Fermentative hydrogen and polyhydroxybutyrate production from pretreated cyanobacterial blooms

Cai, Jinghui; Chen, Minglang; Wang, Guangce; Pan, Guanghua; Yu, Ping

doi:10.1016/j.algal.2015.09.014

Cited by 18 publications

(4 citation statements)

References 33 publications

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“…Butyric acid has a carbon chain length of three and has been noted as one of the most abundant short-chain fatty acids in algal bloom metabolic processes. 10,11,57 Butyric acid can also be an indicator of ocean oxygenation. 58 As algal bloom populations collapse, the dissolved oxygen is depleted, causing negative impacts to ocean health.…”

Section: ■ Methodsmentioning

confidence: 99%

“…This second criterion limited the options to fatty acids with a carbon chain length of three atoms or less. Butyric acid has a carbon chain length of three and has been noted as one of the most abundant short-chain fatty acids in algal bloom metabolic processes. ,, Butyric acid can also be an indicator of ocean oxygenation . As algal bloom populations collapse, the dissolved oxygen is depleted, causing negative impacts to ocean health. , This lack of oxygen also increases ocean acidification …”

Section: Methodsmentioning

confidence: 99%

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Multi-Analyte Concentration Analysis of Marine Samples through Regression-Based Machine Learning

North,

Clark,

Enders

et al. 2024

ACS Earth Space Chem.

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Marine systems are incredibly chemically complex. An understanding of the chemical compounds that make up the chemical diversity in marine samples is critical to understanding ecological and ocean health metrics. Using Raman spectroscopy in tandem with machine learning combines a low-cost, highly transportable analytical technique with a powerful and rapid computational approach that can aid in marine analysis. Here, we use Raman spectroscopy and machine learning to identify mM concentrations of three chemically relevant compounds in three distinct classes in a complex aqueous matrix. Saccharides are represented by glucose, fatty acids by butyric acid, and proteins by an amino acid proxy through glycine. Eight classical machine learning models (gradient boosted regressors, random forests, histogram gradient boosted regressors, decision trees, k-nearest neighbors, support vector regression, multi-layer perceptrons, and multivariate linear regression) were tested for their accuracy in identifying the concentrations of glycine, glucose, and butyric acid in marine samples, which were benchmarked through a mass spectrometric method. Support vector regression was able to best identify all three concentrations of glycine, butyric acid, and glucose. Butyric acid was similarly well described through gradient boosted regression and histogram gradient boosted regression. The described spectroscopy and machine learning methodology has the potential to significantly advance rapid field analysis of marine samples.

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Section: ■ Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Multi-Analyte Concentration Analysis of Marine Samples through Regression-Based Machine Learning

North,

Clark,

Enders

et al. 2024

ACS Earth Space Chem.

View full text Add to dashboard Cite

show abstract

“…Butyric acid has a carbon chain length of three and has been noted as one of the most abundant short chain fatty acid in algal bloom metabolic processes. 10,11,57,58 Butyric acid can also be an indicator of ocean oxygenation. 59 As algal bloom populations collapse, the dissolved oxygen is depleted, causing negative impacts to ocean health.…”

Section: Selection Of Representative Analytesmentioning

confidence: 99%

Multi Analyte Concentration Analysis of Marine Samples Through Regression Based Machine Learning

North,

Clark,

Enders

et al. 2024

Preprint

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Marine systems are incredibly chemically complex. An understanding of the chemical compounds that make up the chemical diversity in these samples is critical to understanding ecological and ocean health metrics. Using Raman spectroscopy in tandem with machine learning combines a low-cost highly transportable analytical technique with a powerful and rapid computational approach that can aid in marine analysis. Here we use Raman and machine learning to identify concentrations of three chemically relevant compounds in three distinct classes. Saccharides are represented by glucose, fatty acids by butyric acid, and proteins are represented by amino acid proxy through glycine. Eight machine learning models (gradient boosted regressors, random forests, histogram gradient boosted regressors, decision trees, k nearest neighbors, support vector regression, multilayer perceptrons, and multivariate linear regression) were tested for their accuracy in identifying the concentrations of glycine, glucose, and butyric acid in marine samples. Support vector regression was able to best identify all three concentrations of glycine, butyric acid, and glucose. Butyric acid was similarly well described through gradient boosted regression and histogram gradient boosted regression. In this work Raman, though it has a lower sensitivity than mass spectrometry, can still be used to identify mM concentrations of organics in complex aqueous matrix. The described methodology has the potential to significantly advance rapid field analysis of marine samples.

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“…Alkalinity has a complex effect on the performance of downstream processes. For instance, high alkalinity can improve biomass degradability and biopolymers solubility, facilitating certain biomass conversion and product recovery operations [9][10][11]. On the contrary, the alkaline biomass slurry may be corrosive or may contain compounds that could have inhibitory effects or be considered as contaminants in the final products [12].…”

Section: Introductionmentioning

confidence: 99%

Autofermentation of alkaline cyanobacterial biomass to enable biorefinery approach

Demirkaya

Vadlamani

Tervahauta

et al. 2023

Biotechnol Biofuels

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Background Carbon capture using alkaliphilic cyanobacteria can be an energy-efficient and environmentally friendly process for producing bioenergy and bioproducts. The inefficiency of current harvesting and downstream processes, however, hinders large-scale feasibility. The high alkalinity of the biomass also introduces extra challenges, such as potential corrosion, inhibitory effects, or contamination of the final products. Thus, it is critical to identify low cost and energy-efficient downstream processes. Results Autofermentation was investigated as an energy-efficient and low-cost biomass pre-treatment method to reduce pH to levels suitable for downstream processes, enabling the conversion of cyanobacterial biomass into hydrogen and organic acids using cyanobacteria’s own fermentative pathways. Temperature, initial biomass concentration, and oxygen presence were found to affect yield and distribution of organic acids. Autofermentation of alkaline cyanobacterial biomass was found to be a viable approach to produce hydrogen and organic acids simultaneously, while enabling the successful conversion of biomass to biogas. Between 5.8 and 60% of the initial carbon was converted into organic acids, 8.7–25% was obtained as soluble protein, and 16–72% stayed in the biomass. Interestingly, we found that extensive dewatering is not needed to effectively process the alkaline cyanobacterial biomass. Using natural settling as the only harvesting and dewatering method resulted in a slurry with relatively low biomass concentration. Nevertheless, autofermentation of this slurry led to the maximum total organic acid yield (60% C mol/C mol biomass) and hydrogen yield (326.1 µmol/g AFDM). Conclusion Autofermentation is a simple, but highly effective pretreatment that can play a significant role within a cyanobacterial-based biorefinery platform by enabling the conversion of alkaline cyanobacterial biomass into organic acids, hydrogen, and methane via anaerobic digestion without the addition of energy or chemicals.

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Fermentative hydrogen and polyhydroxybutyrate production from pretreated cyanobacterial blooms

Cited by 18 publications

References 33 publications

Multi-Analyte Concentration Analysis of Marine Samples through Regression-Based Machine Learning

Multi-Analyte Concentration Analysis of Marine Samples through Regression-Based Machine Learning

Multi Analyte Concentration Analysis of Marine Samples Through Regression Based Machine Learning

Autofermentation of alkaline cyanobacterial biomass to enable biorefinery approach

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