Physico-chemical characterization of selected feedstocks as co-substrates for household biogas generation in Ghana

ABSTRACT

Substituting biogas for Liquefied Petroleum Gas (LPG) in households is a long-awaited sustainable solution for the increasing cost of energy and large amounts of household human-generated waste. Nevertheless, a study of the characteristics of feedstocks is essential to maximise their energy potential. Consequently, this study examined the physico-chemical properties of Human Excreta (HE), Food Leftovers (FLO), Kitchen Residue (KR) and Cow Dung (CD) of Ghanaian origin adhering to recommended standards. Results for volatile to total solid ratios (VS/TS) were 0.81 ± 0.001, 0.97 ± 0.001,0.89 ± 0.001 and 0.85 ± 0.001 for HE, FLO, KR and CD, respectively. The results showed that all feedstocks had high biodegradable content, making them desirable for biogas production. The carbon-to-nitrogen (C/N) ratios determined from the elemental compositions were 8.29 ± 0.09, 22.14 ± 0.26, 23.34 ± 0.25 and 26.19 ± 0.47 for HE, FLO, KR and CD, respectively. Although the C/N ratios for FLO, KR and CD were within the optimal range, that of HE was significantly low. With a mean alkalinity of 1219.67 ± 1.53, 630.00 ± 0.58, 590.00 ± 2.08 and 15,730.00 ± 6.00 mg CaCO₃ eq./L for HE, FLO, KR and CD, it was observed that only CD has the optimal alkalinity value for anaerobic digestion. This brings into perspective the need for co-digestion and the choice of potential co-substrates for household biogas production.

KEYWORDS:

• Anaerobic co-digestion
• biogas
• feedstock
• human excreta
• food waste
• biogas potential

1. Introduction

Ghana shares similar challenges with other developing nations regarding waste management and access to clean energy. Fortunately, these two challenges are connected. A better waste management strategy would involve turning the organic fraction of the wastes produced into biofuels. In many Ghanaian households, substantial amount of organic food waste and human excrement are produced daily (Arthur et al. 2020). Miezah et al. (2015) reported that Ghana generates 12,710 tons of waste per day, which, translates into a waste generation rate of 0.47 kg/person/day. However, only about 44% of the solid wastes generated in Ghanaian metropolis are properly collected and disposed of (Abalo et al. 2018). Additionally, Arthur et al. (2020) reported that 38.6% rural Ghanaians use household flush and non-flush toilet facilities. That notwithstanding, waste management practices in Ghana mostly focus on disposal. With the rising pattern of garbage output, finding sustainable strategies to achieve waste management goals and the sustainable development goals (SDGs) 3(good health and well-being), 6(clean water and sanitation), and 13(climate action) has become difficult for governments and city authorities.

Reliance on fossil fuels is becoming increasingly unsustainable due to ecological and environmental issues. Also, people in rural regions are mostly known for their usage of woody biomass, typically in the form of charcoal or firewood for cooking purposes. Sharma, Pareek, and Zhang (2015) have reported that the global contribution of biomass to total energy consumption is between 75% and 90%, with 40% of people using agricultural biomass such as wood fuel and charcoal to meet household energy needs. However, there are consequences for the environment and human health. Also, liquified petroleum gas (LPG) and biomass-based energy sources like charcoal, have continually been substituted for one another significantly, especially in Ghana. This is frequently brought on by price shocks and, more significantly, by sporadic shortages of LPG in the Ghanaian market. Additionally, low disposable incomes in urban and rural populations make the fuel transition from wood biomass less likely. It is necessary to investigate and utilise eco-friendly renewable energy sources, in order to relieve households of their need to purchase LPG regularly. Environmentally friendly and sustainable renewable energy alternatives, such as biogas from anaerobic digestion process is being recommended to address the issue.

Individual digestion units per family or community can provide a sustainable source of energy for rural and urban communities, as they can readily generate electricity, heat, and cooking gas. That notwithstanding, when designing and operating biogas units, it is crucial to ensure that there is adequate supply of water to moisten the organic matter and promote anaerobic digestion. Less or excess water can lead to a reduction in the efficiency of the process. Having stated that, it is imperative to gather as much data about the physical, biological, and chemical compositions of selected feedstocks. This is because, the characteristics of individual feedstocks and kinetics of degradation have immediate impact on biogas output, anaerobic degradation stability, and startup procedure. Further, data on feedstocks can be used to determine the theoretical methane potential (Browne and Murphy 2013; Angelidaki et al. 2009). Although the effectiveness and suitability of various anaerobic digestion feedstocks for biogas recovery have been well documented, indigenous feedstock characterisation is of utmost importance. The potency of any selected feedstock for energy recovery is influenced by the uniqueness of locations, atmospheric conditions, and nutrition (Fajobi et al. 2022). This highlights the need for appropriate characterisation of locally sourced feedstock to determine their eligibility as substrates for the anaerobic digestion process (Fajobi et al. 2022).

Human Excreta (HE), Food Leftovers (FLO), Kitchen Residue (KR) and Cow Dung (CD) are the feedstocks characterised in this study. It is crucial to determine their physico-chemical properties through standard procedures because of the variability in their availability, energy production methods, and limited information on their suitability as anaerobic co-digestion feedstocks. This study examines the emphasised feedstocks by considering proximate, ultimate, compositional, and mineral analyses. This finding will serve as a reference for biogas producers and stakeholders who desire to extract energy from the examined feedstocks.

2. Materials and Methods

2.1 Feedstock Collection and Preparation

Fresh HE (Figure 1a) was collected from a KVIP at Ayeduase in Kumasi, Ghana. FLO (Figure 1b) and KR (Figure 1c) were also collected from households of staff and the canteen at a Senior High School in Kumasi. Fresh CD (Figure 1d) was collected from the animal farm of the Department of Agriculture, KNUST. Components and compositions of FLO and KR are specified in Table 1. FLO, KR and CD were manually sorted to remove non-biodegradable fractions before organic fractions. They were further shredded into smaller pieces and homogenised into a slurry using a household food grinder. Samples were frozen until ready for use. The frozen samples were allowed to thaw at a temperature of 4 ℃ and used within a day to prevent biological decomposition.

Figure 1. Different feedstocks used in this study (a) Human Excreta (HE), (b) Food Leftovers (FLO), (c) Kitchen Residue (KR) and (d) Cow Dung (CD).

Table 1. Percentage composition of FLO and KR based on wet weight.

KR Components	Composition (% wet weight)	FLO Components	Composition (% wet weight)
Pawpaw peels	3.04	Rice	27.58
Watermelon peels	4.53	Cassava/Fufu	17.52
Avocado peels	3.12	Kenkey (cooked fermented corn dough)	14.66
Banana peels	2.59	Banku (cooked fermented cassava and corn dough)	12.80
Mango peels	4.00	Bread and Vegetables Sauce	4.19
Orange peels	5.87	Plantain	6.58
Pineapple peels	3.28	Kontomire (Boiled Cocoyam Leaves)	2.06
Onion peels	4.98	Gari (Cassava Flakes) and Beans	3.09
Lettuce, Cucumber, Pepper, Tomato, Carrot, Garden Eggs	8.48	Fish	0.12
Cocoyam peels	3.19	Egg	0.05
Plantain peels	12.47	Yam	11.35
Yam peels	20.91
Cassava peels	23.54

Alt Text: Fig. a. Green bucket containing fresh human excreta with ladle in it. Fig.b. White and pick bowl containing food leftovers such as rice, beans, gari and kontomire. Fig c. Left to Right: Pink bowls containing cassava, yam, coconut, plantain, pineapple, banana, pawpaw and avocado peels with cucumber, tomatoes, pepper and lettuce residue. Fig. d White bowl containing fresh, unhomogenized cow dung

2.2 Proximate Analysis

The APHA method 2540 B was used to determine the total solids (TS) content of the feedstocks using a Lanphan DZF-6090 (Figure 2a) drying oven (American Public Health Association APHA 1998). Also, APHA method 2540 E (American Public Health Association APHA 1998) was employed for volatile solids (VS) determination using a Lanphan Atmosphere Furnace-SA2–4-17TP (Figure 2b). The moisture content (MC) and Ash content (AC) of the feedstocks were estimated according to Equations 1 and 2 (Singh et al. 2021).

(1) $MC\hspace{0.17em}\left(\mathrm{\%}\right)\hspace{0.17em}=\hspace{0.17em}100\hspace{0.17em}-\hspace{0.17em}TS\mathrm{\%}$(1)

(2) $\mathrm{\%}AC=100-\left(\mathrm{\%}MC+\mathrm{\%}VS\right)$(2)

Figure 2. Setup and equipment utilized in this study (a)oven, (b) furnace, (c) potentiometric setup for alkalinity, (d) pH meter, (e) COD instruments and (f) ICP-MS.

Fig. a. Gray and greenish blue electric dryer with crucibles inside Fig.b. Closed blue and gray electric furnace. Fig c. Potentiometric setup for alkalinity determination showing a pH metre, hand with gloves using the ph metre, beaker containing some samples, burette stand and electronic stirrer. Fig d. Left to Right: pH metre placed in a white beaker with a plain 250-ml beaker containing homogenised cow dung samples placed beside it. Fig e. Hach COD HR+ (200–15000 mg/L) test vials, Hach heating device with vials placed in them and DR 3900 spectrophotometer with a single feedstock containing vial being read. Fig f. White and black ICP-MS placed in a laboratory setting.

2.3 Ultimate Analysis

Ultimate analysis was conducted at the Department of Agriculture and Department of Natural Resources, KNUST. The levels of carbon (C), hydrogen (H), nitrogen (N), oxygen (O), and sulphur (S) in the feedstocks were determined using standard procedures (American Public Health Association APHA 1998). The total amount of sulphur was quantified using the spectrophotometer method (Singh, Chhonkar, and Pandey 1999), and the percentage of total nitrogen was computed using the Kjeldahl method (Bremner 1965). Titrimetry (McLean 1965) was employed to determine the amount of hydrogen, and the Walkley-Black Wet Oxidation method was used to obtain the amount of organic carbon (Heanes 1984). Using Equation 3 (Fajobi et al. 2022), the oxygen content was calculated.

(3) $\mathrm{\%}O=100-\left(C+H+N+S+AC\right)\hspace{0.17em}\mathrm{\%}$(3)

2.3.1 C/N Ratio

The C/N ratios of the samples were calculated using Equation 4. (Anderson and Ingram 1993).

(4) $C/N=\frac{\mathrm{\%}\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}}{\mathrm{\%}\mathrm{N}\mathrm{i}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{g}\mathrm{e}\mathrm{n}}$(4)

2.4 Chemical Analysis

The alkalinity of each feedstock was determined adhering to the APHA method 2320B using potentiometric titration (Figure 2c) (American Public Health Association APHA 1998). A digital Hanner H1 98,136 pH metre (Figure 2d) was used to measure pH. The COD of each feedstock was also determined by adapting to the HACH COD method using the HACH COD HR+ (200–15000 mg/L) test vials and DR 3900 spectrophotometer (Figure 2e).

2.5 Compositional Analysis

2.5.1 Crude Fat Content

Samples of each feedstock was extracted with ether using method AOAC 2003.05 (AOAC 1990; 2006). Equation 5 was used to calculate the percentage of ether extract.

(5) $\mathrm{\%}EtherExtract=\frac{B}{C}\times 100$(5)

where B = ether extract weight, C = sample weight.

2.5.2 Crude Protein Content

Crude protein was calculated from nitrogen (N) determination using AOAC Method 984.13 (Association of Official Analytical Chemists AOAC 1995). A conversion coefficient of 6.25 was used to calculate protein concentration as shown in Equation 6.

(6) $\mathrm{\%}Crude\hspace{0.17em}Protein=Total\hspace{0.17em}Nitrogen\hspace{0.17em}\left(N_T\right)\times 6.25\left(ProteinFactor\right)$(6)

2.5.3 Crude Carbohydrate Content

Carbohydrates were calculated as the mass-balance difference of the crude fat, protein, moisture and ash determinations (AOAC 1990). Equations 7 and 8 provide the general formula.

(7) $\mathrm{\%}\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{h}\mathrm{y}\mathrm{d}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\hspace{0.17em}=\hspace{0.17em}\mathrm{T}\mathrm{S}\hspace{0.17em}-\hspace{0.17em}\left(\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{t}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{s}\hspace{0.17em}+\hspace{0.17em}\mathrm{f}\mathrm{a}\mathrm{t}\hspace{0.17em}+\hspace{0.17em}\mathrm{a}\mathrm{s}\mathrm{h}\right)$(7)

(8) $\mathrm{\%}\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{h}\mathrm{y}\mathrm{d}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}=\hspace{0.17em}100\hspace{0.17em}-\hspace{0.17em}\left(\mathrm{\%}\mathrm{M}\mathrm{C}\hspace{0.17em}+\hspace{0.17em}\mathrm{\%}\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{t}\mathrm{e}\mathrm{i}\mathrm{n}\hspace{0.17em}+\hspace{0.17em}\mathrm{\%}\mathrm{F}\mathrm{a}\mathrm{t}\hspace{0.17em}+\mathrm{\%}\hspace{0.17em}\mathrm{A}\mathrm{C}\right)$(8)

2.6 Mineral Analysis

Potassium (K) and sodium (Na) contents were analysed using flame photometry (Barnes et al. 1945). The calorimetric determination of phosphorus (P) was done using vanadium phosphomolybdate method specified by (1992). Calcium (Ca) and magnesium (Mg) contents were determined using atomic absorption spectroscopy (AAS) with model Buck Scientific VGP 210 (Katz and Jenniss 1983). PerkinElmer’s NexION 2000 ICP-MS (Figure 2F) was used to detect the amounts of nickel (Ni), molybdenum (Mo), chromium (Cr), cobalt (Co), cadmium (Cd), zinc (Zn), copper (Cu), selenium (Se), manganese (Mn) and iron (Fe) in the feedstocks.

2.7 Theoretical Methane Potential (BMP_TH)

The empirical relationship between the components of the feedstocks were determined using a modified Buswell equation by Boyle (1977), as shown in Equation 9.

(9) $\begin{array}{rl}& C_a{H}_b{O}_c{N}_d{S}_e+\left(a\hspace{0.17em}-\frac{b}{4}-\frac{c}{2}+\frac{3d}{4}+\frac{e}{2}\right)\times H_{2}O\hspace{0.17em}\\ & \to \hspace{0.17em}\left(\frac{a\hspace{0.17em}}{2}-\frac{b}{8}+\frac{c}{4}+\frac{3d}{8}+\frac{e}{4}\right)\times C{O}_2+\hspace{0.17em}\left(\frac{a\hspace{0.17em}}{2}+\frac{b}{8}-\frac{c}{4}-\frac{3d}{8}-\frac{e}{4}\right)\text{break}\times C{H}_4+d\times N{H}_3+e\\ & \times H_{2}S\end{array}$(9)

The theoretical methane yield was estimated using Equation 10 (Scherer, Arthur, and Antonczyk 2021; Fagbohungbe et al. 2015).

(10) $\mathrm{B}\mathrm{M}{\mathrm{P}}_{\mathrm{T}\mathrm{H}}=\hspace{0.17em}\frac{\left.⌊(\frac{a}{2}\right)+\left(\frac{b}{8}\right)-\left(\frac{c}{4}\right)-\left(\frac{3d}{8}\right)-\left(\frac{e}{4}\right)\rfloor \hspace{0.17em}.\hspace{0.17em}22400}{12a+b+16c+14d+32e}$(10)

3. Results and Discussion

3.1 Physico-Chemical Properties of Feedstocks

The physical and chemical characteristics of the selected feedstocks are summarised in Table 2.

Table 2. Physical, Chemical and Compositional Characteristics of HE, FLO, KR and CD (mean (standard deviation); n = 3).

	Analysis	HE	FLO	KR	CD
Proximate Analysis	TS (% wet weight)	11.32 (0.03)	25.65 (0.02)	9.42 (0.01)	24.71 (0.18)
	VS (% of TS)	81.02 (0.05)	96.89 (0.06)	88.59 (0.09)	85.29 (0.03)
	VS (%)	9.17 (0.02)	24.85 (0.004)	8.35 (0.02)	21.08 (0.16)
	VS/TS	0.81 (0.001)	0.97 (0.001)	0.89 (0.001)	0.85 (0)
	MC (% wet weight)	88.68 (0.03)	74.35 (0.02)	90.58 (0.01)	75.29 (0.18)
	AC (% wet weight)	2.15 (0.01)	0.80 (0.02)	1.08 (0.01)	3.63 (0.02)
Compositional Analysis	Carbohydrate (% dry weight)	NA	56.88 (0.04)	63.67 (0)	64.40 (0.02)
	Crude protein (%)	NA	19.58 (0.03)	11.82 (0.01)	12.56 (0.01)
	Crude fat (% dry weight)	NA	3.22 (0.02)	0.36 (0.01)	0.61(0.01)
Ultimate Analysis	Carbon (%)	44.92 (0.02)	46.93 (0.03)	44.03 (0.03)	52.71 (0.03)
	Hydrogen (%)	7.71 (0.02)	8.24 (0.02)	10.96 (0.04)	6.29 (0.02)
	Nitrogen (%)	5.36 (0.06)	2.12 (0.03)	1.89 (0.02)	2.01 (0.04)
	Sulphur (%)	0.32 (0.03)	0.18 (0.03)	0.33 (0.02)	0.59 (0.02)
	Oxygen (%)	39.55 (0.09)	41.73 (0.04)	41.71 (0.04)	34.76 (0.05)
	C/N	8.39 (0.09)	22.14 (0.26)	23.34 (0.25)	26.19 (0.47)

NA means Not Analyzed

3.1.1 Variability of Organic Matter in Feedstocks

The TS content of FLO (25.65 ± 0.02%) and CD (24.71 ± 0.18%) were higher than that of HE (11.32 ± 0.03%) and KR (9.42 ± 0.01%) as shown on Table 2. Nonetheless, higher TS values in FLO and CD may limit the mobility of methanogens, leading to longer retention times. FLO has been reported to have a TS range of 18.1–37.8% (Dhamodharan, Kumar, and Kalamdhad 2015), with a typical TS content of 20% for cooked food (Paritosh et al. 2017). Bodík and Miroslavakubaská (2014), emphasise that traditional food structure and composition variations affect the solid content of food waste from different locations. The TS content of KR in this study (9.42%) was as low as the value of 12.23% reported by Li et al. (2020) due to the high moisture content of kitchen residues like fruit and vegetable waste. Literature reports TS values of HE ranging from 14% to 37% (Singh et al. 2021), which is higher than the value obtained in this study. Also, the reported TS content range of CD is 20.0% to 32.8% (Singh et al. 2021), and the TS value of CD from this study (24.71%) falls within this range.

In addition, Table 2 shows the moisture content of HE, FLO, KR, and CD in this study. It was observed that HE and KR had higher moisture content than FLO and CD. This is beneficial for co-digestion, as it helps to maintain desirable moisture levels. The moisture content values reported in this study are similar to those reported by Singh et al. (2021) for HE (84.0%) and CD (66.0%). Zhang, Ouyang, and Lia (2012) reported a range of 69–93% for moisture content of FLO and KR. Studies have shown that the moisture content of biomass affects its calorific value (Ahmed et al. 2019). It is therefore crucial to operate within an optimal moisture content range as extremely high or low moisture content can negatively impact process performance.

Table 2 also shows the VS values of HE, FLO, KR, and CD. VS is an essential parameter in determining the organic content and energy potential of feedstocks. High VS values recorded for all feedstocks indicate the presence of readily biodegradable organic matter. Literature reported values for HE (81.0%) (Singh et al. 2021), FLO (90.7%–91.9%) (Paritosh et al. 2017; El-Mashad and Zhang 2010), and CD (88.0%–96.0%) (Singh et al. 2021) are consistent with values reported in this study. In addition, the VS/TS ratio is an important indicator of biodegradable content (Li et al. 2013), and FLO had the highest VS/TS ratio of 0.97. The higher VS of FLO explains its lower ash content in comparison to HE, KR, and CD. VS of TS and actual VS reported by Li et al. (2020) for KR were 85.94% and 10.51%, respectively. These values are quite close to that obtained in this work for KR (88.59% and 8.35). However, the actual availability of organic matter for biogas production is limited by microbial uptake for growth.

The carbohydrate, protein, and fat contents of HE, FLO, KR, and CD are presented in Table 2. Carbohydrates ranged from 56.88% to 64.40%, while protein content ranged from 11.82% to 19.58% of dry weight, and fat content ranged from 0.36% to 3.22% for FLO, KR, and CD, respectively. Fisgativa, Tremier, and Dabert (2016) documented the carbohydrate and protein contents of food waste to be 36.4%VS and 21.0%VS, respectively. High sugar concentrations can cause the rapid accumulation of volatile fatty acids (VFAs) and decreased pH in the biogas digester (Paritosh et al. 2017). High protein substrates, however, generate substantial amounts of methane (Hagos et al. 2017). Also, fats have a high biogas yield although long-chain fatty acid decomposition is complex.

3.1.2 Variability in Chemical Composition of Feedstocks

The C, H, N, S, and O contents were found to be in the range of 44.92 ± 0.02%−52.71 ± 0.03%, 6.29 ± 0.02% −10.96 ± 0.04%, 1.89 ± 0.02% −5.36 ± 0.07%,0.18 ± 0.03%-0.59 ± 0.02% and 34.76 ± 0.05%−41.73 ± 0.04%, respectively, for HE, FLO, KR and CD (Table 2). C and O had the highest contents for all feedstock types, while N and S recorded the lowest values. N and S contents are expected to be low during anaerobic digestion in order to reduce the quantity of trace gases (hydrogen sulphide, ammonia) produced. Correspondingly, the C/N ratios are reported on Table 2. Singh et al. (2021) documented C/N ratios of 12.0 and 24.0 for HE and CD respectively. Dhamodharan, Kumar, and Kalamdhad (2015) and Zhang et al. (2007) have also reported a C/N range of 14.7–36.4 for FLO. Generally, a C/N ratio of 20–30 gives a more stable AD process (Afifah and Priadi 2017). Contrarily, Guarino et al. (2016) reported an optimum C/N ratio range of 9 to 50 and the value for HE (8.39) was close to the lower threshold of the range. Although the C/N ratio for HE was low, that for FLO, KR and CD were within the recommended C/N range of 20 to 30 as stated by Scherer, Arthur, and Antonczyk (2021), for anaerobic digestion (Table 2). Research has proven that co-digestion of feedstock such as HE, FLO, KR and CD can help balance and maintain optimum C/N levels.

Alt Text: Fig. a. Blue bar graph plotting the levels of pH in HE, FLO, KR and CD. Plot shows that CD has the highest level. Fig.b. Blue bar graph plotting the levels of alkalinity in HE, FLO, KR and CD. Plot shows that CD has the highest level. Fig c. Blue bar graph plotting the COD content in HE, FLO, KR and CD. Plot shows that CD has the highest COD content.

Mean pH values of 4.91 ± 0.01 and 4.56 ± 0.01 in the acidic range were obtained for FLO and KR respectively, while HE and CD, with respective pHs of 7.21 ± 0.01 and 7.82 ± 0.02, were within the suitable range for AD (Figure 3a). The pH of HE was slightly above neutral (Figure 3a). Fanyin-Martin et al. (2017) reported a pH of 7.48 ± 0.33, 7.41 ± 0.36 and 7.87 ± 0.37 for HE from public septage, private septage and pit latrine, respectively. Also, Fisgativa, Tremier, and Dabert (2016), documented an average pH of 5.1 ± 0.7, for food waste from 65 different studies. The low pH recorded for food waste could be attributed to the possible presence of carbohydrate-containing food materials, which can be converted to volatile fatty acids (Pramanik et al. 2019). The low pH range is favourable for fermentative bacteria that could easily develop during the first few hours of the AD process. However, a higher pH is necessary for the digester to favour the development of methanogen microorganisms. In addition, pH values of 8.7 (Bah et al. 2014) and 7.67 (Egwu, Uchenna-Egwu, and Ezeokpube 2021) have been reported in literature for CD. These values are in the optimum range, just like what is reported in this study. Gashaw (2016) reported that reducing the pH of CD from 7.5 to 7.0 increased methane production by four times. Nonetheless, the AD process can tolerate a pH range of 6.6 to 8.0 (Gashaw 2014). When the pH level exceeds 8.5, it creates an unfavourable environment for methanogenic bacteria (Gashaw 2014).

Figure 3. (a) pH, (b) Alkalinity and (c) COD levels in HE, FLO, KR and CD.

Alkalinity values for HE, FLO, KR and CD for this study are shown in Figure 3b. The alkalinity represents the buffering capacity in the biogas production system to maintain pH. A major part of the alkalinity of a feedstock is required to buffer the CO₂, leaving only a small amount of ‘reserve alkalinity’ to neutralise the VFAs. A high alkalinity value allows the system to absorb the VFAs produced, without leading to sharp decrease in pH (Gómez-Quiroga et al. 2020). Alkalinity values of 1200 mg CaCO₃ eq./L for KR (Li et al. 2013), 825 mg CaCO₃ eq./L for FLO (Chen, Zhang, and Wang 2015), 980 mg CaCO₃ eq./L for sludge (Chen, Zhang, and Wang 2015), 19550 mg CaCO₃ eq./L for CD (Egwu, Uchenna-Egwu, and Ezeokpube 2021), and 38,050 mg CaCO₃ eq./L for CD (Gómez-Quiroga et al. 2020) have been documented. It is clear from this study and that of other researchers that CD has high alkalinity and could serve as a good buffer source when used as co-substrate or inoculum source during the AD process. However, the alkalinity values for FLO (630 mg/L) and KR (589 mg/L) in this study do not fall within the optimal range; hence, the need for co-digestion with high alkalinity feedstock. Filer, Ding, and Chang (2019) recommend that alkalinity be kept at around 3000 mg/L to maximise methane yield. However, Scherer, Arthur, and Antonczyk (2021) recently found that alkalinity of 10,000 mg CaCO₃ eq./L yielded almost 100% biodegradation of organics.

Most likely, the COD strength of a feedstock has a significant impact on the final amount of biogas and methane yields. As shown in Figure 3c, the high COD values obtained for HE, FLO, KR and CD show that the feedstocks have great potential during biogas generation. COD value was highest in CD (258115 mg/L), followed by FLO (187730 mg/L), KR (158327 mg/L) and HE (87682 mg/L) in this study. COD levels reported in literature for HE ranged from 800 to 92,600 mg/L (Ahmed et al. 2019; Kim, Kim, and Lee 2019; Fanyin-Martin et al. 2017). Moreover, some authors documented a COD range of 143,000–510,000 mg/L for FLO (Kim, Kim, and Lee 2019; Bodík and Miroslavakubaská 2014; Fisgativa, Tremier, and Dabert 2016). Furthermore, Singh et al. (2021) reported a COD concentration of 280,000 mg/L for CD.

3.1.3 Variability in Mineral Composition of Feedstocks

Trace elements are essential for microbial growth and have been reported to improve AD operation even in reactors with high organic loadings and contribute to reduction in the VFAs (Arthur and Scherer 2020). Sodium (Na), calcium (Ca), potassium (K), phosphorus (P), and Magnesium (Mg) are essential constituents of biomass that maintain the metabolic activities of microorganisms in AD. For the purposes of green energy generation, effluent and solid sludge reuse, it is essential to analyse the presence of indigenous micro- and macro-nutrients in feedstocks prior to the commencement and during of AD process (Arthur and Scherer 2020). The macronutrient content (P, K, Ca, Mg and Na) for HE, FLO, KR and CD in this study ranged between 17.63 mg/L and 4184.83 mg/L (Figure 4).

Figure 4. Phosphorus, potassium, calcium, magnesium and sodium content in HE, FLO, KR and CD.

Alt Text: Bar chart with light orange, light green, purple, yellow and light blue colours, respectively, showing phosphorus, potassium, calcium, magnesium and sodium content in HE, FLO, KR and C. Significantly, FLO has high levels of macronutrients.

In this study, FLO had the highest Na (4184.83 mg/L), P (1902.67 mg/L), Ca (2308.33 mg/L) and K (3390.00 mg/L) levels, while CD had the highest Mg (543.33 mg/L) level (Figure 4). Na, K and Ca are more prevalent in FLO and may contribute to salt inhibition. For K, Mg, Na, P and Ca content in FLO, Fisgativa, Tremier, and Dabert (2016) reported mean values of 12,000.00, 2000.00, 22000.00, 5000 and 16,000 mg/L, respectively. However, the K, Mg, Na, P and Ca levels of FLO in this study (Figure 4) were lower. Furthermore, Ahmed et al. (2019) reported Ca, Mg, Na and K values of 90.00, 10.00, 530.00 and 710.00 mg/L, respectively, while Fagbohungbe et al. (2015) documented 20,700.00, 2.00, 900.00 and 890.00 mg/L, respectively, for HE. These values are higher than what were obtained in this study (Figure 4). For pit, public and private septage, Fanyin-Martin et al. (2017) obtained 520.00, 230.00 and 140.00 mg/L, respectively, for phosphorus. The phosphorus level for this study (292.33 mg/L) lies within the range reported by (Fanyin-Martin et al. 2017). Nonetheless, the presence of Na, K, Mg, and Ca can be inhibitory and toxic at certain concentrations. Na inhibits at a threshold concentration between 8000 mg/L and 12,000 mg/L (Anwar et al. 2016). However, after microorganism adaptation, concentrations up to 15,000 mg/L are tolerated (Speece 1983). Conversely, at concentrations of 350–400 mg/L, Na creates an ideal environment for methanogens. On the other hand, the presence of Ca has a threshold value of 7000 mg/L (Lo et al., 2012), with the optimum concentration being between 150 and 300 mg/L (Paritosh et al. 2017). Also, the potassium (K) inhibition threshold is around 7500 mg/L (Chen and Cheng 2007).

At relatively low concentrations, micronutrients (trace metals) are critical cofactors in numerous enzymatic reactions involved in the biochemistry of methane formation (Arthur et al. 2022). Enzymes such as hydrogenase (containing Fe and or Ni) and formate dehydrogenase (containing Fe, Se, and Mo) release electrons from H₂ and HCOOH during interspecies hydrogen/formate transfer (Banks et al. 2012). The Fe, Ni, Zn, Cr, Co, Cu, Cd, Mo, Mn and Se levels of HE, FLO, KR and CD in this study are summarised in Table 3. For all trace elements, a suitable concentration range between the maximum nutrient requirements and inhibition is established. In this study, Fe, Zn and Mn for all feedstocks lie outside the stimulatory concentration range, while Ni, Cr, Co, Cu, Cd and Se lie within (Table 4). Mo lies within the stimulatory concentration range for FLO and KR, but lies outside the range for CD and HE. The micronutrients in HE are in the order Fe>Zn>Mn>Cu>Mo>Cr>Ni>Co>Se>Cd, whereas those in FLO are in the order Fe>Zn>Mn>Cu>Ni>Cr>Mo>Co>Cd=Se. KR on the other hand, have micronutrients in the order Zn>Fe>Mn>Cu>Ni>Cr>Mo>Cd>Co=Se and CD, in the order Mn>Fe>Zn>Cr>Mo>Ni>Cu>Co>Cd=Se. Lin (1992) demonstrated that the relative toxicity of heavy metals to acetic acid degradation in mesophilic anaerobic digestion of sewage sludge was Cd>Cu>Cr=Zn>Pb>Ni. Evaluating heavy metal toxicity during anaerobic digestion of sewage sludge, Ahring and Westermann (1985) revealed severe inhibition at various concentrations for certain heavy metals, such as 70 to 400 mg/L for Cu, 200 to 600 mg/L for Zn, and 10 to 2000 mg/L for Ni.

Table 3. Micronutrient Characteristics of HE, FLO, KR and CD (mean (standard deviation); n = 3).

Analysis	HE	FLO	KR	CD
Iron, Fe (mg/L)	2.64 (0.001)	11.86 (0.004)	5.64 (0.003)	14.18 (0.001)
Nickel, Ni (mg/L)	0.34 (0.001)	0.09 (0.001)	0.06 (0.001)	1.43 (0.01)
Zinc, Zn (mg/L)	1.36 (0.001)	7.40 (0.001)	9.76 (0.005)	1.29 (0.0004)
Chromium, Cr(mg/L)	0.36 (0.001)	0.06 (0.002)	0.03 (0.001)	4.32 (0.001)
Cobalt, Co (mg/L)	0.013 (0.0003)	0.005 (0)	0.004 (0)	0.203 (0.001)
Copper, Cu (mg/L)	0.95 (0.001)	0.23 (0.001)	0.36 (0.001)	1.25 (0.01)
Cadmium, Cd (mg/L)	0.003 (0)	0.002 (0)	0.004 (0)	0.01 (0.001)
Molybdenum, Mo (mg/L)	0.58 (0.001)	0.03 (0.001)	0.02 (0.001)	3.04 (0.01)
Manganese, Mn (mg/L)	0.23 (0.0001)	0.59 (0.01)	0.39 (0.01)	1.84 (0.001)
Selenium, Se (mg/L)	0.004 (0)	0.002 (0)	0.002 (0)	0.015 (0.0001)

Table 4. Reported stimulatory and inhibitory concentrations of metals on anaerobic biomass (expanded from Romero-Güiza et al. (2016)) compared with values from this study.

Trace Element	Values from this study (mg/L)	Stimulatory Concentration (mg/L)	Inhibitory Concentration (mg/L)	References
Ca	547.50–2308.33	100<Ca<1035	300<Ca<8000	Lo et al. (2012)
Mg	17.63–543.33	<720	NR	Lo et al. (2012)
Na	588.33–4184.83	100<Ca<350	3500<Na<8000	Lo et al. (2012)
K	494.50–3390.00	<400	400<K<28934	Lo et al. (2012)
Fe	2.64–14.18	<0.3	NR	Worm et al. (2009)
Ni	0.06–1.4	0.03<Ni<27	35<Ni<1600	Arthur et al. (2022) Altaş (2009) Fermoso et al. (2009)
Zn	1.29–9.76	0.03<Zn<2	7.5<Zn<1500	Altaş (2009) Li and Fang (2007)
Cr	0.03–4.32	0.01<Cr<15	27<Cr<2500	Altaş (2009) Li and Fang (2007)
Co	0.004–0.203	0.03<Co<19	35<Co<950	Fermoso et al. (2009) Arthur et al. (2022)
Cu	0.23–1.25	0.03<Cu<2.4	12.5<Cu<350	Altaş (2009) Li and Fang (2007)
Cd	0.002–0.01	<1.6	36<Cd<3400	Altaş (2009) Li and Fang (2007)
Mo	0.02–3.04	<0.05	NR	Worm et al. (2009)
Mn	0.23–1.84	<0.027	NR	Worm et al. (2009)
Se	0.002–0.015	<0.04	NR	Worm et al. (2009)

NR means Not Reported.

Ni, Co, and Fe, have received the most attention in recent studies because they are essential cofactors of carbon monoxide dehydrogenase and other enzymes involved in acetoclastic methanogenesis (Choong et al. 2016). Fe is used in the transport system of methanogenic bacteria to convert CO₂ to CH₄, and it serves as both an electron acceptor and donor (Choong et al. 2016). Fe also acts as a binding component in sulphide precipitation, controlling the level of hydrogen sulphide in the biogas (Choong et al. 2016). Also, optimum Fe content in AD is likely to increase the rate of methane formation by activities of microorganisms such as Methanosarcina barkeri (Lin 1992). Cobalt (Co), a metal-ligand for vitamin B12, influences methyl transferase activity, a methyl transport component. This Co property allows microbes to degrade methanol (Romero-Güiza et al. 2016). Furthermore, Ni serves as a core element for coenzyme F430, which is involved in autotrophic methanogenesis (Choong et al. 2016). Zn acts as a structural ion in the transesterification factor and is involved in the function of enzymes involved in methanogenesis, such as coenzyme M methyltransferase ((Romero-Güiza et al. 2016).

On the other hand, Cu is required for coenzyme Q and biological electron transport (Fermoso et al. 2009). Mn is an electron acceptor in anaerobic respiration processes and Mo, is found in enzymes like formate dehydrogenase (FDH), which catalyzes formate production by propionate oxidisers (Banks et al. 2012; Fermoso et al. 2009). Schmidt et al. (2014) reports a rapid accumulation of VFAs when Fe and Ni are depleted, whereas Co and W have long-term effects. Fermoso et al. (2009) illustrated the fundamental role of these micro-nutrients by demonstrating their interactions with microbe cells. Overall, the elements in methanogens cells were in the following order Fe>Zn>Ni>Cu=Co=Mo>Mn. Also, Schönheit, Moll, and Thauer (1979) discovered that Methanobacterium thermoautotrophicum grew in response to trace elements of Fe>Ni>Co=Mo. Zhang et al. (2015) documented the effects of Fe (5.0 mg/L), Co (1.0 mg/L), Ni (1.0 mg/L), and Se (0.2 mg/L) on the AD of FLO. The authors reported that without trace elements, a VFA concentration of 30,000 mg/L inhibited methane production. In contrast, the digesters with added trace elements had a stable performance with a high methane yield of 465.4 mL CH₄/gVS. For further justification, Zhang, Zhang, and Li (2015) demonstrated that trace elements (Fe, Co, Mo and Ni) supplementation recovered unstable mono-digestion of food waste from process imbalance, as evidenced by increased CH₄ yields from 384.1 to 456.5 mL CH₄/gVS added, decreased the concentration of propionate from 899.0 to 10.0 mg/L, and increased pH from 6.9 to 7.4.

3.2 Theoretical Methane Potential (BMP_TH) and Biogas Potential (BGP_TH)

The molecular formulas and product equations for HE, FLO, KR and CD summarised in Table 5 were determined using the results from the ultimate analysis. The BMP_TH and BGP_TH of HE, FLO, KR and CD were subsequently calculated (Table 5). The percentage methane composition lay within 49.9%−65.3%. The BGP_TH and BMP_TH usually assumes that 100% of the substrate is biodegradable, but in reality, only 40%−90% of the material is converted into biogas (Curry and Pillay 2012). Fagbohungbe et al. (2015) reported biomethane yields of 290 mL/gVS and 566 mL/gVS for substrate to inoculum ratios of 0.5 and 1, respectively, during AD of HE. The methane yield of HE in this study therefore lies within the range reported by (Fagbohungbe et al. 2015).

Table 5. Theoretical Biogas and Bio-Methane Potential of HE, FLO, KR, and CD.

Feedstock	Molecular Formula	Product Equation	BGPTH mL/gVS	BMPTH mLCH4/gVS	CH4 (%)
HE	^C3.74 ^H7.65 ^O2.47 ^N0.38 ^S0.01	2.06CH4 + 1.67CO2 + 0.38NH3 + 0.01H2S	946.93	472.50	49.90
FLO	^C3.91 ^H8.17 ^O2.61 ^N0.15 ^S0.01	2.27CH4 + 1.64CO2 + 0.15NH3 + 0.01H2S	918.72	512.05	55.74
KR	^C3.67 ^H10.87^O2.61 ^N0.13 ^S0.01	2.49CH4 + 1.18CO2 + 0.13NH3 + 0.01H2S	864.09	563.91	65.26
CD	^C4.39 ^H6.24 ^O2.17 ^N0.14 ^S0.02	2.37CH4 + 2.02CO2 + 0.14NH3 + 0.02H2S	1058.94	552.27	52.15

Also, Zhang et al. (2007) documented methane outputs of 348 and 435 mLCH₄/gVS for FLO after 10 and 28 days of digestion, respectively. Similar methane production from FLO was measured by other authors in the range of 401–529 mL CH₄/gVS (Browne and Murphy 2013; El-Mashad and Zhang 2010). Additionally, Ebner et al. (2016) reported bio-methane potentials for FLO and KR ranging from 165 to 496 mL CH₄/gVS. The highest methane production was in materials rich in lipids or rapidly degradable carbohydrates. The biomethane potential for FLO in this study lies within the range reported in literature, while KR in this study is higher than the range reported. This is, however, expected because of the variability of food waste. Furthermore, Sandhu and Kaushal (2022b) after digesting breeding manure, reported optimum values of 1104.77 ml and 1465.22 ml for methane and biogas, respectively.

3.3 Potential, Challenges and Justification of HE as Main Substrate for Anaerobic Co-digestion in Households

HE was chosen as substrate in this study because of its availability in every household and its ability to be easily digested anaerobically. From the results of this study and other studies, HE possesses very important nutrients that can support its valorisation. Also, the pH and alkalinity lie within the optimum range for a successful AD process. The methane content reported in this study for HE (53.2%) is low. Similarly, a low methane yield of 48% was observed when Gao et al. (2019) treated black water with relatively lower free ammonia concentrations of 26 and 60 mg/L. Also, the C/N ratio of 8.39 obtained for HE in this study was very low. This is similar to the low C/N ratio of 7.9 reported by Afifah and Priadi (2017). That notwithstanding, HE can be co-digested with co-substrates (FLO, KR or CD) higher in C/N ratio. Finally, it is recommended to connect HE directly to the biogas system without any direct human contact due to the high bacteria load.

3.4 Potential, Challenges and Justification of FLO and KR as Co- Substrates for Anaerobic Co-digestion in Households

The composition of FLO and KR strongly depends on different eating and cooking habits. Hence, it could be said that their characteristics vary from place to place. From this work, the C/N ratios of FLO (22.1) and KR (23.3) were found to be within the optimum range for AD. However, the pH (4.9 and 4.6) and alkalinity (630 and 590 mg/L) values for FLO and KR, respectively, were significantly low. As these values cannot support a stable AD process, FLO and KR are recommended to be used as potential co-substrates for household biogas generation. With this, a substrate like HE with optimum alkalinity and pH level could be added during the digestion of FLO and KR. Optimal methane and biogas values of the co-digestion of FLO and substrates like algae, chicken, fish mixed and cow manure have been reported to be 1345.97 ml, and 2244.58 ml, respectively (Kaushal, Sandhu, and Soni 2022). Also, optimum values of cumulative biogas and methane were found to be 3401.8 ml and 2266.3 ml, respectively, when KR such as apples, vegetables, fruit pulp wastes as well as algae, pond sludge and CD were co-digested (Sandhu and Kaushal 2022a). FLO and KR are better of co-substrates because, even though they are readily available in large amounts in households, it is possible that there is competition for the use of FLO and KR as feed for household animals.

3.5 Potential, Challenges and Justification of CD as Inoculum Source for Anaerobic Co-digestion in Households

There are two major advantages of using CD for co-fermentation or as inoculum. First, it provides nutrients like trace metals, vitamins, and other substances needed for microbial growth (Gashaw 2014). This is confirmed by the mineral analysis of CD in this work. Secondly, it helps to balance pH and increase buffering ability (Gashaw 2014). The pH (7.8) and alkanity (15730 mg/L) of CD in this study are within the optimum range and can support a successful AD process. The high buffering capacity of CD makes the process more resistant to VFA accumulation and thus mitigates inhibition processes (Gashaw 2014). Further, Font- Palma (2019), reported that the C/N ratio of CD fell into the optimal range (15–30) for AD. Similarly, the C/N ratio obtained for CD (26.2) in this study is within the optimal range. Because of the above characteristics, the use of CD as inoculum or co-substrate during the AD process is justified.

4. Conclusions

The suitability of HE, FLO, KR and CD for household biogas production has been established since all feedstocks contain readily available biodegradable components that can easily be converted to biogas. However, HE, proposed as the main substrate for household biogas generation, has a very low C/N ratio, which could lead to low AD performance. Hence, using small portions of FLO and KR as co-substrates will balance the C/N ratio during co-digestion. Also, CD as inoculum will be a good source of microbial community and buffer. This information was established through the results obtained for the various characterisations done in this study, which mostly met the requirements for suitable AD feedstock(s) available in literature. Therefore, HE, FLO, KR and CD are recommended to be very well developed as feedstock sources for household biogas generation.

Data availability statement

Data are available within the article or its supplementary materials.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Regional Water and Environmental Sanitation Centre Kumasi (RWESCK) at the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi with funding from the Ghana Government through the World Bank under the Africa Centers of Excellence project.

Notes on contributors

Blissbern Appiagyei Osei-Owusu

Blissbern Appiagyei Osei-Owusu is an Environmental Sanitation and Waste Management PhD. Candidate at the Regional Water and Environmental Sanitation Centre, Kumasi (RWESCK), Department of Civil Engineering, KNUST.

Martina Francisca Baidoo

Dr. Martina Francisca Baidoo has been working in the Department of Chemical Engineering, KNUST since November, 2016. She has gained in-depth knowledge in catalysis and petrochemistry from the Norwegian University of Science and Technology. Her research interests include waste recycling and valorization, biofuels and biomaterials, catalysts and its application in different areas.

Richard Arthur

Professor Richard Arthur is the dean for the faculty of engineering at Koforidua Technical University, Ghana. His general research areas include biotechnology and resources management, application of remote sensing techniques for assessing potential bioenergy resources in developing countries, anaerobic digestion of agricultural residues and OFMSW, solid and liquid waste treatment, biomass conversion technologies, water quality and climate change. His current research interest looks at trace elements requirements and impact on anaerobic digestion; biogas production from water hyacinth and other aquatic plants; optimization and stabilization of full-scale biogas plants.

Sampson Oduro-Kwarteng

Professor Sampson Oduro-Kwarteng is the director for the Regional Water and Environmental Sanitation Centre, Kumasi (RWESCK), KNUST. He has 25 years of experience as a Civil, Water & Sanitation Engineer. He is a Full Professor in Water and Environmental Sanitation Engineering in the Civil Engineering Department at Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. He is involved in water and environmental sanitation designs, services and construction supervision including: design of small towns water supply systems; Water, Sanitation, Hygiene (WASH) training and community participatory learning and action; WASH Behavioral Change and Management Training; assessment of capacity of public and private sector firms involved in water and environmental sanitation; and feasibility studies and designs of water and environmental sanitation projects.