Optimization of Forming Process and Storage and Transportation Conditions of Sunflower Straw Solid Fuel
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摘要:
向日葵在北方被广泛种植，其秸秆纤维强度高、油性好，适于制作固体燃料。为提高燃料成型效果，降低储运过程中燃料损耗率，采用田口法优化成型工艺参数，提高燃料品质。试验模拟储藏环境研究储藏湿度对燃料表面形貌、密度、全水分及磨损率的影响，确定储藏条件。以LBT-5024型振动台模拟运输振动状况，研究燃料包装材料和运输振动频率对向日葵秸秆固体燃料振动后质量缺损的影响，确定最佳包装材料及合理的振动频率。结果表明，向日葵秸秆固体燃料成型工艺参数对密度的贡献率分别为含水率59.26%、温度1.59%、压力32.96%及粒径0.59%，最佳工艺组合是含水率9%、温度110 °C、压力120 MPa、粒径0.16～0.63 mm，为保证固体燃料的物理品质符合使用要求，储藏湿度＜60%RH，包装材料的选择顺序为铁箱>木箱>蛇皮袋>纸箱，运输过程中应将振动频率控制在2 Hz左右。
Abstract:Sunflower has a vast planting area in the north, and its straw fiber has high strength and good oiliness, which is suitable for making solid fuel．In order to improve fuel molding effect and reduce fuel loss rate during storage and transportation, the Taguchi method was used to optimize molding process parameters and improve fuel quality．Storage environment was simulated to study effect of storage humidity on surface morphology, density, total moisture and wear rate of fuel, and storage conditions were determined．The LBT-5024 vibration table was used to simulate transportation vibration, and influence of fuel packaging material and transportation vibration frequency on quality defect of sunflower straw solid fuel after vibration was studied, and optimal packaging material and reasonable vibration frequency were determined．Results showed that, contribution rate of sunflower straw solid fuel molding process parameters to density was moisture content 59.26%, temperature 1.59%, pressure 32.96% and particle size 0.59%, optimal process combination was moisture content 9%, temperature 110 °C, pressure 120 MPa, particle size 0.16−0.63 mm．In order to ensure that physical quality of solid fuel could meet requirements for use, storage humidity should be lower than 60%RH, and choice of packaging materials was iron box > wooden box > snakeskin bag > carton．Vibration frequency was controlled at about 2 Hz．
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图 2 储藏环境对全水分差值的影响
Figure 2. Influence of storage environment on total moisture difference
表 1 燃料成型试验结果及其信噪比
Table 1. Fuel molding test results and signal-to-noise ratio
序号 因素 密度/（g·cm−3） 信噪比 含水率A 温度B 压力C 粒径D y1 y2 y3 R1 −1 −1 −1 −1 0.939 0.953 0.960 −0.441 R2 −1 0 1 0 1.045 1.040 1.042 0.360 R3 −1 1 0 1 0.860 0.867 0.844 −1.342 R4 0 −1 1 1 0.893 0.886 0.907 −0.962 R5 0 0 0 −1 1.061 1.060 1.064 0.520 R6 0 1 −1 0 0.893 0.906 0.891 −0.948 R7 1 −1 0 0 0.934 0.940 0.911 −0.648 R8 1 0 −1 1 0.815 0.823 0.804 −1.789 R9 1 1 1 −1 0.943 1.029 1.034 −0.006 
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表 2 比效应值
Table 2. Ratio effect value
因素/水平 $\left({ {\displaystyle \frac{S}{N} } }\right)_{I}^{i}$ ${ {\displaystyle M} }_{I}^{i}$ j=1 j=2 j=3 A/ −1 −0.441 0.360 −1.342 −0.474 A/ 0 −0.962 0.520 −0.948 −0.463 A/ 1 −0.648 −1.789 −0.006 −0.814 B/ −1 −0.441 −0.962 −0.648 −0.684 B/ 0 0.360 0.520 −1.789 −0.303 B/ 1 −1.342 −0.948 −0.006 −0.765 C/ −1 −0.441 −0.948 −1.789 −1.059 C/ 0 −1.342 0.520 −0.648 −0.490 C/ 1 0.360 −0.962 −0.006 −0.203 D/ −1 −0.441 0.520 −0.006 0.024 D/ 0 0.360 −0.948 −0.648 −0.412 D/ 1 −1.342 −0.962 −1.789 −1.364
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表 3 方差分析结果
Table 3. ANOVA results
方差来源 含水率A 温度B 压力C 粒径D 误差 F 138.58 4.69 77.53 2.37 − P <0.0001 0.0230 <0.0001 0.1220 − $ {\rho }_{F} $/% 59.26 1.59 32.96 0.59 5.6
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