Free nitrous acid (FNA) pretreatment and alkyl polyglucoside (APG) addition are environmental-friendly methods to enhance waste activated sludge (WAS) anaerobic fermentation (AF) performances. In the current study, FNA pretreatment combined with APG addition was applied to recover short-chain fatty acids (SCFAs) from sludge AF. Experimental results indicated that 0.653 mg/L FNA+0.075 g/g VSS APG treatment could synergistically achieve the maximum total SCFAs yield of 324.94 mg COD/g VSS at day 5 of AF, which was much more than those of sole FNA or sole APG treatment. Mechanisms analysis indicated that FNA+APG treatment could promote WAS solulibization, hydrolysis and acidogenesis, while inhibit methanogenesis severely. Microbial community analysis results demonstrated that SCFAs producer, including Enterococcus and Clostridium, became the dominant genus in the FNA+APG reactor. Finally, AF liquor after ammonia removal was applied for batch-mode polyhydroxyalkanoates (PHAs) synthesis, and the PHAs yield increased to a maximum 44.44% of biomass after five operation periods.
In order to improve the lipid-rich anaerobic fermentation (AF) performance, sodium hydroxide (NaOH) conditioning was applied in this study. Experimental results demonstrated that 96% WAS (v/v) at pH 7.5 could achieve the maximum SCFAs yield (1180.05 mg/g VS fed ) at day 12, and ortho-P content in the AF liquor (AFL) was more than that of without NaOH addition. Anaerovibrio , Sporanaerobacter and Aminobacterium were the major genus in the lipid-rich AF system, which could degrade lipids, proteins and polysaccharides efficiently. Phosphorus (P) in the AFL from 96% WAS+pH 7.5 reactor was recovered through vivianite crystallization method, and 86% of P could be recovered, with 91% of SCFAs remaining in the post-AFL. Meanwhile, analysis results verified the vivianite formation in the P precipitate products. This study provided a new idea to achieve SCFAs and P simultaneous recovery from lipidic waste by means of AF with NaOH conditioning and vivianite crystallization.
ABSTRCTModified industrial waste steel slag (SS) activating persulfate (PS) for degrading rhodamine B (RhB) in wastewater was brought in this paper. The high-temperature modified steel slag (HTM-SS) activating PS demonstrated the most effective performance, achieving a degradation efficiency of 93.0% for 20 mg/L RhB within 90 minutes at 25 °C. The degradation efficiency was close to the predicted results (93.8%) obtained through multi-factor fitting experiments based on response surface design, demonstrates the excellent simulation and prediction capability of the response surface model. Remarkably, even after 8 cycles of reuse of HTM-SS, the degradation efficiency for RhB remained above 90%, indicating exceptional stability. Characterizations indicated that the improved degradation of RhB by HTM-SS is linked to the presence of transition metal oxides and CaO on HTM-SS surface. These transition metal oxides can activate persulfate (PS) to produce SO4-·, while CaO can undergo hydrolysis to create alkaline conditions, promoting the formation of 1O2 and ·OH and reducing the leaching of transition metals. Finally, we demonstrated through liquid chromatography-mass spectrometry (LC-MS) that the primary degradation pathway of RhB involves non-radical (1O2) and radical-mediated reactions (SO4-· and ·OH), resulting in the mineralization of RhB into small organic or inorganic compounds.
Object detection is to identify objects and then find some objects of interest. With the development of computers, target detection has evolved from traditional detection methods to artificial intelligence methods, and the latter are mainly based on some algorithms of deep learning. This paper mainly tests the treated sewage. First, the neural network and convolutional neural network algorithms in deep learning are studied, and then a target detection system is built based on these two algorithms. Finally, the treated sewage is detected and then compared with that of the traditional target detection system. The experimental results show that the target detection system of the convolutional neural network algorithm has a very stable recognition rate for the treated sewage, swinging around 70%, and the amplitude is not large. However, the target detection system of the neural network algorithm is not very stable in the recognition rate of the treated sewage, and the recognition rate is about 60%.
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