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  • 學位論文

生質原料快速熱解成生質油之設備研製及製程最佳化研究

Study on the development of a novel fast pyrolysis unit for converting biomass to bio-oil and the optimal operational conditions

指導教授 : 潘永寧
共同指導教授 : 謝國煌(Kuo-Huang Hsieh)

摘要


本研究針對生質原料化為生質油之製程,開發一創新型的快速熱解試驗機,在熱解反應系統中設計一獨特之單擠壓推拔型導螺桿,利用變頻器控制馬達轉速,再搭配鏈條傳動導螺桿,以輸送生質原料並同時進行熱解,熱解反應之生成產物,經固/氣分離器將揮發份氣體與微粒炭灰(ash)分離,然後揮發份氣體再經一冷凝器被快速冷凝成生質油,揮發份中之非凝結性氣體可做為燃料。本研究所開發之雛型機具有裝置簡單、轉化效率高、價廉且設備易放大等優點,另一方面,熱解反應器之特點係採用一單擠壓推拔型導螺桿,克服了傳統生質原料顆粒大時內部無法完全熱解之缺點。又,導螺桿之材質為自行研發配製之耐高溫、抗氧化及具低膨脹係數之球墨鑄鐵,在長期高溫熱解反應使用過程中,可以顯著減少導螺桿發生翹曲變形及卡鎖阻礙傳動之問題。 本研究選用林業廢棄物,如:茄苳樹(cedar)、樟樹(camphor)及痲瘋樹(jatropha)等,作為生質原料(biomass)進行本裝置之初步測試,探討一些關鍵熱解反應製程參數對於熱解效率(生質油之產值及熱值)之影響。製程參數包含:生質原料粒徑大小(1.0mm)、烘乾時間(2~6hr)、熱解速率(導螺桿轉速20rpm)及冰水冷凝溫度控制 在5±2oC等。初步實驗結果顯示,茄苳樹與樟樹之熱解溫度在520 oC及500 oC時,生質油可獲得最高產率,分別為42.9wt%及49.6wt%,而熱值(High Heating Value, HHV)則分別為18.73MJ/kg及19.14MJ/kg。針對痲瘋樹而言,所得到之最高生質油產率為41.6wt%,熱值(HHV)為31.41MJ/kg,其最佳熱解溫度為560 oC。 本研究亦針對所開發之創新型生質原料熱解系統,進一步探討並建立最佳製程條件,實驗上選用樟樹作為生質原料。首先,(1) 固定導螺桿轉速為20rpm,探討不同生質原料粒徑大小(0.425~3.35mm)及熱解溫度(400~550oC),對生質油產率之影響,實驗結果得知,在固定(非最佳)導螺桿轉速條件下,獲致最高生質油產率的最佳熱解溫度與生質原料粒徑大小有關,即熱解溫度在470oC時,生質原料粒徑在0.425~0.6mm及0.6~0.85mm之生質油產率分別為60.4wt% 和58.2wt%。另外,熱解溫度在500oC時,生質原料粒徑在<0.425mm、0.85~1.18mm、1.18~1.70mm、1.70~2.50mm及2.50~3.35mm之生質油產率分別為50.2wt%、57.6wt%、52.5wt%、53.0wt%和50.8wt%。其次,(2) 固定熱解溫度為500oC,探討不同導螺桿轉速(20~60rpm)及不同生質原料粒徑大小(0.425~3.35mm)對生質油產率之影響,實驗結果得知,導螺桿轉速在20rpm時,生質原料粒徑在<0.425mm、0.425~0.6mm、0.60~0.85mm及0.85~1.18mm之生質油產率分別為50.2wt%、51.8wt%、57.0wt% 和57.6wt%。另外,導螺桿轉速在40rpm時,生質原料粒徑在1.18~1.70mm、1.70~2.50 mm及2.50~3.35mm之生質油產率分別為57.8wt%、56.0wt% 和60.2wt%。綜合上述實驗結果得知,當粒徑大小(1.70~3.35mm)增大時,生質原料的熱解效率隨著導螺桿轉速(40rpm)的提高而升高,由此可知,設備的生產效能(4kg/h)一定會大大的提高。 由於生質油之產率同步受到導螺桿轉速、熱解溫度、生質原料粒徑大小等之影響,為獲致最高生質油產率之最佳製程條件,本研究進一步以田口實驗設計方法與SPSS複迴歸分析,根據上述之初步研究結果,選定關鍵製程參數之條件範圍,如:熱解溫度(X1:450 oC、470 oC和500 oC)、熱解速率(導螺桿轉速X2:20rpm、40rpm和60rpm)及生質原料粒徑大小(X3:0.425mm、1.70mm和3.35mm)等作更深入之分析探討。實驗結果得知,本創新熱解系統之最佳製程條件為︰熱解溫度為500oC、導螺桿轉速為40rpm、生質原料粒徑為3.35mm,所得到之生質油產率(Y)為60.2 wt%。以SPSS複迴歸分析得到:Ycamphor = 1409.04+0.00672X12-0.0519X22–6.253X32 + 0.0071X1X2– 0.133X1X3+0.157X2X3–6.221X1+ 87.99X3 。

並列摘要


A novel fast pyrolysis reactor for converting biomass into bio-oil has been developed in this study. A reaction chamber equipped with a single tapered screw extruder was designed for simultaneous feeding and pyrolyzing the biomass. The screw extruder is chain-driven by a motor with adjustable rotation speed. In the system development, the aim is to have the capability and simplicity to achieve high production rate, so that it can be easily scaled up with low cost. Moreover, the screw extruder was made of a special type of cast iron having excellent heat resistance and low thermal expansion coefficient, so distortion or warp and hence interference of the screw extruder can be prevented during the operation. The effect of pyrolysis temperature (ranging from 460~560oC) on bio-oil yield was investigated for cedar, camphor and jatropha trees. The experimental results, indicate that the highest bio-oil yields of 42.9 wt% can be obtained for cedar tree with 18.73 MJ/kg HHV at 520oC. For camphor and jatropha trees, the highest bio-oil yields respectively reach 49.6 wt% with 19.14 MJ/kg HHV at 500oC and 41.6 wt% with 31.41 MJ/kg HHV at 560 oC. The effects of process parameters of camphor tree biomass grain size (ranging from 0.425 to 3.35mm), rotational speed (ranging from 20 to 60rpm) and the pyrolysis temperature (ranging from 400 to 550 oC) on the yield of bio-oil were investigated in this study by using a fast pyrolysis reactor with a single tapered screw extruder. This study gives the optimal pyrolysis temperatures and rotational speeds for different grain sizes to achieve peak bio-oil yield. The results indicate that higher feed rates are required for larger grain sizes to achieve peak bio-oil yields. Taguchi experimental design method and the SPSS (Statistical Package for Social Sciences) multiple regression analysis software were employed to analyze the effects of key process parameters for camphor. The results showed that the optimal pyrolysis temperature depends on the grain size and rotation speed. (1) At fixed 20rpm feed rare, the pyrolysis temperature was varied from 400 to 550oC for different grain sizes to attain the optimal pyrolysis temperatures. For the grain size of 0.425~0.6mm and 0.6~0.85mm, the peak bio-oil yields were obtained at 470oC respectively with 60.4wt% and 58.2wt%. In addition, at 500oC the peak bio-oil yields reached 50.2wt%, 57.6wt%, 52.5wt%, 53.0wt%, 50.8wt% respectively for the grain sizes of <0.425mm, 0.85~1.18mm, 1.18~1.70mm, 1.70~2.50 mm and 2.50~3.35mm. (2) At constant temperature of 500oC, the rotation feed rate was varied from 20 to 60 rpm for different grain sizes. Based upon the experimental results was then conducted to evaluate the effect of rotational speed, ranging from 20rpm to 60rpm, on the yield of bio-oil for different biomass grain sizes. The results show that different optimal pyrolysis temperatures and rotational speeds are required for different grain sizes to achieve peak liquid yield. In addition, when larger grain sizes were used, higher feed rates were required to achieve peak bio-oil yield, implying that a significant increase in productivity is resulted. This implies that the productivity of bio-oil can be significantly increased in the current system. For the case of 2.5~3.35mm grain size, which corresponds to an optimal rotational speed of 40rpm, the production rate was estimated to be around 4kg/h. From the experiments, we can see the production yield and high heating value of the bio-oil were influenced by the process parameters (grain size, pyrolysis temperature, and biomass feed rate). The S/N ratios were obtained using Taguchi’s methodology. Here, the intended objective is to obtain higher product yields. Hence, the larger the better type S/N ratio was used to transform the yields of the bio-oil. Therefore, the SPSS multiple regression analysis was performed to obtain the regression formulae that correlate the production yield (Y, dependent variable) with the process parameters (pyrolysis temperature, X1:450oC, 470oC and 500oC , feed rate, X2:20rpm, 40rpm and 60rpm, and grain size, X3:0.425mm, 1.70mm and 3.35mm). The regression formula model is as follows: Y = a + b1X1 + b2X2 + b3X3 + e, where a is the interception, b1, b2, and b3 are the regression factors, and e is the random error. The obtained formula is: Ycamphor = 1409.04+0.00672X12–0.0519X22–6.253X32 + 0.0071X1X2–0.133X1X3+ 0.157X2X3–6.221X1+ 87.99X3

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