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研究生: 朱宣嫚
Chu, Hsuan-Man
論文名稱: 不同培養條件及醣類前處理對藍綠菌Thermosynechococcus sp. CL-1之固碳及單醣類產出之影響
Effects of cultivation condition and pretreatment of carbohydrate on the CO2 fixation and production of monosaccharide by Thermosynechococcus sp. CL-1
指導教授: 朱信
Chu, Hsin
學位類別: 碩士
Master
系所名稱: 工學院 - 環境工程學系
Department of Environmental Engineering
論文出版年: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 137
中文關鍵詞: 藍綠菌Thermosynechococcus sp. CL-1氮限制二氧化碳生質酒精醣類前處理單醣
外文關鍵詞: Cyanobacteria, Thermosynechococcus sp. CL-1, nitrogen deprivation, bioethanol, pretreatment of carbohydrate, monosaccharides
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  • 因工業革命造成大氣中二氧化碳濃度上升及全球暖化,各方找尋各種碳捕捉技術,期望降低大氣中碳含量。其中生物固碳法被視為具有發展潛力及永續性,其利用光合微生物如微藻及藍綠菌行光合作用捕捉大氣中二氧化碳,固碳後之微生物生質體可轉為生質能源,同時減少使用石油所造成之二氧化碳排放。萃取完生質能源之生質體亦可作為火力發電廠之燃燒基質,達到零廢棄零汙染之永續發展循環。
    本研究結合二氧化碳捕捉技術中之生物固碳法和化學吸收法之鹼液吸收法之優點,並針對二氧化碳排放大宗之火力發電廠之煙道氣做為主要二氧化碳之來源。將氣相二氧化碳溶入鹼性吸收劑溶解為無機碳源,作為培養光合微生物之碳源,故本研究選用生長速率較高之嗜熱嗜鹼藍綠菌Thermosynechococcus sp. CL-1 (TCL-1)作為產生質能源之光合微生物。為提高固碳效率,本研究利用高照光面積之薄板反應器,探討最佳生質體密度,並進一步探討最佳培養基組成,以達最佳之生質體產率及固碳效率。根據統計,藍綠菌可累積較多的碳水化合物,可作為生質酒精之發酵基質。水解生質體為單醣為生質酒精產出過程重點之一,故本研究先針對TCL-1之碳水化合物前處理方法進行探討,並進一步研究在最佳培養基組成下之不同氮限制對於TCL-1之累積碳水化合物含量及單醣種類之影響。
    研究結果為:在生質體密度3 g/L不需控制pH值、培養基組成為5倍MF培養基(29.16 mM DIN)及3倍微量元素時有最大生質體產率155.6 ± 7.8 mg/h/L及最大CO2固定速率240.7 ± 7.8 mg CO2/L/h。並在此條件下之DIN濃度7.29 mM時有最佳生質體產率124.1 ± 2.7 mg/L/h,最大生質體增加量34.9 ± 3.0%和最佳CO2固定速率201.2 ± 2.7 mg CO2/L/h,並隨著培養基中DIN濃度降低而降低。醣類分析方面:利用雙因子-ANOVA建立最佳單醣前處理方法為一段水解(4% H2SO4)加上澱粉水解酶amyloglucosidase。故以此方法分析氮限制條件下之單醣類組成及碳水化合物總量,發現在最佳培養條件下TCL-1所累積之單醣多為葡萄糖(約89.3-91.9%)、木糖(6.4-7.3%)及阿拉伯糖(1.8-3.5%),氮限制濃度0.29 mM情況下有最大碳水化合物含量55.3 ± 0.47%、最大葡萄糖含量50.5 ± 0.47%及最大碳水化合物產率53.5 ± 12.7 mg/L/h。和其他文獻比較,可知TCL-1極具有潛力做為生質酒精之料源。以SEM圖比較缺氮(0.29 mM) 及充足氮源(29.17 mM) DIN 濃度之TCL-1型態,發現在兩種情況均有分裂。唯在0.29 mM DIN濃度之缺氮情況下,TCL-1之長度比充足氮源29.17 mM DIN濃度下長23%,此與TCL-1質量增加比例相同,故可能細胞為有分裂。假設若有細胞分裂,則在此氮限制之範圍內(0.29-7.29 mM) 以Monod equation 為TCL-1生長之動力學模式;若沒有細胞分裂,則以經驗公式Logistic regression 做為TCL-1之生質體產出、Luedeking-Piret方程式做為碳水化合物及Modified Luedeking-Piret方程式做為氮源消耗動力模式。以上兩組動力模擬結果均符合實驗結果,且皆證實TCL-1在缺氮情況下仍然成長,此可供未來實廠化之應用。

    Due to the global warming caused by the increasing CO2 concentration in atmosphere in recent decades, several carbon capture and sequestration (CCS) technologies are under researched. Biological carbon mitigation (BCM) is considered as a sustainable and potential process which uses autotrophic organisms such as microalgae and cyanobacteria to absorb CO2 from atmosphere through photosynthesis. Then large amounts of produced biomass can be used as biofuel which can reduce the usage of the gasoline. The residue of biofuel extraction can be also used as the feedstock for combustion. The whole recycle process achieves zero waste.
    In this study, the combination of the advantage of the chemical-alkaline-absorption and BCM is applied as the technology of carbon fixation from one of the main CO2 emission sources, power plant. CO2 has much more solubility in the alkaline solution and becomes HCO3- or CO32- as the carbon source for cyanobacteria. For meeting this practical requirement, thermophilic and basophilic cyanobacteria Thermosynechococcus sp. CL-1 (TCL-1) was chosen in this study. In order to increase the biomass productivity and carbon fixation rate, the higher surface-area-ratio flat panel was used as the photobioreactor (PBR) with high initial biomass concentration. After finding the best initial biomass concentration, the 23 factorial experimental design was used to study the macronutrient and micronutrient ratio of the MF medium for higher growth and carbon fixation rate. From several researches, cyanobacteria have the ability of accumulation high carbohydrate content as the feedstock for fermentation to bioethanol in certain conditions. Nitrogen deprivation seems as the condition that can accumulate the carbohydrate in several researches. Therefore, this condition was also researched in the optimized medium in this study. Furthermore, selection of the most suitable pretreatment method is crucial in achieving a high level of bioethanol production. Therefore, the pretreatment for carbohydrate of TCL-1 was studied and the content and composition of carbohydrate and monosaccharides under nitrogen deprivation were analyzed.
    The results show that the maximum biomass productivity 155.6 ± 7.8 mg/h/L and CO2 fixation rate 240.7 ± 7.8 mg CO2/L/h are reached in the condition of 3 g/L initial biomass concentration without pH control and modified MF medium of 5-fold macronutrient and 3-fold micronutrient ratio. The nitrogen deprivation in this modified medium can achieve the highest biomass productivity 124.1 ± 2.7 mg/L/h, CO2 fixation rate 201.2 ± 2.7 mg CO2/L/h, and biomass increment 34.9 ± 3.0% at the 7.29 mM initial DIN concentration.
    In carbohydrate analysis, one-step hydrolysis (4% H2SO4) followed by enzyme amyloglucosidase for TCL-1 carbohydrate pretreatment was established by two-way ANOVA. By using this pretreatment, it can be found that glucose (89.3-91.9%), xylose (6.4-7.3%) and arabinose (1.8-3.5%) are the main monosaccharides of TCL-1 in the cultivation condition of this study. The maximum carbohydrate content 55.3 ± 0.47%, maximum glucose content 50.5 ± 0.47%, and maximum carbohydrate productivity 53.5 ± 12.7 mg/L/h are reached in the 0.29 mM initial DIN condition. Moreover, from SEM, TCL-1 is 23% longer in the sufficient DIN concentration (29.17 mM) than that in deficient DIN concentration (0.29 mM). This is equivalent to the biomass increment. Therefore, TCL-1 may not have cell division. However, cell divisions were observed in both conditions. If cell division occurred, Monod equation was used as kinetic regression. If cell division didn’t occur, the biomass growth curves and carbohydrate productivity of TCL-1 were developed by empirical kinetic models, Logistic regression, and Modified Logistic regression, respectively, the carbohydrate accumulation curves by the Luedeking-Piret equation, and the DIN consumption curves by Modified Luedeking-Piret equation in the nitrogen deprivation conditions (0.29-7.29 mM) for the future application. The results for both kinetic studies show that all those equations fit well with experimental data and the cell divisions are confirmed.

    摘要 I Abstract III 致謝 VI Content A List of Figure E List of Table H Nomenclature J Chapter 1 Introduction 1 Chapter 2 Literature Review 10 2-1 Cyanobacteria 10 2-2 Photosynthesis 12 2-2-1 Light process 13 2-2-2 Calvin cycle 14 2-3 Carbohydrates 16 2-3-1 Monosaccharides 16 2-3-2 Oligosaccharides 17 2-3-3 Polysaccharides 17 2-4 Carbohydrate pretreatment 19 2-4-1 Physical pretreatment 20 2-4-2 Chemical pretreatment 21 2-4-3 Enzymatic hydrolysis 24 2-5 Influential factors for cyanobacteria growth and biomass composition 25 2-5-1 Light 25 2-5-2 Temperature 28 2-5-3 pH value 30 2-5-4 Nutrients 32 2-5-5 Salinity 37 2-6 Cyanobacteria Cultivation 39 2-6-1 Cultivation system 39 2-6-2 Photobioreactor (PBR) 41 Chapter 3 Methods and Materials 43 3-1 Thermosynechococcus sp. CL-1 (TCL-1) 43 3-2 Chemical and Materials 44 3-2-1 Medium 44 3-3 Experimental equipments 46 3-3-1 Cultivation equipments 46 3-3-2 Analysis equipments 47 3-3-3 Other equipments 49 3-4 Experimental Methods 52 3-4-1 Experimental process 52 3-4-2 Photosynthesis bioreactor 53 3-4-3 Conservation 55 3-4-4 Biomass source cultivation 55 3-4-5 Batch cultivation 57 3-5 Analysis method 58 3-5-1 Biomass concentration analysis 58 3-5-2 CO2 fixation rate analysis 59 3-5-3 Statistical analysis model 60 3-5-4 Monosaccharides analysis 61 3-5-5 SEM (Scanning electron microscopy) 64 3-6 Kinetic model 65 3-6-1 Monod equation 65 3-6-2 The Logistic regression 65 3-6-3 The Luedeking-Piret equation 67 3-6-4 Modified Luedeking-Piret equation 68 Chapter 4 Results and Discussion 70 4-1 Pre-cultivation 70 4-2 Biomass productivity improvement 75 4-2-1 The effect of initial biomass concentrations without pH control 75 4-2-2 The effect of initial biomass concentration with pH control 78 4-2-3 The effect of nutrient compositions 81 4-3 Statistical analysis 84 4-3-1 Statistical model 84 4-3-2 Regression test 87 4-3-3 Regression diagnostics 89 4-4 Monosaccharide productivity improvement 91 4-4-1 Pretreatment 91 4-4-2 Nitrogen deprivation 94 4-4-3 Scanning electron microscope (SEM) 104 4-5 TCL-1 growth kinetic model 106 4-5-1 Monod equation 106 4-5-2 Logistic regression 107 4-5-3 Luedeking-Piret equation 107 4-5-4 Modified Luedeking-Piret equation 108 4-5-5 Modified Logistic regression 115 Chapter 5 Conclusion and Suggestion 116 5-1 Conclusion 116 5-2 Suggestion 118 Reference 119 Appendix 136

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