How the acetate and propionate accumulation impact anaerobic syntrophy during methane formation is not well understood. To investigate such effect, continuous acetate (35 g/L), propionate (11.25 g/L) and bicarbonate (30 g/L) supplementation were used during mesophilic anaerobic digestion. The high throughput sequencing (16S rRNA and mcrA), Real-Time quantitative PCR, and stable carbon isotope fingerprinting were applied to investigate the structure and activity of microbial community members. The results demonstrated that the abundance of syntrophic acetate oxidizing bacteria exhibited a gradual decrease coupled with heavier stable carbon isotopic signature of methane (δ 13CH4) in the three reagents impacted reactors. The increased acetate and propionate concentrations exerted negative influence on biogas production but the relatively stable hydrogenotrophic methanogens together with syntrophic acetate/propionate oxidizing bacteria kept the stable methane formation facing acetate and propionate accumulation. The functional genes copy number of the hydrogenotrophic Methanocellaceae and Methanomicrobiaceae correlated significantly with δ 13CH4 (R2 > 0.74), but only the abundance of Methanocellaceae fitted well with δ 13CH4 (p < 0.05). The δ 13CH4 signatures can predict methanogenesis, as it directly reflects the main methanogenic pathway; yet, further investigation of isotope fractionation in acetate/propionate coupled with δ 13CH4 is needed. Collectively, these results provide deep insight into anaerobic syntrophy and reveal changes of synergistic relationships, both of which may contribute to the stability of biogas reactors.
- MeSH
- acetáty MeSH
- anaerobióza MeSH
- bioreaktory * MeSH
- methan MeSH
- propionáty * MeSH
- RNA ribozomální 16S genetika MeSH
- Publikační typ
- časopisecké články MeSH
The large amounts of ammonia emissions generated from industrial production have caused serious environmental pollution problems, such as soil acidification, eutrophication, the formation of fine particles and changes in the global greenhouse balance, and also greatly endanger human health. At present, effectively reducing ammonia emissions or recovering ammonia is still a huge challenge. Ionic liquids (ILs) as a new class of green solvent have been introduced for ammonia absorption with great potential, but a huge number on combination systems of ILs lead to the difficulty of measuring the ammonia solubility in all ILs by experiments (e.g., danger and cost). Hereby, this study proposed a novel approach for estimating the ammonia solubility in different ILs. A predictive model was developed based on the novel Algorithm - extreme learning machine (ELM) and the molecular descriptors of electrostatic potential surface areas (SEP) as input parameters. Besides, 502 data points of ammonia solubility in 17 ILs were gathered with a wide range of pressure and temperature. For the total set, the determination coefficient (R2) and the average absolute relative deviation (AARD) of the developed model were 0.9937 and 2.95%, respectively. The regression plots revealed good consistency between predictive and experimental data points. Results show the good performance and reliability of the developed model, indicating that the proposed approach can be potentially applied for screening reasonable ILs to absorb ammonia from chemical industry processes.
- MeSH
- amoniak MeSH
- iontové kapaliny * MeSH
- lidé MeSH
- reprodukovatelnost výsledků MeSH
- rozpouštědla MeSH
- teplota MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Ionic liquids (ILs) have attracted increasing attention both in the scientific community and the industry in the past two decades. Their risk of being inevitable released to ecosystem lights up the urgent research on their toxicity to the environment. To reduce the time and capital consumption on testing tremendous ILs ecotoxicity experimentally, it is essential to construct predictive models for estimating their toxicity. The objective of this study is to provide a new approach for evaluating the ecotoxicity of ILs. A comprehensive ecotoxicity dataset for Vibrio fischeri involving 142 ILs, was collected and investigated. The electrostatic potential surface areas (SEP) of separate cations and anions of ILs were firstly applied to develop predictive models for ecotoxicity on Vibrio fischeri. In addition, an intelligent algorithm named extreme learning machine (ELM) was employed to establish the predictive model. The squared correlation coefficients (R2), the average absolute error (AAE%) and the root-mean-square error (RMSE) of the developed model are 0.9272, 0.2101 and 0.3262 for the entire set, respectively. The proposed approach based on the high R2 and low deviation has remarkable potential for predicting ILs ecotoxicity on Vibrio fischeri.
