1. Introduction
Biomass is the main source of energy in the rural areas of most developing countries, and globally biomass contributes approximately 14% of the world’s energy supply [
1,
2]. International commitments to Sustainable Development Goal 7, which is to “Ensure access to affordable, reliable, sustainable and modern energy for all”, combined with a continued shift away from fossil fuels and increasing populations, ensure that biomass will remain a major source of energy in the future. Biomass and charcoal are used extensively for cooking in the Republic of Congo, resulting in poor air quality and associated health impacts. This firewood and charcoal is often unsustainably sourced from the Congo Basin forest, resulting in deforestation and reduced biodiversity and carbon storage capacity [
3]. The identification of alternative biomass feedstocks can relieve the pressure on these forests and may also promote the development of alternative conversion routes such as using biogas for cooking.
The physio-chemical properties which significantly affect the choice of biomass for conversion to bioenergy include biochemical composition (carbohydrate, protein and lignin content), elemental content (C, H, N, S, O), calorific value (HHV) and proximate analysis (moisture, volatile, fixed carbon and ash content) [
2]. These properties not only determine the suitability of a conversion process, but also influence the cost of the conversion technology. Characterisation of biomass is thus a crucial step when assessing new feedstocks for bioenergy production. The following tools are routinely used for its characterisation: elemental analysis to determine the ultimate analysis (C, H, N, S, O), proximate analysis to determine its moisture, volatile fixed carbon and ash content determination, X-ray fluorescence (XRF) spectroscopy, AAS or ICP-MS to determine metals and mineral matter and gas chromatography mass spectroscopy (GC-MS) to determine lipid profiles. Another important technique used to predict the thermal behaviour of biomass during pyrolysis or combustion is thermogravimetric analysis (TGA), in which the mass loss of the feedstock is determined as a function of temperature, under controlled heating rates and atmospheric conditions [
4].
Among the most popular biomass feedstocks used for bioenergy production are woods, agricultural wastes, industrial residues, municipal solid waste, and sawdust [
5]. Aquatic plants such as algae have recently emerged as potential feedstocks for biofuel production, largely due to their high lipid and carbohydrate contents [
6,
7,
8]. For large-scale production purposes, algae can be cultivated in both freshwater and marine environments, in either open culture systems such as lakes, ponds or basin raceways; or in highly controlled closed culture systems called photobioreactors [
5]. Focus has mainly been directed towards the use of microalgae species; however, macroalgae and other freshwater aquatic macrophytes have also received attention in recent years [
9,
10]. One such macroalgae is
Ulva spp., including
Ulva lactuca, which can be found throughout the marine environments of the Republic of Congo.
Ulva lactuca is the most abundant macroalgae in the coastal waters of Alexandria, Egypt [
11], and represents an unexplored natural resource with potential economic value for use in human and animal nutrition and as a potential biofuel resource [
12].
U. lactuca has many potential applications, for instance, it is edible and is a source of essential amino acids; it is also a source of bioactive compounds [
13]. The utilisation of
Ulva lactuca as a feedstock for bioenergy has also been investigated widely by a number of researchers. Bikker et al. [
14] presented a biorefinery approach utilising
Ulva lactuca for the production of animal feed, chemical and biofuels. A sugar-rich hydrolysate containing 38.8 gL
−1 sugars and a protein-enriched fraction containing 343 g/kgDM
−1 protein was obtained following hot water treatment and enzymatic hydrolysis. The sugar fraction can be fermented to produce bioethanol and the protein fraction was proposed as a promising source of essential amino acids. From an energy perspective, direct combustion of
U. lactuca appears an unsuitable conversion route for the generation of bioenergy, due to the inherent high moisture and ash contents of the biomass. In particular, high concentrations of alkali metals can prove to be problematic in the thermal conversion of
U. lactuca [
15], resulting in a severe risk of slagging and fouling. As a result, biological processing has been identified as a more suitable conversion route, due to an increased tolerance for high moisture and high ash feedstocks [
15]. The production of both bioethanol and biogas have been investigated for
Ulva lactuca [
16]. However, biogas production is considered a more feasible conversion route due to the complete degradation of macromolecular structures (carbohydrates, lipids and proteins), rather than carbohydrates only, providing a greater energy output [
17]. The biomethane yields obtained from
U. lactuca range from 157–271 mL CH
4 gVS
−1 [
15,
18]; although the biodegradability is typically low (38%–43%) [
18], potentially linked to its low C:N and high sulphur contents, causing an inhibitory effect for anaerobic digestion [
19].
Another green aquatic plant with the potential for biofuel production is
Ledermanniella schlechteri (LS); belonging to the
Podostemaceae plant family [
20]. This green aquatic plant grows abundantly in the falls of the river Djoué, one of the tributaries of the Congo River. This aquatic plant is commonly called Michiélé [
21] and is currently used by a minority of the population as a food in the Southwest population of Brazzaville. It is rich in nutrients and is commonly eaten in tropical Africa. Mata et al. [
20] have evaluated the concentrations of toxic metals in
Ledermanniella schlechteri and their potential health risks to consumers. Metal levels in
Ledermanniella schlechteri were compared with international regulations for human consumption set by the Food and Agriculture Organization (FAO) and the World Health Organization (WHO) and were found to exceed permissible limits for human consumption. Mata et al. [
20] demonstrated that metal concentrations in
Ledermanniella schlechteri varied significantly across different sampling sites. The average values (in mg/kg) ranged from 0.5–9.0 (Cr), 0.2–4.5 (Ni), 5.5–78.4 (Cu), 336–1520 (Zn), 0.1–0.5 (As), 0.25–0.8 (Cd), 0.4–11.8 (Pb) and 0.02–0.24 (Hg). Across all sampling sites, the average concentration of Zn, As, Cd and Hg exceed the FAO/WHO’s permissible limits for human consumption. The consumption of plants contaminated by heavy metals may lead to cancer, anemia and male infertility, as well as cardiovascular, nervous and lung diseases [
20]. If biomass is contaminated and unfit for human consumption, it may still be possible to use it as a feedstock for bioenergy, exploiting the value of this natural resource. However, little is currently known about the behaviour of
Ledermanniella schlechteri as a feedstock for bioenergy generation.
For the sustainable production of biofuels it is important to take into account the availability and suitability of potential biomass feedstock resources in a regional context. Potential aquatic biomass in the Congo region, such as UL and LS, has not yet been characterised in the literature. Both UL and LS are abundant in tropical Africa, so this work could have wider implications for identifying future feedstocks for generating bioenergy. The present work is primarily focused on determining the physio-chemical composition of Ulva lactuca macroalgae and the aquatic macrophyte Ledermanniella schlechteri, which are abundant, respectively, in the coastal marine environment and the rivers of the Republic of Congo. Secondly, this study evaluates the use of these alternative biomass resources as a possible feedstock for the production of bioenergy in the Congo region, by assessing both thermochemical and biological conversion routes.
2. Materials and Methods
The marine macroalgae identified as
Ulva lactuca (UL) was collected directly from the Ocean at Pointe Noire (Pointe Indienne and Matombi; nomenclated UL1 and UL2, respectively). The freshwater aquatic macrophyte
Ledermanniella schlechteri (LS) biomass samples were collected in Brazzaville (in two different sites of the Djoué River for LS1 and LS2, respectively). The physical appearances of UL and LS are shown in
Figure 1. The sampling dates and grid references of the sampling sites are listed in
Table 1.
Each biomass was dried using a solar-dryer and oven for three days at approximately 70 °C. After the drying process, samples were ground using two steps, (i) a common blender and (ii) a Retsch CryoMill (Retch, Haan, Germany), to obtain a fine powder. The ground samples were sieved to obtain particle size of <100 µm and stored in the dark until further characterisation.
Ultimate analysis was performed to determine the elemental (C, H, N, S) content of the biomass using a CHNS Elemental Analyser (Flash 2000, Thermo Fisher Scientific, Waltham, MA, USA) following the protocols of Thermo Fisher Scientific [
7]. Oxygen content was calculated via the difference method. Proximate analysis was performed to determine the moisture, fixed carbon, volatile matter and ash content using a METTLER TOLEDO TGA/DSC 1 (Mettler Toledo, Columbus, OH, USA). A 10 mg sample of each feedstock was heated up to 105 °C at a rate of 10 °C min
−1, held isothermally for 9 min under an N
2 atmosphere to determine the volatile matter content. Finally, the atmosphere was changed to air to burn off the samples for the determination of the fixed carbon and ash content. Total solids (TS) and volatile solids (VS) contents were determined gravimetrically via drying at 105 °C and subsequently ashing at 550 °C [
22].
Biochemical analysis was performed using a modified Van Soest method [
23] to determine the cellulose and lignin content. Lipid analysis was determined via Soxhlet extraction in hexane following the method described by Bi and He [
6], followed by evaporation of the solvent using a Vacuum Controller V-800 (BÜCHI Rotavapor R-205, BÜCHI, Flawil, Switzerland). Protein content was determined using the DUMAS method using a nitrogen-to-protein conversion factor of 5.13 [
24]. The total carbohydrate content was determined by the difference between 100-ash + protein + moisture + lignin. The higher heating value (HHV) was determined using bomb calorimetry (Parr Model 6200, Parr Instrument Company, IL, USA) according to BS ISO 1928:2009.
Inorganic analysis was performed using XRF spectroscopy (ZSX Primus II, Rigaku, Tokyo, Japan), operating at a 4.0 kW Rh anode (50 kV, 50 mA). Then, 2.7 g of each sample in powder form and 0.3 g of binder (BM-0002-1 CEREOX, Fluxana, Bedburg-Hau, Germany) was mixed in a plastic container using a vortex mixer for 4–6 min and sieved for making press pellets. The slagging and fouling behaviour of the biomass was determined according to predictive indices: the alkali index (AI), bed agglomeration index (BAI), acid–base ratio (Rb/a), slagging index (SI), fouling index (FI) and slag viscosity index (SVI); as described previously. Indices were calculated based on the inorganic oxide content of the biomass, determined via XRF analysis; further details on the calculations can be found here [
10,
25]. The interpretation of each index is described in
Table 2.
Fatty acid methyl ester (FAME) analysis was performed on the extracted lipid from both LS and UL, following derivatization using a GC-MSQP2010 SE (Shimadzu, Kyoto, Japan). Derivatization was performed via the addition of 200 µL 2:1 chloroform/methanol and 300 µL 0.6 M HCl in methanol to 5–30 mg of extracted lipid in a 2 mL vial. The vials were sealed and placed on a hot plate for one hour at 70 °C. Once cooled, 1 mL hexane was added to each vial and after vigorous shaking, the two solvent layers formed were allowed to separate. Fifty microliters of the top (organic) layer was added to 950 µL hexane and 20 µL international standard (C:17, 16.3 mg/mL in hexane).
Theoretical biochemical methane potential (
TBMP) was calculated stoichiometrically, based on the elemental composition (C, H, N and O) of the biomass, which was applied to Boyle’s Equation [
26]. Equation (1) describes Boyle’s Equation, where coefficients
a,
b,
c and
d represent the molar fractions of C, H, O and N, respectively.
Experimental biochemical methane potential (
EBMP), measured using an AMPTS II (Bioprocess Control, Lund, Sweden), was maintained at 37 °C for a 30-day incubation period. A 2:1 inoculum-to-substrate ratio was used by diluting samples to 10 gVSL
−1 and inoculum to 20 gVSL
−1, using distilled water. Two hundred milliliters of each was added to the reactors, leaving a 100 mL headspace. Blank reactors containing only inoculum (200 mL, 20 gVSL
−1) and 200 mL distilled water were run simultaneously, to account for the residual methane emissions from the inoculum.
EBMP values were expressed as (mL CH
4 gVS
−1). More details on the methodology can be found here [
10]. The headspaces of
EBMP reactors were flushed with nitrogen before starting the test, to ensure anaerobic conditions. Inoculum was collected from an active digester (Esholt WWTP, Yorkshire, UK), during steady-state operation. The inoculum was passed through a 2-mm screen to remove large particulates and stored at 4 °C, until required. The inoculum was pre-incubated at 37 °C for approximately 2 days before the test, to reduce enteric methane emissions. A particle size of <1 mm was used for each biomass during the
EBMP tests.
The biodegradability index (BI) was determined according to Equation (2) [
27]. The digestion kinetics of the
EBMP curves were described using the modified Gompertz model [
28] described in Equation (3). Here,
Hm is the maximum biomethane yield (mL CH
4 gVS
−1),
Rm is the peak biomethane production rate (mL CH
4 gVSd
−1),
λ is the lag-phase time (d),
t is time (d) and
e = 2.71828.
Hm,
Rm and
λ were estimated using the Solver Function in Microsoft Excel, via the least-squares method [
29]. The accuracy of the modified Gompertz model was determined through a squared correlation coefficient (R
2), comparing experimental and model data. The peak time of fermentation (
Tm), Equation (4) [
28], was predicted using parameters from the modified Gompertz model. Finally, the technical digestion time (T
80) was used to describe the time taken to generate 80% of the total
EBMP [
30].