with a control reactor, their study demonstrated the possibility of valorization of real organic waste (i.e., food waste) for bioethanol production via EF. As shown in Table 1.1, the majority of EF studies were conducted with synthetic substrates. Therefore, future EF should consider utilizing real waste biomass to demonstrate the feasibility of EF for practical application.
1.3.3 Bio-Butanol
Bio-butanol is another increasingly popular platform chemical that can also be used as liquid biofuel or additive to fossil fuel due to high energy content (27.08 MJ/l) and compatibility with combustion engines (Kumar and Gayen, 2011). The fermentation process driven by solventogenic Clostridia has been widely used for biobutanol production from carbohydrates-rich feedstock due to its low capital cost compared to the petrochemical butanol production process (Elbeshbishy et al., 2015). Before 2005, butanol was mainly used for the synthesis of other industrial chemicals. After being recognized as a biofuel, the demand for butanol has considerably increased in the last 15 years (Kumar and Gayen, 2011). The global market of biobutanol is now expected to reach USD 17.78 billion by 2022 (Grand View Research, 2020).
Low biobutanol yield, as well as production rate, have been identified as major bottlenecks for wide-scale application of fermentative biobutanol production (Elbeshbishy et al., 2015). Over the years, various strategies, including the deployment of genetically modified Clostridia and non-Clostridia organisms as well as designing novel fermentation systems, have been considered for alleviating these bottlenecks. In recent years, a few studies investigated biobutanol production via EF of glucose with pure-culture Clostridium species (Choi et al., 2014; Engel et al., 2019; Mostafazadeh et al., 2016). Choi et al. (2014) studied cathodic EF of glucose with pure culture Clostridium pasteurianum DSM 525 in a dual-chamber EF system. The authors found that C. pasteurianum could produce butanol by utilizing electrons from both cathode and substrate (glucose). Although NADH generation from electricity was trivial as compared to that generated from glucose, EF could increase butanol production by 2.5 times over the control fermenter. Thus, their results suggested a metabolic shift in reduction pathways in C. pasteurianum due to the applied potential.
A study by Mostafazadeh et al. (2016) also reported enhanced biobutanol yield (g butanol/g consumed glucose) and productivity (g butanol/L-h) in cathodic EF using pure-culture C. pasteurianum. Compared to the control (i.e., conventional fermentation), butanol yield and productivity increased by 29% and 30%, respectively. Furthermore, their study highlighted the importance of process optimization of EF for biobutanol production. The authors investigated the effects of various process parameters, including glucose concentration (86.3-153.6 g/L), temperature (27.6-39.4°C), and applied voltage (0-2.6 V) using statistical design of experiment in cathodic EF of glucose to biobutanol using pure-culture C. pasteurianum. The maximum biobutanol concentration of 13.31 g/L butanol was achieved at an applied voltage of 1.32 V under an operating temperature of 33.51°C and at a glucose concentration of 120 g/L. The glucose concentration ≥140 g/L introduced inhibition and led to a decrease in butanol yield. Furthermore, the authors also compared the impact of cathode electrode materials on butanol production. The results showed that up to an applied voltage of 1.5, graphite felt as cathode could provide higher butanol concentration as compared to the stainless-steel electrode. However, with the applied voltage >1.5 V, the performance of both electrodes declined. With an increase in applied voltages, hydrogen production on the cathode linearly increased in both reactors. Moreover, the stainless-steel electrode led to higher hydrogen production as compared to the graphite, which is consistent with some of the reports on superior hydrogen evolution reaction (HER) with the metal electrode (Cheng et al., 2009). Thus, superior HER on the stainless surface could introduce instability of cathode biofilms. Overall, this study demonstrated considerable scope to optimize process parameters and design of EF systems to maximize productivity and yield of target value-added products.
1.3.4 Microalgae Derived Lipids
Up to date, fossil fuels have been widely adopted as energy sources across the world. However, there is a great need to significantly develop the renewable fuels (e.g., non-fossil fuel) as energy sources on a large scale to outcompete the fossil fuels, eliminating the issues of aggravating CO2 concentrations and global warming (IPCC, 2018; Liu et al., 2019; Liu et al., 2020c). Out of many renewable fuels, microalgae-derived biofuels have demonstrated to be highly promising due to their advantages, such as high biomass production per unit area, a high weight ratio of lipids and ability to accumulate lipids in oleosomes, and no competition for arable land usage (Adeniyi et al., 2018; Chisti, 2007; Hallenbeck et al., 2016; Hu et al., 2008; Rittmann, 2008). Despite these attracting features, several drawbacks for the microalgae biofuel generation have been reported, emphasizing their high costs, and environmental concerns associated with algae harvesting and lipids extraction (Markou and Nerantzis, 2013; Pierobon et al., 2018; Rittmann, 2008; Sills et al., 2013). For instance, the requirement of pre-treatment (e.g., acid/alkaline hydrolysis, pulsed electric fields, ultrasound (Sheng et al., 2011; Zbinden et al., 2013)) for microalgae processing are generally highly energy intensive (e.g., in terms of both capital and operational costs), which was one of the arguments against further development and scaling-up. Lipid extraction is a critical step prior to the microalgal biodiesel production process. However, highly toxic chloroform-methanol solvents have been widely adopted for lipid extractions due to their effective performance (e.g., the ability to penetrate cell wall and membrane) (Bligh and Dyer, 1959; Folch et al., 1957). However, they must be replaced by other non-toxic solvents (e.g., hexane-isopropanol) due to the environmental and public health concerns (Lai et al., 2016a; Lai et al., 2014). Hence, a greener approach is required to address the challenges associated with microalgae-derived lipids extraction.
In response, selective fermentation (SF) has been implemented as a new approach for enhancing lipid extraction for microalgae biofuel generation (i.e., biodiesel) (Lai et al., 2016b). SF utilizes the fact that lipids are generally biodegraded more slowly than carbohydrates and proteins under an anaerobic environment (Lai et al., 2016b; Rittmann and McCarty, 2001; Siegert and Banks, 2005), in which, the lipid fermenters, known as the slow-growing microbes (Christ et al., 2000; Rittmann and McCarty, 2001), can be washed out of the system with a relatively short solids retention time (SRT) (Lai et al., 2016b). Hence, the SF solely allows the removal of carbohydrates and proteins in microalgae cells, leaving the lipids intact, providing a state much easier to extract lipids (Lai et al., 2016b). More importantly, the SF process can significantly promote the lipid extraction process (e.g., >5000-fold increase vs. untreated biomass) when using the hexane-isopropanol solvents (Lai et al., 2016a). Moreover, the lipid extraction is also benefitted by SF due to biohydrogenation, where the dominant lipid groups on long-chain fatty acids (LCFAs) are converted to saturated forms (e.g., C18:1 to C18:0) (Liu et al., 2019); the SF process can increase the saturation ratio of extracted lipids by up to 80% (Liu et al., 2020c). This saturating process via SF for the LCFAs is highly desirable as they can aid the transportation fuel generation due to more energy value, higher octane number (e.g., better combustion efficiency), and greater resistance to oxidation (Knothe, 2011). Also, SF biohydrogenation is superior to the catalytic hydrogenation (e.g., requires high temperature and metal catalysts to operate) due to significantly less or no energy/ catalyst demand (McArdle et al., 2011; Plourde et al., 2004; Wang et al., 2017). Figure 1.5 shows a conceptual schematic of lipid extraction from microalgae using electro-selective fermentation.
However, the SF itself can suffer from the fact that the protein fermentation is slower than the carbohydrate fermentation (Lu et al., 2010). Furthermore, accumulation of short-chain carboxylates (e.g., high COD in the system) is another challenge (Lai et al., 2016b), resulting in the following two cases: (1) wasting the electrons from the feed biomass (e.g., not all the electrons are used up or recovered as value-added products); and (2) lowering pH, which inhibits
