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Characteristics And Fermentation Optimization Of Efficient Flocculant-producing Bacterium

Posted on:2013-08-31Degree:MasterType:Thesis
Country:ChinaCandidate:D WuFull Text:PDF
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The performance of bio-flocculant fermented by flocculant-producing bacteriahas been focused on for decades, but the problems of its low yield, a shortage ofcharacteristic parameter and the complex of industrial fermentation process hindered theproduction of large quantities of bio-flocculant. In this study,an efficient high-yieldflocculant-producing bacterium MFX was screened. The fermentation parameters ofMFX were optimized by applying BP neural network, and make a trial of predictingindustrial fermentation process based on the model built by BP neural network. At thesame time, the fermented bioflocculant was dealt with sulfamethoxazole,and theflocculating removal efficiency of sulfamethoxazole was detected.In this study,an efficient high-yield flocculant-producing bacterium MFX wasscreened. Compared with other flocculant-producing bacteria, the flocculation rate andyield of MFX were high. After molecular identified, flocculant-producing bacteria MFXbelongs to Klebsiella. sp, which the similarity was99%. And its morphologicalcharacteristics, physiological and biochemical characteristics matched the features ofKlebsiella. sp.The bio-flocculant dry agent yield of MFX achieved4.5g/L, which washigher than that of F+2g/L. The yield of flocculant-producing bacterium MFX washigh, and further study is needed.The fermentation parameters were optimized through single factor experimentand orthogonal experiments. Optimized fermentation conditions were33℃, shakerspeed140r/min, pH value of7.5, seed solution volume7ml, fermentation time2h.Under above conditions, the flocculating rate and yield were89.04%and1.907g/L.Meanwhile, influence of fermentation parameters was quantified by orthogonalexperiment. Temperature, shaking speed and pH value were selected as the neuralnetwork input, flocculating rate and yield as the neural network output, and the trainingsamples were designed. After repeated training, high accuracy and few error predictionmodel was built, and the optimum fermentation conditions were temperature33℃,shaker speed141r/min, pH value of7.90. Under these fermentation conditions, actualflocculating rate and yield were92.67%and2.1809g/L. Efficient and high yieldfermentation conditions of flocculant-producing bacterium MFX were achieved. Fewsimulation error of using this model to predict the fermentation process of flocculant-producing bacteria F+in the fermenter proved that BP neural network could preestimate industrial fermentation process well.Also, in this study, bio-flocculant was dealt with sulfamethoxazole, and primarilyachieved the optimum flocculating conditions. Optimized conditions were pH value of5, flocculant dosing volume of5mL, coagulant dosing volume of0.5mL, flocculation time of1h, temperature35℃. Under this flocculation conditions, the actual removal rateof sulfamethoxazole was67.82%. The impact factor of removal of the sulfamethoxazolein sewage was optimized, and its optimum flocculating conditions were pH value of5,flocculant dosing volume of8mL, coagulant dosing volume of0.2mL. Under thisflocculation conditions, the actual removal rate was53.27%. It is noted that bio-flocculant have a good effect on the removal of sulfamethoxazole in sewage.
Keywords/Search Tags:flocculant-producing bacteria, Klebsiella sp., flocculating properties, BP neural network, sulfamethoxazole
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