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Waste Conversion into n -Caprylate and n -Caproate: Resource Recovery from Wine Lees Using Anaerobic Reactor Microbiomes and In-line Extraction

View Article: PubMed Central - PubMed

ABSTRACT

To convert wastes into sustainable liquid fuels and chemicals, new resource recovery technologies are required. Chain elongation is a carboxylate-platform bioprocess that converts short-chain carboxylates (SCCs) (e.g., acetate [C2] and n-butyrate [C4]) into medium-chain carboxylates (MCCs) (e.g., n-caprylate [C8] and n-caproate [C6]) with hydrogen gas as a side product. Ethanol or another electron donor (e.g., lactate, carbohydrate) is required. Competitive MCC productivities, yields (product vs. substrate fed), and specificities (product vs. all products) were only achieved previously from an organic waste material when exogenous ethanol had been added. Here, we converted a real organic waste, which inherently contains ethanol, into MCCs with n-caprylate as the target product. We used wine lees, which consisted primarily of settled yeast cells and ethanol from wine fermentation, and produced MCCs with a reactor microbiome. We operated the bioreactor at a pH of 5.2 and with continuous in-line extraction and achieved a MCC productivity of 3.9 g COD/L-d at an organic loading rate of 5.8 g COD/L-d, resulting in a promising MCC yield of 67% and specificities of 36% for each n-caprylate and n-caproate (72% for both). Compared to all other studies that used complex organic substrates, we achieved the highest n-caprylate-to-ncaproate product ratio of 1.0 (COD basis), because we used increased broth-recycle rates through the forward membrane contactor, which improved in-line extraction rates. Increased recycle rates also allowed us to achieve the highest reported MCC production flux per membrane surface area thus far (20.1 g COD/m2-d). Through microbial community analyses, we determined that an operational taxonomic unit (OTU) for Bacteroides spp. was dominant and was positively correlated with increased MCC productivities. Our data also suggested that the microbiome may have been shaped for improved MCC production by the high broth-recycle rates. Comparable abiotic studies suggest that further increases in the broth-recycle rates could improve the overall mass transfer coefficient and its corresponding MCC production flux by almost 30 times beyond the maximum that we achieved. With improved in-line extraction, the chain-elongation biotechnology production platform offers new opportunities for resource recovery and sustainable production of liquid fuels and chemicals.

No MeSH data available.


Related in: MedlinePlus

Beta diversity for the inoculum, the substrate, and bioreactor samples from Periods 1–5. PCoA of weighted UniFrac distances was performed with sequence data for 12 microbiome samples, including: one inoculum sample (square); one wine lees substrate sample (triangle); three bioreactor samples from the batch phase (Period 1); and seven bioreactor samples from the phase of semi-continuous substrate addition with continuous in-line extraction (Periods 2–5) – bioreactor samples are shown by circles. The increasingly darker blue circles were collected at later days. The numbers within the circles indicates the collection day number. The increased distance between samples represents increased dissimilarity. The first two PCoA axes are shown, which together explain 72% of the overall phylogenetic variation between samples; PC1 explains 52%, while PC2 explains 20% of the overall phylogenetic variation.
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Figure 8: Beta diversity for the inoculum, the substrate, and bioreactor samples from Periods 1–5. PCoA of weighted UniFrac distances was performed with sequence data for 12 microbiome samples, including: one inoculum sample (square); one wine lees substrate sample (triangle); three bioreactor samples from the batch phase (Period 1); and seven bioreactor samples from the phase of semi-continuous substrate addition with continuous in-line extraction (Periods 2–5) – bioreactor samples are shown by circles. The increasingly darker blue circles were collected at later days. The numbers within the circles indicates the collection day number. The increased distance between samples represents increased dissimilarity. The first two PCoA axes are shown, which together explain 72% of the overall phylogenetic variation between samples; PC1 explains 52%, while PC2 explains 20% of the overall phylogenetic variation.

Mentions: We also analyzed the dissimilarity of the OTU composition between pairs of samples by using the weighted UniFrac algorithm (beta diversity) (Lozupone and Knight, 2005), resulting in greater distances between more dissimilar samples. Principal coordinates analysis (PCoA) allows us to visualize these distances on a 2-dimensional plot (Figure 8). We observed a chronological order from early samples (lighter circles) to late samples (darker circles). One discrepancy in this chronological order was observed for Periods 4 and 5 (Days 74 and 84, respectively) (Figure 8). The sample from Day 84 was actually more similar to the sample from Day 64 than for the sample from Day 74. We had increased the broth-recycle rate from Period 2 through Period 4 (Day 74), and then we decreased the recycle rate during Period 5 again (Day 84) (Figure 5). Therefore, we suggest that the larger distance between the sample from Period 4 (Day 74) and earlier samples was due to the increased broth-recycle rate. The most dissimilar sample from our study (Day 74) represented the sample from the bioreactor with the highest broth-recycle rate, MCC productivity, and n-caprylate-to-n-caproate ratio (Figure 5), identifying that the microbiome had likely been shaped for its function. However, a future study would be needed to substantiate this finding.


Waste Conversion into n -Caprylate and n -Caproate: Resource Recovery from Wine Lees Using Anaerobic Reactor Microbiomes and In-line Extraction
Beta diversity for the inoculum, the substrate, and bioreactor samples from Periods 1–5. PCoA of weighted UniFrac distances was performed with sequence data for 12 microbiome samples, including: one inoculum sample (square); one wine lees substrate sample (triangle); three bioreactor samples from the batch phase (Period 1); and seven bioreactor samples from the phase of semi-continuous substrate addition with continuous in-line extraction (Periods 2–5) – bioreactor samples are shown by circles. The increasingly darker blue circles were collected at later days. The numbers within the circles indicates the collection day number. The increased distance between samples represents increased dissimilarity. The first two PCoA axes are shown, which together explain 72% of the overall phylogenetic variation between samples; PC1 explains 52%, while PC2 explains 20% of the overall phylogenetic variation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5121217&req=5

Figure 8: Beta diversity for the inoculum, the substrate, and bioreactor samples from Periods 1–5. PCoA of weighted UniFrac distances was performed with sequence data for 12 microbiome samples, including: one inoculum sample (square); one wine lees substrate sample (triangle); three bioreactor samples from the batch phase (Period 1); and seven bioreactor samples from the phase of semi-continuous substrate addition with continuous in-line extraction (Periods 2–5) – bioreactor samples are shown by circles. The increasingly darker blue circles were collected at later days. The numbers within the circles indicates the collection day number. The increased distance between samples represents increased dissimilarity. The first two PCoA axes are shown, which together explain 72% of the overall phylogenetic variation between samples; PC1 explains 52%, while PC2 explains 20% of the overall phylogenetic variation.
Mentions: We also analyzed the dissimilarity of the OTU composition between pairs of samples by using the weighted UniFrac algorithm (beta diversity) (Lozupone and Knight, 2005), resulting in greater distances between more dissimilar samples. Principal coordinates analysis (PCoA) allows us to visualize these distances on a 2-dimensional plot (Figure 8). We observed a chronological order from early samples (lighter circles) to late samples (darker circles). One discrepancy in this chronological order was observed for Periods 4 and 5 (Days 74 and 84, respectively) (Figure 8). The sample from Day 84 was actually more similar to the sample from Day 64 than for the sample from Day 74. We had increased the broth-recycle rate from Period 2 through Period 4 (Day 74), and then we decreased the recycle rate during Period 5 again (Day 84) (Figure 5). Therefore, we suggest that the larger distance between the sample from Period 4 (Day 74) and earlier samples was due to the increased broth-recycle rate. The most dissimilar sample from our study (Day 74) represented the sample from the bioreactor with the highest broth-recycle rate, MCC productivity, and n-caprylate-to-n-caproate ratio (Figure 5), identifying that the microbiome had likely been shaped for its function. However, a future study would be needed to substantiate this finding.

View Article: PubMed Central - PubMed

ABSTRACT

To convert wastes into sustainable liquid fuels and chemicals, new resource recovery technologies are required. Chain elongation is a carboxylate-platform bioprocess that converts short-chain carboxylates (SCCs) (e.g., acetate [C2] and n-butyrate [C4]) into medium-chain carboxylates (MCCs) (e.g., n-caprylate [C8] and n-caproate [C6]) with hydrogen gas as a side product. Ethanol or another electron donor (e.g., lactate, carbohydrate) is required. Competitive MCC productivities, yields (product vs. substrate fed), and specificities (product vs. all products) were only achieved previously from an organic waste material when exogenous ethanol had been added. Here, we converted a real organic waste, which inherently contains ethanol, into MCCs with n-caprylate as the target product. We used wine lees, which consisted primarily of settled yeast cells and ethanol from wine fermentation, and produced MCCs with a reactor microbiome. We operated the bioreactor at a pH of 5.2 and with continuous in-line extraction and achieved a MCC productivity of 3.9 g COD/L-d at an organic loading rate of 5.8 g COD/L-d, resulting in a promising MCC yield of 67% and specificities of 36% for each n-caprylate and n-caproate (72% for both). Compared to all other studies that used complex organic substrates, we achieved the highest n-caprylate-to-ncaproate product ratio of 1.0 (COD basis), because we used increased broth-recycle rates through the forward membrane contactor, which improved in-line extraction rates. Increased recycle rates also allowed us to achieve the highest reported MCC production flux per membrane surface area thus far (20.1 g COD/m2-d). Through microbial community analyses, we determined that an operational taxonomic unit (OTU) for Bacteroides spp. was dominant and was positively correlated with increased MCC productivities. Our data also suggested that the microbiome may have been shaped for improved MCC production by the high broth-recycle rates. Comparable abiotic studies suggest that further increases in the broth-recycle rates could improve the overall mass transfer coefficient and its corresponding MCC production flux by almost 30 times beyond the maximum that we achieved. With improved in-line extraction, the chain-elongation biotechnology production platform offers new opportunities for resource recovery and sustainable production of liquid fuels and chemicals.

No MeSH data available.


Related in: MedlinePlus