臺灣博碩士論文加值系統

Acidithiobacillus ferrooxidans 是一種嗜酸性自營菌，可將亞鐵離子氧化為鐵離子以獲得電子及能量。本研究使用鐵離子將硫化氫氧化，並以此菌作為鐵離子的生物再生系統，串聯反應器可藉由鐵/亞鐵離子之循環再生來進行養豬場沼氣除硫之應用。文獻中及本實驗室原有的「載體式」串聯除硫反應器在高硫化氫負荷量下，會有硫沈澱物堵塞等缺點。因此，本研究新設計了「噴灑式」串聯除硫系統，能夠可靠與長期使用於沼氣除硫用途。在第一代「噴灑式」系統的實驗室規模操作中，我們測試了不同硫化氫進流速度以及液體噴灑壓力對於硫化氫去除率之影響。此測試證明該系統能在高硫化氫負荷量下 (~ 1,300 g-S m-3 h-1) 具有高去除率 (RE > 90%)，且不會有硫沈澱物堵塞。「噴灑式」系統經放大後以野生株 CP9 進行 356 天之實場除硫測試，項目包含了突增負荷及停工試驗。在氣體滯留時間 216 秒的條件下，可達 94.8% 除硫效率及 64 g-S m-3 h-1 的負荷移除能力。此系統的快速復原能力證實了經過突增負荷及停工試驗後，不會對系統能力造成長期損害。在第二代「噴灑式」串聯除硫系統中，我們改良了化學槽及儲存槽的連接方式，提升最佳反應效能並快速有效將硫沈澱物移除。在實驗室規模操作中，我們測試了噴灑液滴及化學槽體積對於硫化氫去除率之影響。在第二代系統經放大後以高效能突變株 W3進行 500 天之實場除硫測試，最佳操作參數為氣體滯流時間 73 秒時，系統有 90% 除硫效率及 302 g-S m-3 h-1的負荷移除能力。除硫沼氣經 30 kW 發電機發電測試，以含 70% 甲烷之沼氣在 220 LPM 流速下進入發電機可得最大輸出功率 27.6 kW，此條件下的熱能使用效率達 26.4%。第二代反應器菌株 W3 之最大鐵氧化效率約為 CP9 之 2 倍，藉由化學槽體積及連接方式改良，使氣體滯留時間為原本三分之一即可處理約 5 倍的進流負荷。
再者，我們也分析了 A. ferrooxidans 野生株 CP9 及突變株 W3 之差異，本研究以次世代定序系統分析 CP9 及 W3 之基因體序列及不同培養條件下的全基因表現。CP9 及 W3基因體定序後，兩者皆有 88.4% (3309 個基因中的 2829 個基因) 的序列可比對到已知基因體的 A. ferrooxidans ATCC 23270。此外，在 W3 基因體上找到 310 個不同於 CP9 基因體之鹼基對，其中 288 個位於密碼子 (coding region) 上。以基因功能 (GO term) 分析這些突變基因的功能種類，發現與全基因分析後獲得的群組種類類似，此現象符合 W3 為隨機突變下獲得之突變株。在轉錄體分析方面，六個樣本分別有 80.3% - 81.9% 的資料可比對到已知的基因。此外，本研究首次以 NGS 的方法進行 A. ferrooxidans在不同能量來源 (硫及鐵) 下的大規模基因表現分析，對此菌在硫代謝與電子傳遞系統中提供新證據。研究結果中我們觀察到 sre 轉錄組的基因會產生將元素硫還原為硫化物的酵素 sulfur reductase，並在硫培養條件下有 2–4 倍增量表現。在鐵培養條件下，A. ferrooxidans 細胞內的硫化物來源為亞硫酸物，實驗觀察到 cysJ 及 cysI 在此條件下有約 8 倍增量表現，其酵素 sulfite reductase 可能是此催化反應的關鍵蛋白。除此之外，由結果可分析出共有十個基因，在 DNA層次為突變基因，也分別在四個條件下具有增量表現。其中的 glcF 較為特殊，其表現出的 glycolate oxidase 可將 A. ferrooxidans 在固碳作用中產生的毒性副產物 glycolate，進一步代謝為無毒的 glyoxylate。此基因只在 W3 的 lag phase 中相對於 log phase 有約 3 倍增量表現，但在 CP9 中，glcF於此兩 phases 並無明顯表現差異。本研究同時以 qPCR 確認 glcF 在 W3 菌株中有此現象，發現 lag phase 表現量為 log phase 之 6 倍，進一步證實此現象，由此我們推論在 lag phase 具 8 倍以上快速生長能力的 W3，可能是因為此基因引起之增強解毒能力的結果。

The acidophilic and autotrophic Acidithiobacillus ferrooxidans oxidizes ferrous iron into ferric iron to obtain the electrons and reducing power. The ferric iron was used to be an oxidant for H2S elimination and A. ferrooxidans was immobilized in the bioreactor for the ferric iron regeneration. This combined and renewable system was applied for the biogas purification in this study. The former “carrier-based” reactors are unadvisable as H2S elimination system because they are prone to sulfur blockage problems under heavy loading operations. Therefore, the “scrubbing-style” reactor was designed for robust biogas purification under pilot-scale long-term operation. In the type I “scrubbing-style” system of laboratory scale study, various H2S inlet flow rates and spray pressures were used to evaluate the H2S removal efficiency (RE) in the chemical reactor. The high H2S RE (> 90%) demonstrated that this scrubbing-style system performed well under heavy loading (~ 1,300 g-S m-3 h-1) without severe sulfur blockage. In the scaled-up application, the type I system using A. ferrooxidans CP9 was operated for consecutive 356 days for biogas purification, including shock loading and shutdown tests. The system achieved an average RE of 94.8% with an elimination capacity (EC) value of 64 g-S m-3 h-1 under EBRT 216 s. In addition, the system recovered quickly in the shock loading and shutdown tests without permanent damage; however, the solid sulfur still caused blockage after the long-term operation. In the type II “scrubbing-style” system, the design of the connection between the chemical absorbers and the storage tank was improved to elevate the removal efficiency and quickly remove the sulfur solid. In laboratory scale study, the effects of droplet size and column size on the optimal H2S removal were characterized. In the scaled-up application, the type II system using the high growth rate strain W3 was operated for 500 consecutive days for biogas purification. The optimal conditions were an average RE of 90% with an EC of 302 g-S m-3 h-1 under EBRT 73 s. In the power generation test with 30 kW biogas generation, the maximum power output was 27.6 kW and the maximum thermal efficiency was 26.4% at a biogas supply rate of 220 litter per minute (LPM) using 70% CH4. The W3 strain in the type II system showed approximately 100% higher maximum iron oxidation rate than the CP9 in the type I system. Furthermore, only 34% EBRT was required for the type II system to deal with the 5 folds H2S loading higher than the type I system.
To further characterize the differences between A. ferrooxidans CH9 and W3, their genome and transcriptome were subjected to next-generation sequencing (NGS) analysis. The results show 88.4% of the sequenced genomes (2829 of 3309 genes) from CP9 and W3 were assembled by mapping to the reference ATCC 23270 genome. Moreover, 288 mutated paired bases were located on the 79 coding sequences (CDS), whereas 22 paired bases were located on the non-coding region of the W3 genome. The gene ontology (GO term) analysis showed similar hit term distributions for both mutant genes and total genes, which indicates that the mutation rate in each specific class is size-related and randomly mutated. In the NGS transcriptomic analysis, the total qualified paired-end sequencing reads from six samples mapped to the reference genome ranged from 80.3% to 81.9%. Also, this study is the first time to apply NGS in the differential expressed gene analysis of a different energy source for A. ferrooxidans. Collection of sulfur metabolism–related genes from the NGS data provided new evidence of candidate genes that encode key enzymes involved in unidentified pathways. For example, the sreABCD protein encoded in the sre operon was highly expressed under sulfur-growth conditions (fold change (FC) = 2–4), which was considered responsible for reducing sulfur into sulfide. Moreover, cysJ and cysI are highly expressed under iron-growth conditions (FC = 8). Thus, these genes encode proteins that catalyze the reduction of sulfite into sulfide and could involve in the only pathway for sulfide production under such conditions. Furthermore, 10 genes were found in the mutant W3 with differentially expressed under four various conditions. In particular, glcF was highly expressed (FC = 2.7) during the lag phase rather than in the log phase of the mutant W3; however, this gene was minimally expressed during both the lag phase and the log in the CP9 strain. The fold change was also examined by quantitative polymerase chain reaction (qPCR) and shows the evidence that glcF in W3 was highly expressed (FC = 6.1) in the lag phase than in the log phase. The glycolate oxidase encoded by the glc operon could catalyze the conversion of glycolate into innocuous glyoxylate in A. ferrooxidans carbon metabolism. Therefore, highly efficient detoxification could be account for the 8.5 folds higher growth rate during the lag phase of the mutant W3 than that of the CP9.

Abstract ……………………………………………………………………………...III
Acknowledgement………………………………………………………………….VIII
Chapter 1. Introduction 1
1.1 Research rationale 1
1.2 Research objectives 2
1.3 Research flow chart 3
Chapter 2. Literature reviews 4
2.1 Development of renewable energy 4
2.2 Introduction of biogas 5
2.2.1 Development of biogas resource from swine waste in Taiwan 6
2.2.2 Generation of biogas 7
2.2.3 Impurities in biogas 8
2.3 Methods for biogas purification 10
2.3.1 Physical methods for biogas purification 11
2.3.2 Biological methods for biogas purification 13
2.3.3. Chemical methods for biogas purification 15
2.3.4. Regeneration of chemical reagents 18
2.3.5. Combined chemical–biological method 20
2.4. Spraying system 21
2.5 Description and significance 24
2.6 Genome structure of Acidithiobacillus ferrooxidans 25
2.7 Recent research on the general physiology of A. ferrooxidans 25
2.7.1 Carbon metabolism 26
2.7.2 Nitrogen metabolism 27
2.7.3 Aerobic ferrous iron oxidation 28
2.7.4 Sulfur oxidation 29
2.8 Anaerobic growth of A. ferrooxidans 30
2.9 Mixotrophic growth of A. ferrooxidans 31
2.10 Industrial applications of A. ferrooxidans on bioleaching 32
Chapter 3. Materials and methods 35
3.1 Microorganism isolation identification 35
3.1.1 Culture media of A. ferrooxidans 35
The compositions of broth and solid media for the growth of A. ferrooxidans are listed in the table 3.1. 35
3.1.2 Immobilization process of A. ferrooxidans 35
3.1.3 Analysis of the cell density from inoculated carrier 36
3.1.4 Analysis of the cell density from broth media 36
3.2 Chemical-biological H2S elimination systems 36
3.2.1 Carrier-based style reactor – 50 pigs pilot scale 37
3.2.2 Scrubbing-style reactor type I – lab scale 37
3.2.3 Scrubbing-style reactor type I – 750 pigs pilot scale 38
3.2.4 Scrubbing-style reactor type II – lab scale 39
3.2.5 Scrubbing-style reactor type II – 3,000 pigs pilot scale 39
3.3 Operation of the carrier-based style reactor (pilot, 50 pigs) 40
3.4 Operation of the scrubbing-style reactor type I (lab scale) 40
3.4.1 Effects of H2S inlet flowrate and liquid spraying pressure on H2S removal (chemical reaction only) 40
3.4.2 Iron oxidation rate analysis in bioreactor for the scale-up design 41
3.5 Operation of the scrubbing-style reactor type II (pilot, 750 pigs) 41
3.6 Operation of the scrubbing-style reactor type II (lab scale) 42
3.6.1 Effect of the spraying droplet size on H2S elimination in lab scale study 42
3.6.2 Effect of the absorber size on H2S elimination in lab scale study 42
3.6.3 Evaluation of the optimal pH on H2S elimination and cell growth in lab scale study 43
3.7 Operation of the scrubbing-style reactor II (pilot, 3,000 pigs) 43
3.8 Analytical methods 44
3.8.1 Ferrous iron (Fe2+) determination 44
3.8.2 ferric iron (Fe3+) determination 44
3.8.3 total iron determination 45
3.8.4 elemental sulfur (S0) determination 45
3.8.5 sulfide (S2-) determination 46
3.8.6 sulfate (SO42-) determination 48
3.8.7 Hydrogen sulfide (H2S) determination 48
3.8.8 methane (CH4) determination 49
3.8.9 carbon dioxide (CO2) determination 49
3.8.10 moisture and temperature determination 49
3.8.11 pressure drop determination 49
3.8.12 sprayed droplet size determination by particle image velocimetry (PIV) 50
3.8.13 Bioaerosol analysis 50
3.8.14 Empty bed retention time (EBRT) calculation 51
3.8.15 H2S loading value calculation 51
3.8.16 Biological ferrous iron oxidation rate in the bioreactor 51
3.8.17 Power generation analysis 52
3.9 Microbial population analysis by DGGE 52
3.9.1 Genomic DNA extraction from the carrier sample 52
3.9.2 Nested PCR 53
3.9.3 DNA electrophoresis and gel extraction 53
3.9.4 Building the DGGE gel assembly 54
3.9.5 Pouring the gel 54
3.9.6 Running the gel 55
3.9.7 Staining the gel 56
3.9.8 Sequencing and strain identification 56
3.10 Random mutagenesis for high growth rate A. ferrooxidans selection 57
3.10.1 NTG random mutagenesis (Delić et al., 1970) 57
3.10.2 Selection of the high growth rate strain by the batch broth media 58
3.10.3 Selection of the high growth rate strain by the continuous culture 58
3.11 Comparison of the growth performances between the mutant and CP9 strains 59
3.11.1 Ferrous iron oxidation ability test in batch culture 59
3.11.2 Effect of washout rate on growth performance in continuous culture 59
3.12 Analysis of genomics and transcriptomics on A. ferrooxidans – sample preparation 60
3.12.1 A. ferrooxidans genomic DNA extraction 60
3.12.2 A. ferrooxidans total RNA extraction 60
3.12.3 RNA Formaldehyde-agarose gel electrophoresis 62
3.13 Analysis of transcriptomics on A. ferrooxidans – Next Generation Sequencing (NGS) 64
3.13.1 Quality control of RNA samples for NGS (Yourgene Bioscience, Taiwan) 64
3.13.2 Construction of cDNA library for NGS 64
3.13.3 Library cluster formation and sequencing by Illumia Hiseq 2000 65
3.13.4 Analysis of the NGS data, assembly transcripts and estimates their abundances 66
3.13.5 Volcano plot 66
3.13.6 Hypergeometric probability test 67
3.14 Analysis of transcriptomics on A. ferrooxidans – The qPCR confirmation 68
3.14.1 Reverse transcriptase PCR for cDNA preparation 68
3.14.2 Quantitative PCR (qPCR) 68
Chapter 4. Results and discussion 70

Results and discussion of Part I 70
Part I. Development and operation of the type I scrubbing chemical-biological biogas purification systems
4.1 Development of solid media for A. ferrooxidans plating 70
4.2 Carrier-based style reactor – 50 pigs scale 71
4.2.1 Effects of biogas inlet rate on the Fe2+ / Fe3+ concentration 71
4.2.2 Effects of H2S loading on the efficiency of H2S removal 73
4.2.3 Variations in pH and cell density 73
4.3 Scrubbing-style reactor I – lab scale 74
4.3.1 Effects of H2S inlet flow rates and spraying pressures on the efficiency of H2S removal 74
4.3.2 Effects of H2S loading on the H2S removal efficiency 75
4.4 Pilot-scale scrubbing-style reactor I for H2S removal from biogas 76
4.4.1 Effect of inlet H2S concentration and loading on the performance of pilot-scale scrubbing-style reactor I 77
4.4.2 Fe2+ / Fe3+ ratio variation in the chemical-biological system 79
4.4.3 pH and pressure drop variation during the operation processes 80
4.4.4 Bacterial cell numbers and community analysis 82

Results and discussions of Part II 86
Part II. Development and operation of type II scrubbing chemical-biological biogas purification systems
4.5 Scrubbing-style reactor type II – lab scale 86
4.5.1 Effect of the spraying droplet size and the absorber size on H2S elimination in the lab-scale study 86
4.5.2 Effect of the absorber size on H2S elimination in the lab-scale 88
4.5.3 Evaluation of the optimal pH on H2S elimination and cell growth in the lab-scale study 90
4.6 Scrubbing-style reactor II – 3,000 pigs scale 91
4.6.1 Effect of EBRT and loading on the performance of the hybrid system in the field scale operation 91
4.6.2 Analysis of the ferrous / ferric iron ratio variation and the biological oxidizing capacity in the field scale operation 93
4.6.3 Pressure drop and cell number variation in the field scale operation 94
4.6.4 Microbial communities analysis by DGGE 97
4.6.5. Effects of methane concentration and biogas supply rate on power generation 97

Results and discussions of Part III 100
Part III. Selection of the high growth rate A. ferrooxidans mutant strain and mutant sites localization by NGS whole genome sequencing
4.7 Selection of the high growth rate A. ferrooxidans strain 100
4.8 Genomic properties 101
4.9 Contig assembly and mapping of genomic sequence 102
4.10 Localization of W3 mutant sites 103
4.11 Functional annotation and classification of W3 mutant genes 104
4.12 Analysis of the differentially expressed genes in the noncoding regions of A. ferrooxidans mutant W3 104

Results and discussions of Part IV 107
Part IV. Transcriptome profiling of differential expression involved in strains (CP9 and W3), cell phase (log and lag), and energy source (Fe and S) with NGS
4.13 Transcriptome properties 107
4.14 Contig assembly and mapping of transcriptomic sequence 107
4.15 Functional and differential expression analysis of transcriptomic data 108
4.15.1 Lag - W3/CP9 condition 109
4.15.2 Log - W3/CP9 condition 110
4.15.3 CP9 - log/lag condition 111
4.15.4 W3 - log/lag condition 112
4.15.5 CP9 – S/Fe condition 113
4.15.6 W3 – S/Fe condition 114
4.16 Differentially expressed genes under S or Fe energy sources 115
4.16.1 Conversion of sulfur to sulfite 117
4.16.2 Conversion of sulfur to sulfide 118
4.16.3 Conversion of sulfite to sulfide 118
4.16.4 Conversion of sulfate into adenylylsulfate (APS) and then into sulfite 119
4.17 Analysis of the cross-relationship between gene mutation and differential expression 119
Chapter 5 Conclusions 122
5.1 Part I – Development and operation of the type I scrubbing chemical–biological biogas purification systems 122
5.2 Part II – Development and operation of the type II scrubbing chemical-biological biogas purification systems 123
5.3 – Part III Selection of the high growth rate A. ferrooxidans mutant strain and mutant sites localization by NGS whole genome sequencing 124
5.4 – Part IV Transcriptome profiling of differential expression involved in strains (CP9 and W3), cell phase (log and lag), and energy source (Fe and S) with NGS 125
5.5 Prime cost analysis in the biogas development in Taiwan 127
Reference…………………………………………………………………………..208
Publications………………………………………………………………………..222

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