1. Introduction
The worldwide economic advancements and population growth have been contributing to the increased demand for the electricity generation capacity. About 82% of the gross inland energy consumption in the world still derives from petroleum (32%), coal (28%), and natural gas (22%) [
1]. The same trend is evident in different continents and countries around the world, including Nigeria which is one of the major economies in Sub-Saharan Africa and the largest oil producer in Africa. Its total primary energy consumption comes from traditional solid biomass and waste (80%) [
2]. The fossil fuels utilised in Nigeria are mainly being consumed in the transportation sector (100% oil), productive (17% gas and 16% oil), and residential uses (3% oil). Nigerian power generation comes mainly from gas (62%), oil (33%), and hydro (5%) [
3]. Therefore, there is a need for affordable alternative sources of energy that will decrease the share of fossil fuels utilised in the transportation sector and productive uses, reduce environmental concerns caused by the extensive utilization of these resources (e.g., climate change and global warming), increase energy security, and improve the access of the population to electricity. Renewable energy sources from lignocellulosic biomass have been reported as promising solutions to these problems [
4]. Nowadays, biomass itself is responsible for 10% primary energy consumption worldwide [
5] and it can be used as a promising feedstock for biofuel production. The sustainability of wastes and by-products as a biofuel for the transportation sector in a circular economy has been studied in the literature, in order to make the biogas–biomethane chain more sustainable [
6,
7,
8]. These studies show the positive impact of biofuels in the transportation section in Europe. However, further research needs to be done in order to apply circular economy models to emerging economies.
Sub-Saharan Africa has great potential to develop renewable energy sources, such as wind, biomass, solar, and hydro. Just in Nigeria, the biomass potential is about 144 million tonnes per year and the potential of its lignocellulosic agricultural waste varies between 0.4 and 2.3 t/ha, as reported in previous publications [
9]. However, the utilisation of biomass resources for electricity, biofuel, or biogas generation has not been extensively utilised or studied in most African countries [
9,
10,
11]. From the different lignocellulosic materials currently available in Nigeria, Napier grass has been reported to be a particularly attractive feedstock for production of biofuels and bio-based products mainly due to its high cellulose content (34.2–40%), high yields per unit area, tolerance to drought, and a good water use efficiency (ratio of water used by the crop to water lost by evapotranspiration) [
12,
13,
14,
15,
16,
17].
Napier grass is a perennial C4 plant endemic to Sub-Saharan Africa with a high heating value biomass (16.58 MJ/kg) [
18]. This crop is mainly used to feed cattle, but it can also be used for grazing, silage, or hay production or fish food [
15]. In moderate climates, it can be harvested up to four times per year [
19,
20]. However, in most of the cases, Napier grass is a neglected crop that exists in the wild and that does not need to be cultivated [
21,
22], making it a particularly attractive feedstock for biogas and bioethanol production. Sawasdee and Pisutpaisal [
23] studied the potential of Napier grass for biogas production. At 5% total solids, the authors obtained the highest kinetics rate for biogas production and concluded that this feedstock can be grown for this purpose. Narinthorn et al. [
13] also investigated the biomethane potential of Napier grass. For this, the authors applied combined alkaline and biological pretreatment methods as a strategy to enhance biomethane yields from Napier grass. The results reveal that alkaline pretreatment method increased the anaerobic digestibility from 49% (untreated grass) to 77% and improved the biomethane yields by about 34%. Janejadkarn and Chavalparit [
24] quantified biogas production from Napier grass. The results indicated that with a 2% volatile solids content and an organic load rate of 0.57 kg VS/m
3, it is possible to achieve the maximum biogas yield (0.529 m
3/kg VS). Under the same conditions, the methane production was 0.242 m
3/kg VS added. All this suggests that Napier grass can be successfully converted into biogas by means of anaerobic digestion.
Liu et al. [
25] investigated the potential of Napier grass for bioethanol production by using dilute-alkali and dilute-acid pretreatment methods. The results show that, for a feeding concentration of 10 g/L, the theoretical conversion rate of this feedstock is about 12.6%, and for a feeding concentration of 15 g/L its conversion rate increased to 23%. The authors concluded that agricultural waste had potential for bioethanol production. Wongwatanapaiboon et al. [
26] analysed the potential of Napier grass as feedstock for lignocellulosic bioethanol production by using alkaline peroxide as a pretreatment method. The ethanol yields from Napier grass produced by simultaneous saccharification and cofermentation (SSCF) are 1171.69 L/ha/year, indicating that Napier grass has potential for cellulosic ethanol production.
Although bioethanol production from lignocellulosic materials has been widely studied, its production still has environmental, economic, and energetic constraints. From the environmental perspective, the sidestream generated after the distillation stage has a high pollutant potential and the best handling options still need to be studied. Economically, the energy costs required in the pretreatment stage are still high, making biofuel production less competitive compared to fossil fuels. Energetically, ethanol from biomass has a low-energy return on energy invested (ERoEI) when compared to coal, oil, and gas. Therefore, solutions to add value to the bioethanol production chain to make its production more competitive are needed. Having this in mind, anaerobic digestion (AD) has been proposed as a handling option for waste recovery from biodegradable waste and bioethanol sidestreams [
4,
8,
27].
As the Nigerian biofuel sector is in a developing stage, this paper aims at evaluating the potential of Nigerian Napier grass for bioethanol and biogas production and at investigating its reliability as an alternative source of energy for the transportation sector in Nigeria and other African countries with high availability of this grass. For this, samples taken from different stages of bioethanol production (pretreatment, hydrolysis, and distillation) and bioethanol sidestream were used. These samples went through a separation process (solid and liquid fractions) and different production pathways in order to enhance bioenergy yields, improve the efficiency of the production chain, decrease the energy and water requirements, and reduce the sidestream volume generated at the end of process.
2. Materials and Methods
2.1. Bioethanol Production
2.1.1. Biomass
The Penisetum purpurum (Napier grass) grew in the wild and was harvested near Effurun town of Delta State in Nigeria. It was harvested in the Harmattan period in early January of 2019 and allowed to dry naturally in the sun. After drying, the biomass was shipped to Estonia where all the experiments were carried out. The samples were milled and sieved to the size of 3 mm or smaller in the Cutting Mill SM 100 Comfort (from Retsch GmbH).
2.1.2. Pretreatment
The Napier grass was pretreated with the nitrogen explosive decompression (NED) method. For pretreatment, 100 g of raw material were added into the 2 L non-stirred pressure vessel and soaked in 800 g of distilled water. The vessel was closed, and the samples were heated up from room temperature (23 °C) up to 150 °C, 170 °C, 190 °C, or 200 °C, under constant pressure (30 bar), for the retention time of one minute. Once the desired temperature was reached, the reactor was cooled down to approximately 80 °C and the pressure was released in an explosive manner using the pressure release valve.
Figure 1 illustrates the pretreatment system utilised in these experiments. After the pretreatment process, the samples were cooled down to 50 °C for the following enzymatic hydrolysis.
2.1.3. Hydrolysis
The material obtained from the pretreatment process was added into a 1000 mL shake flask for enzymatic hydrolysis. For this, 30 FPU g/cellulose of the cellulase complex Accelerase 1500 (DuPont de Nemours) was added into the suspension, and the flask was filled up with distilled water to 1000 mL (working volume). The process was carried out in an orbital shaker (IKA®-Werke GmbH & Co. KG, Staufen im Breisgau, Germany) (KS 4000 I control) during a 24 h period, at temperature of 50 °C and rotation speed of 250 rpm.
2.1.4. Fermentation
Glucose in the hydrolysate was converted into ethanol in the following fermentation step. The fermentation process was performed in glass bottles with a working volume of 1000 mL, using 2.5 g of the commercial yeast Saccharomyces cerevisiae (Turbo yeast T3). After adding the yeast, the glass bottles were closed with an airlock and the fermentation process was carried through for seven days, at room temperature.
2.1.5. Distillation
After the fermentation, the samples went through a distillation process at 175 mbar using a rotating evaporation system designed for ethanol separation, Buchi R-210 Rotavapor System from BÜCHI Labortechnik (Flavil, Switzerland). The material obtained after the distillation process (bioethanol production sidestream) was analysed in terms of its potential for biomethane production in the biomethane potential assay (BMP).
2.2. Biomethane Potential (BMP)
Samples from the solid and liquid fractions of different stages of bioethanol production process (pretreatment, hydrolysis, and distillation) were used as a feedstock.
Figure 2 illustrates the different production pathways utilised in this study. The BMP was measured in untreated Napier grass (pathway 1), samples from the solid fraction of post-pretreatment broth (pathway 2) and post-hydrolysis broth (pathway 4), and samples from the liquid fraction of post-pretreatment broth (pathway 3), post-hydrolysis broth (pathway 5) and post-distillation broth (pathway 6).
The BMP assay utilised in these experiments is based on a modified version of the methods reported by Owen et al. [
29] and Angelidaki et al. [
30]. The inoculum sludge utilised in this study was obtained from the local wastewater treatment plant (Tartu, Estonia). Prior to use, the inoculum was stabilized for four days in an incubator at 36 °C, so the residual organic matter would be consumed, and the dissolved gases would be removed by a process of degasification. The assays were performed in 575 mL glass bottles, with a working volume of 200 mL, headspace volume of 375 mL and VS substrate/VS inoculum ratio of 0.25 (based on the volatile solids content that characterizes the quantity of organic material available in the solid). Before sealing the bottles, nitrogen gas was purged into the headspace of the flasks for approximately three minutes to ensure anaerobic conditions. The bottles were further sealed with rubber stoppers and aluminium caps, mixed, and incubated for 42 days, under mesophilic conditions (37 °C ± 1 °C) until the methane production was less than 1% of the total amount produced. The bottles were mixed daily by shaking. The experiments were performed in triplicates and a blank test with inoculum sludge only was also prepared in order to determine the methane production from the inoculum sludge itself, which was later utilised in the calculations of methane gas produced.
The biogas production was evaluated by measuring the increase of the total headspace pressure in the test flasks before and after the gas chromatograph (GC) analysis with a pressure meter WAL BMP-Testsystem (from WAL Mess-und Regelsysteme GmbH, Germany).
The pH of all the samples was measured at the end of the experiments with a SevenMulti™ S47-dual pH/conductivity meter to ensure that the anaerobic digestion was performed under optimum conditions (pH of 6.8–7.2) from the beginning until the end of the process [
31]. The biomethane results are reported in moles of methane per 100 g of raw material using standard conditions to understand the amount of methane that can be obtained from the original raw material, and in L CH
4/kg VS.
2.3. Analytical Methods
The composition of the samples in terms of cellulose, hemicellulose, and lignin (fibre analysis) was determined using an ANKOM 2000 analyzer (ANKOM Technology, Macedon, NY, USA). The percentage of moisture in the samples was analysed in the Kern MLS-50-3D moisture analyser from Kern & Sohn GmbH.
The active volume of substrate and inoculum was determined from the analysis of the total solids (TS) and volatile solids (VS) content, which were determined according to the method 1684 from the U.S. Environmental Protection Agency (EPA). The methane content in the biogas was measured chromatographically using the GC (CP-4900 Micro-GC, Varian Inc., Palo Alto, CA, USA). The gas chromatograph was equipped with a thermal conductivity detector, a Molsieve 5A Backflush heated column (20 m × 0.53 mm) and a PoraPLOT U heated column (10 m × 0.53 mm). Argon was used as a carrier gas in column 1, and the operational conditions of this column were as follows: injection temperature 110 °C, column temperature 120 °C, and column pressure 50 Psi. In column 2, the carrier gas was helium and the injection temperature, column temperature, and column pressure were set to 110 °C, 150 °C, and 22 Psi, respectively.
The samples of solid and liquid fractions investigated in this study were obtained from different stages of the bioethanol production process (pretreatment, hydrolysis, and distillation) with a separation process using the pathway illustrated in
Figure 2. For this, post-pretreatment broth and post-hydrolysis broth were collected and centrifuged using Thermo Scientific Heraeus Megafuge at a rotational speed of 10,000 rpm for 20 min until the solid and liquid fractions were fully separated. To ensure a full separation of the supernatant (liquid fraction) and the retentate (solid fraction), the samples were separated using vacuum filtration. After that, samples from the solid fraction were rinsed with distilled water to remove residual solubles and dried at 40 °C to a moisture content of 4.5% (or less). Both fractions were analysed for BMP.
Glucose, xylose, galactose, arabinose, mannose, glycerol, acetic acid, and ethanol were quantified with a high pressure liquid chromatography [
32] using fractions after hydrolysis and fermentation steps.
2.4. Calculations
The quantity of methane gas (initial) produced in the test flask
(mol CH
4) is given by Equation (1):
where P
I (Pa) is the total pressure at the headspace determined prior to the GC analysis, V
HS (m
3) is the volume of the headspace of the bottle, MF is the methane fraction determined by the GC in the current period of time, R is the ideal gas constant (8314 Jmol
−1 K
−1), and T is the temperature in the incubator (°C).
The quantity of methane gas (final) in the headspace of the test flask
(mol CH
4) is determined by Equation (2):
where P
F (Pa) is the total pressure at the headspace determined following the GC analysis.
The cumulative methane produced in the current period of time
(mol CH
4) is defined by Equation (3):
where
(mol CH
4) is the quantity of methane in the headspace of the flask (initial) in the current period of time,
(mol CH
4) is the quantity methane in the headspace of the test bottle (final) in the prior period of time, and
(mol CH
4) is the quantity of cumulative methane gas produced in the prior period of time.
The results of methane gas produced were modelled in the statistics software GraphPad Prism 5.0 using a nonlinear regression model that was further fitted in an exponential first-order association model (Equation (4) [
19,
20]:
where B is the cumulative methane produced (mol CH
4/100 g) at time interval (t), B
max is the maximum methane yield (mol CH
4/100 g), k is the kinetics rate constant (d
−1).
2.5. Statistics
The statistical analysis was performed with the software GraphPad Prism 5. The Shapiro-Wilk’s normality test was utilised to determine the normal distribution of the variables. The Kruskal–Wallis test and the post hoc test Dunn’s multiple comparison test were used to investigate the differences between the variables. The results are represented with the respective error bars and intervals that denote one standard deviation. The results were considered significantly different when the p-value was inferior to p < 0.05.
2.6. Napier Grass Availability, Production, and Growth
The estimated biomass yields (of all the feedstocks with exception of Napier grass) were obtained from FAO (Food and Agriculture Organisation of UN) bioenergy and food security rapid appraisal tool (Excel-based tools) and represent a ten-years average of annual production at country level [
33]. The different Napier grass yields were obtained from the literature [
34,
35,
36].