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A case study on optimization of biomass flow during single-screw extrusion cooking using genetic algorithm (GA) and response surface method (RSM).

机译:利用遗传算法(GA)和响应面法(RSM)优化单螺杆挤压烹饪过程中生物质流的案例研究。

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In the present study, response surface method (RSM) and genetic algorithm (GA) were used to study the effects of process variables like screw speed, rpm (x1), L/D ratio (x2), barrel temperature ( degrees C; x3), and feed mix moisture content (%; x4), on flow rate of biomass during single-screw extrusion cooking. A second-order regression equation was developed for flow rate in terms of the process variables. The significance of the process variables based on Pareto chart indicated that screw speed and feed mix moisture content had the most influence followed by L/D ratio and barrel temperature on the flow rate. RSM analysis indicated that a screw speed >80 rpm, L/D ratio >12, barrel temperature >80 degrees C, and feed mix moisture content >20% resulted in maximum flow rate. Increase in screw speed and L/D ratio increased the drag flow and also the path of traverse of the feed mix inside the extruder resulting in more shear. The presence of lipids of about 35% in the biomass feed mix might have induced a lubrication effect and has significantly influenced the flow rate. The second-order regression equations were further used as the objective function for optimization using genetic algorithm. A population of 100 and iterations of 100 have successfully led to convergence the optimum. The maximum and minimum flow rates obtained using GA were 13.19x10-7 m3/s (x1=139.08 rpm, x2=15.90, x3=99.56 degrees C, and x4=59.72%) and 0.53x10-7 m3/s (x1=59.65 rpm, x2=11.93, x3=68.98 degrees C, and x4=20.04%).
机译:在本研究中,使用响应面法(RSM)和遗传算法(GA)来研究过程变量如螺杆速度,rpm( x 1 )的影响, L / D 比( x 2 ),料筒温度(摄氏度; x 3 )和进料混合物的水分含量(%; x 4 ),取决于单螺杆挤出蒸煮过程中生物质的流量。针对过程变量,针对流量开发了二阶回归方程。基于帕累托图的过程变量的意义表明,螺杆速度和进料混合物的水分含量对流量的影响最大,其次是 L / D 比和料筒温度。 RSM分析表明,螺杆速度> 80 rpm, L / D 比率> 12,料筒温度> 80摄氏度,进料混合物的水分含量> 20%,可实现最大流速。螺杆速度和 L / D 比率的增加增加了阻力流,并增加了挤出机内部进料混合物的移动路径,从而导致了更大的剪切力。生物质进料混合物中约35%的脂质的存在可能已引起润滑效果,并显着影响了流速。将二阶回归方程进一步用作目标函数,以使用遗传算法进行优化。 100的总数和100的迭代次数已成功地收敛了最优值。使用GA获得的最大和最小流速为13.19x10 -7 m 3 / s( x 1 = 139.08 rpm, x 2 = 15.90, x 3 = 99.56摄氏度, x 4 = 59.72%)和0.53x10 -7 m 3 / s( x 1 = 59.65 rpm, x 2 = 11.93, x 3 = 68.98摄氏度和< i> x 4 = 20.04%)。

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