Electron flow of biological H2 production by sludge under simple thermal treatment: Kinetic study

The response regularity of biohydrogen production by anthracite H2-producing bacteria consortium to six conventional veterinary antibiotics

The impact of antibiotics on H₂-producing bacteria must be considered in the industrialization of biological H₂ production using livestock manure as raw resources. However, whether antibiotics that may be contained in excreta will threaten the safety of biohydrogen production needs to be researched. This study explored the impact characteristics and mechanism of six single antibiotics and three groups of compound antibiotics on H₂ production. Experiments confirmed that most antibiotics have different degrees of H₂ production inhibition, while some antibiotics, which like Penicillin G, Streptomycin Sulfate, and their compound antibiotics, could promote the growth of Ethanoligenens sp. and improve H₂ yield on the contrary. Comprehensive analysis shows that the main inhibitory mechanisms were: (1) board-spectrum inhibition, (2) partial inhibition, (3) H₂ consumption enhancement; and the enhancement mechanisms were: (1) enhance the growth of H₂-producing bacteria, (2) enhanced starch hydrolysis, (3) inhibitory H₂ consumption or release of acid inhibition. Meanwhile, experiment found that the effect of antibiotics on H₂ producing was not only related to type, but also to dosage. Even one kind of antibiotic may have completely opposite effects on H₂-producing bacteria under different dosage conditions. Inhibition of H₂ yield was highest with Levofloxacin at 6.15 mg/L, gas production was reduced by 88.77%; and enhancement of H₂ yield was highest with Penicillin G at 7.20 mg/L, the gas production increased by 72.90%. In the selection of raw material, the type and content of antibiotics demand a detailed investigation and analysis to ensure that the sustainability of H₂ yield.

Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production

The complex nature of wastewater treatment has led to search for alternative strategies such as different artificial intelligence (AI) techniques to model the various operational parameters. The present work is aimed at predicting the transmembrane pressure (TMP) as a key operational parameter in the case of anaerobic membrane bioreactor-sequencing batch reactor (AnMBR-SBR) during biohydrogen production using the adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural network (ANN). In both the models, organic loading rates (OLR) ranging from 0.5 to 8.0 g COD/L/d, effluent pH (3.6-6.9), mixed liquor suspended solid (4.6-21.5 g/L) and mixed liquor volatile suspended solid (3.7-15.5 g/L) were used as the input parameters to test TMP as an output parameter. The ANFIS model was trained using the hybrid algorithms for TMP prediction. The higher prediction performance was obtained by using the Gauss membership function with four membership numbers. A back-propagation algorithm was also employed for the feed forward training of ANN model; the best structure was a Levenberg-Marquardt training algorithm with nine neurons in the hidden layer. By employing ANFIS and ANN models, relatively a good prediction of TMP was obtained with the R² values of 0.93 and 0.88, respectively while the calculated mean square error for TMP in the ANFIS model (7.3 × 10^-3) was lower than that of ANN model (8.02 × 10^-3). The higher R² and lower MSE values for the ANFIS model exhibited a better TMP prediction performance than the ANN model. Finally, it was observed that in the sensitivity analysis of ANN model, OLR was the most important input parameter on the variation of TMP.

A Review of Biohydrogen Productions from Lignocellulosic Precursor via Dark Fermentation: Perspective on Hydrolysate Composition and Electron-Equivalent Balance

This paper reviews the current technological development of bio-hydrogen (BioH2) generation, focusing on using lignocellulosic feedstock via dark fermentation (DF). Using the collected reference reports as the training data set, supervised machine learning via the constructed artificial neuron networks (ANNs) imbedded with feed backward propagation and one cross-out validation approach was deployed to establish correlations between the carbon sources (glucose and xylose) together with the inhibitors (acetate and other inhibitors, such as furfural and aromatic compounds), hydrogen yield (HY), and hydrogen evolution rate (HER) from reported works. Through the statistical analysis, the concentrations variations of glucose (F-value = 0.0027) and acetate (F-value = 0.0028) were found to be statistically significant among the investigated parameters to HY and HER. Manipulating the ratio of glucose to acetate at an optimal range (approximate in 14:1) will effectively improve the BioH2 generation (HY and HER) regardless of microbial strains inoculated. Comparative studies were also carried out on the evolutions of electron equivalent balances using lignocellulosic biomass as substrates for BioH2 production across different reported works. The larger electron sinks in the acetate is found to be appreciably related to the higher HY and HER. To maintain a relative higher level of the BioH2 production, the biosynthesis needs to be kept over 30% in batch cultivation, while the biosynthesis can be kept at a low level (2%) in the continuous operation among the investigated reports. Among available solutions for the enhancement of BioH2 production, the selection of microbial strains with higher capacity in hydrogen productions is still one of the most phenomenal approaches in enhancing BioH2 production. Other process intensifications using continuous operation compounded with synergistic chemical additions could deliver additional enhancement for BioH2 productions during dark fermentation.