to low pH values, which impedes the digester operation. Although bacteria under anaerobic conditions thrive in a broad range of pH, methanogens are notably sensitive in lower pH values. As a result, elevated concentration of VFAs subdues the methanogenic reactions, a fact that creates a deficit in methane ( Anggarini et al., 2015 ).

pH and alkalinity for the mono-digestion (R1→3) and co-digestion (R4→12) tests.

The microorganisms are also nifty at their tolerance in alkalinity of the bioreactor. Buffer capacity, the so-called alkalinity, is a parameter to evaluate the stability of anaerobic digesters ( Cheng et al., 2016 ). The total alkalinity of the bioreactors at the beginning and end of the experiments is shown in Figure 3 . In all the experimental sets, the ISR was set two, as this is considered optimal for maintaining buffering capacity. No extra alkalinity was added in this study as it was provided by the inoculum. Inoculum use is levied on the AD process as it can supply nutrients and alkalinity subduing, and therefore overcome the drawbacks of the digestion of hardly degradable materials. Franchi et al. insinuate that the choice of inoculum source must be nifty at its physiological interaction with the microbiomes within the digester ( Franchi et al., 2018 ).

Gupta et al. (2012) ascertained the influential effect of different sources of microbial inoculum on the digestion of GW and the prevention from unavoidable disturbances. They concluded that paddy-field soil can enhance the biogas production compared with that using cow-dung, mine water, or termite guts as inoculums. The upshots of alkalinity showed a similar pattern with the one of pH. The pH decrease is offset by the elevated alkalinity from the presence of bicarbonate, carbon dioxide, and ammonia.

The determination of VS removal aimed to examine the degradation efficiency and correspondence with the biogas produced. The calculated VS removal of all reactors is appended in Table S1 . R6 showed the highest VS degradation rate of 48.2% following by R4 and R5 with degradation rates of 45.4% and 44.0%, respectively. Oligomer solubility is a crucial regulator of the hemicellulose hydrolysis rate ( Gray et al., 2003 ).

Battista et al. (2015) scaled-up the co-digestion of agro-food wastes and explored the effect of inhibitory substance-containing feedstocks in the bioreactor’s efficiency. They elucidated the importance of macro-elements (e.g. nitrogen, phosphorus) on the microbial growth. From another aspect, the functional relationship of cumulative biogas yield and VS% removal was plotted in Figure 4 . A curve regression equation was established (Y = −0.0003X2 + 0.176X + 15.632, R 2 = 0.2666) and as anticipated, cumulative biogas yield pursued the same incremental tendency as the VS removal.

Correlation of biogas produced per gram of volatile solids (VS) and percentage of VS removal for all the experiments.

Kinetics results

The kinetic parameters obtained by applying the first-order and cone model are epitomised in Table 3 , which evinces the picture of the kinetics. To ratify the soundness of the results from both models, the predicted values of biogas were plotted against the measured values ( Figure S3 ). Both models were found to show good fitting with the experimental results. It is notable that the difference between the measured and predicted values of both models was less than 5% for all the reactors.

Results of the kinetic study using the first-order and cone model.

RMSE: root mean square error; VS: volatile solids.

Reactors treating only FW (R2 in both models) showed the highest hydrolysis rate with 0.2219 (R 2 = 0.9876) and 0.2234 (R 2 = 0.9948) in the first-order and cone model, respectively. One possible reason for the improved hydrolysis of the substrate is the easily digestible material contained in FW. Lower biogas yields appear owing to the inhibited methanogens growth from the rapid FW acidification resulting in a slow methanogenesis rate. Even though R7 showed low hydrolysis rates, microbial interactions from inoculum and manure might favour overall degradation performance.

Recommendations

Broadly speaking, research efforts provide insights into the technological barriers for sustainable transition to bioeconomy ( Chen, 2016 ; Lauer and Thrän, 2018 ; Lindkvist et al., 2019 ). AD is regarded as an ecological approach for energy recovery in rural areas and the production of valuable products from organic waste can ameliorate the agricultural economy ( Llewellyn, 2018 ). The versatile use of biogas as well as the production of valuable bio-fertiliser will play a key role in the agricultural chain. However, there are ambiguous facets not clearly investigated, namely the case of multiple waste streams, microbiome, or end-products ( Baek et al., 2018 ; Éles et al., 2019 ; Huang et al., 2017 ; Koç et al., 2019 ; Owczuk et al., 2019 ; Xu et al., 2017 ).

Figure 2 limns the overall view of the AD upshots from the experimental tests. A wide consortium of microorganisms is involved in the mesophilic AD process, thus, mesophilic temperatures have been predicated more suitable for efficient biogas production than the thermophilic temperatures ( Guo et al., 2018 ; Önen et al., 2019 ; Shin et al., 2019 ). The partial addition of FW and GW represents an efficient pathway for farm-scale digesters. Treating ternary mixtures can have significant impact on the adoption of AD technique owing to the incremental availability of wastes. The type of animal slurries is also crucial for the stability of the anaerobic bioreactors. According to Świątek et al. (2019) , the bioreactor intaking with chicken manure has changed its microbiome. Considering the above inferences, the mesophilic co-digestion of ternary mixtures represents a promising solution to alleviate inhibitors of digesters and attain a high biogas yield. Begum et al. (2018) facilitated high-rate co-digestion of mixed organic wastes and reported positive ramification on the biogas yield. A techno-economic evaluation of pilot AD in continuous mode would be interesting in order to assess other factors than the mixing ratio for full-scale applications ( Achinas and Euverink, 2019 ; Baccioli et al., 2019 ; Benato and Macor, 2019 ; Carlini et al., 2017 ; De Medina-Salas et al., 2019 ). In addition, we recommend an in-depth analysis of microbiome heterogeneity to assess the activity discrepancies between microbial communities. Alongside this, molecular tools can unveil the microbiomes–process conditions nexus in order to optimise the anaerobic digester operation and avoid pertubations that occur in full-scale.

This study explored the AD of three different waste streams and suggests an optimal mixing ratio for an efficient biogas production. Three different waste streams and their ternary mixtures were anaerobically treated in batch mode. The treatment of ternary mixtures showed positive impact on the AD performance. The results from the experimental tests revealed that ternary digestion with a CM:FW:GW mixing ratio of 40:50:10 yielded a higher biogas amount than that of the mono-digestion of FW. The high recalcitrance of CM and GW can be overcome by the addition of FW. In addition, small biogas units can be considered a beneficial option for farmhouse owners to convert bio-degradable waste into biogas and feriliser. Furthermore, the kinetics models fitted well with the experimental data enhancing the applicability of ternary digestion.

Supplemental Material

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental material: Supplemental material for this article is available online.

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Experimental and simulation analysis of biogas production from beverage wastewater sludge for electricity generation

Affiliations.

• 1 Center for Environmental Science, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia.
• 2 School of Mechancial and Industrial Engineering, Addis Ababa Institute of Technology, Addis Ababa University, P. O. Box 1176, Addis Ababa, Ethiopia. [email protected].
• 3 Center for Environmental Science, College of Natural and Computational Sciences, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia. [email protected].
• PMID: 35650251
• PMCID: PMC9160279
• DOI: 10.1038/s41598-022-12811-3

This study assessed the biogas and methane production potential of wastewater sludge generated from the beverage industry. The optimization of the biogas production potential of a single fed-batch anaerobic digester was operated at different temperatures (25, 35, and 45 ℃), pH (5.5, 6.5, 7.5, 8.5, and 9.5), and organic feeding ratio (1:3, 1:4, 1:5, and 1:6) with a hydraulic retention time of 30 days. The methane and biogas productivity of beverage wastewater sludge in terms of volatile solid (VS) and volume was determined. The maximum production of biogas (15.4 m 3 /g VS, 9.3 m 3 ) and methane content (6.3 m 3 /g VS, 3.8 m 3 ) were obtained in terms of VS and volume at 8.5, 35 ℃, 1:3 of optimal pH, temperature, and organic loading ratio, respectively. Furthermore, the maximum methane content (7.4 m 3 /g VS, 4.4 m 3 ) and biogas production potential (17.9 m 3 /g VS, 10.8 m 3 ) were achieved per day at room temperature. The total biogas and methane at 35 ℃ (30 days) are 44.3 and 10.8 m 3 /g VS, respectively, while at 25 ℃ (48 days) increased to 67.3 and 16.1 m 3 /g VS, respectively. Furthermore, the electricity-generating potential of biogas produced at room temperature (22.1 kWh at 24 days) and optimum temperature (18.9 kWh) at 40 days was estimated. The model simulated optimal HRT (25 days) in terms of biogas and methane production at optimum temperature was in good agreement with the experimental results. Thus, we can conclude that the beverage industrial wastewater sludge has a huge potential for biogas production and electrification.

© 2022. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

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HYPERLINK "sps:id::fig1||locator::gr1||MediaObject::0" ( a ) Schematic diagram of the experimental setup for small-scale…

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