Li Weizhen, Jiang Yang, Rao Shu, Yin Xiuli, Jiang Enchen. Parameter optimization of corn stover blended with sawdust and sodium lignosulphonate compression experiments[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(1): 198-203. DOI: 10.11975/j.issn.1002-6819.2018.01.027
    Citation: Li Weizhen, Jiang Yang, Rao Shu, Yin Xiuli, Jiang Enchen. Parameter optimization of corn stover blended with sawdust and sodium lignosulphonate compression experiments[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(1): 198-203. DOI: 10.11975/j.issn.1002-6819.2018.01.027

    Parameter optimization of corn stover blended with sawdust and sodium lignosulphonate compression experiments

    • Abstract: Transforming to fuels is the reasonable way of utilization for large amounts of corn stover in China. Blended material compression is one of the main directions for the further study of biomass compression technology. Because sawdust and sodium lignosulphonate have higher lignin content and a variety of active groups, corn stover blended with sawdust and sodium lignosulphonate is used as raw material in order to improve the quality of fuel. In this paper the compression characteristics of corn stover blended with sawdust and sodium lignosulphonate are studied, using central composite design of response surface method. The single-pellet compression experiments are conducted on the WD-100 type electronic pressure machine. The effects of parameters including sawdust content (0-40%), sodium lignosulphonate content (2%-10%), temperature (40-160 ℃), moisture content (4%-20%), and pressure (1-9 kN) on relaxed density, specific energy consumption, and radial maximum stress are studied in detail. In the range of parameters, the variation ranges of 3 technical indicators respectively are 883-1 201 kg/m3, 5.25-23.18 kJ/kg, and 390-156 6 N. Through the regression analysis of experimental data, the response surface model is established. All the selected models of 3 technical indicators are adjusted quadratic models, and the models' adjusted R2 values respectively are 0.868, 0.879 9, and 0.934 3, and predicted R2 values respectively are 0.802 6, 0.783 5, and 0.895 6. Interaction analysis shows that temperature and moisture content, and moisture content and pressure have significant interaction effect on relaxed density, sodium lignosulphonate content and temperature, as well as radial maximum stress. After reaching a certain temperature, the increase of water content has little effect on the relaxed density. For example, when the temperature reaches 140 ℃ and the water content is more than 8%, if increasing the water content continually, the relaxed density has no obvious change and is about 1 150 kg/m3. When the water content reaches a certain level, the increase of pressure has little effect on the relaxed density. For example, when the water content reaches 14% and the pressure is greater than 6 kN, if increasing the pressure continually, the relaxed density has no obvious change and is about 1 100 kg/m3. When the temperature is less than 100 ℃, the effect of sodium lignosulphonate content on radial maximum stress is small. Otherwise, the sodium lignosulphonate content has great effect on radial maximum stress. The simultaneous increase of temperature and moisture can keep radial maximum stress constant. When the pressure keeps constant and water content increases, the radial maximum stress decreases. While water content keeps constant and pressure increases, the radial maximum stress reaches the maximum and then slightly decreases. According to the biomass fuel standards, the technical indicators are set as follows: Relaxed density is not less than 1000 kg/m3, and specific energy consumption reaches the minimum in the optimization process. The calculated optimal parameters are sawdust content of 10.15%, sodium lignosulphonate content of 8%, temperature of 95.83 ℃, moisture content of 15.83%, and pressure of 3 kN, and the prediction values of 3 technical indicators respectively are 1012 kg/m3, 7.81 kJ/kg, and 737.4 N. The verification values respectively are 1 054 kg/m3, 8.02 kJ/kg, and 685 N through validation experiment, and the prediction error is within 8%. This shows the regression model has good reference value to guide the production of biomass pellets.
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