Abstract:The influence of different pre-treatments was studied in order to observe the effects of temperature, pH and treatment time on Waste Activated Sludge (WAS) solubilization, and anaerobic digestion of pre-treated sludge. Results showed that thermo-chemical pre-treatments were the most efficient on Chemical Oxygen Demand (COD) solubilization, which could reach 83% at 170• C with pH=12. Yet, increase in COD solubilization in thermo-chemical pre-treatment was not linked to an increase in soluble Volatile Solids (VS) as optimal conditions were 170• C, and 130• C with pH=10, for this criterion. So, temperature was found to be the most influential parameter on COD and VS solubilization.
Biodegradability batch anaerobic tests confirmed results obtained on WAS solubilization, that is to say that 170• C and 130• C with pH=10 were optimal conditions, with respectively 45% and 21% of anaerobic digestion enhancement. Thus these two conditions were chosen for sludge treatment before continuous anaerobic digestion. Results, after stabilization have shown a better efficiency of 170• C compared with 130• C with pH=10 pre-treatment, since after anaerobic digestion it led to 71% of COD degradation and 59% of Total Solids (TS) degradation, with an improvement of 54% in biogas production. The main differences between those two pre-treatments could be due to the pre-treatments themselves more than to an effect on anaerobic digestion, because the first one led to a partial loss of WAS COD (near 17% of initial COD) and the second one to an increase in TS due to addition of base.
The microbial community structure of pig manure slurry (PMS) was determined with comparative analysis of 202 bacterial, 44 archaeal and 33 eukaryotic small subunit (SSU) rDNA partial sequences. Based on a criterion of 97% of sequence similarity, the phylogenetic analyses revealed a total of 108, eight and five phylotypes for the Bacteria, Archaea and Eukarya lineages, respectively. Only 36% of the bacterial phylotypes were closely related (>or=97% similarity) to any previously known sequence in databases. The bacterial groups most often represented in terms of phylotype and clone abundance were the Eubacterium (22% of total sequences), the Clostridium (15% of sequences), the Bacillus-Lactobacillus-Streptococcus subdivision (20% of sequences), theMycoplasma and relatives (10% of sequences) and the Flexibacter-Cytophaga-Bacteroides (20% of sequences). The global microbial community structure and phylotype diversity show a close relationship to the pig gastrointestinal tract ecosystem whereas phylotypes from the Acholeplasma-Anaeroplasma and the Clostridium purinolyticum groups appear to be better represented in manure. Archaeal diversity was dominated by three phylotypes clustering with a group of uncultured microorganisms of unknown activity and only distantly related to the Thermoplasmales and relatives. Other Archaea were methanogenic H2/CO2 utilisers. No known acetoclastic Archaea methanogen was found. Eukaryotic diversity was represented by a pluricellular nematode, two Alveolata, a Blastocystis and an Entamoebidae. Manure slurry physico-chemical characteristics were analysed. Possible inhibitory effects of acetate, sulphide and ammonia concentrations on the microbial anaerobic ecosystem are discussed.
The exact extent of microbial diversity remains unknowable. Nevertheless, fingerprinting patterns [denaturing gradient electrophoresis (DGE), single-strand conformation polymorphism (SSCP)] provide an image of a microbial ecosystem and contain diversity data. We generated numerical simulation fingerprinting patterns based on three types of distribution (uniform, geometric and lognormal) with a range of units from 10 to 500,000. First, simulated patterns containing a diversity of around 1000 units or more gave patterns similar to those obtained in experiments. Second, the number of bands or peaks saturated quickly to about 35 and were unrelated to the degree of diversity. Finally, assuming lognormal distribution, we used an estimator of diversity on in silico and experimental fingerprinting patterns. Results on in silico patterns corresponded to the simulation inputs. Diversity results in experimental patterns were in the same range as those obtained from the same DNA sample in molecular inventories. Thus, fingerprinting patterns contain extractable data about diversity although not on the basis of a number of bands or peaks, as is generally assumed to be the case.
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