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A Case Study on Maximizing Aqua Feed PelletProperties Using Response SurfaceMethodology and Genetic Algorithm

机译:利用响应面法和遗传算法最大化水产饲料颗粒性能的案例研究

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Aims: The present case study is on maximizing the aqua feed properties using response surface methodology and genetic algorithm.Study Design: Effect of extrusion process variables like screw speed, L/D ratio, barrel temperature, and feed moisture content were analyzed to maximize the aqua feed properties like water stability, true density, and expansion ratio.Place and Duration of Study: This study was carried out in the Department of Agricultural and Food Engineering, Indian Institute of Technology, Kharagpur, India.Methodology: A variable length single screw extruder was used in the study. The process variables selected were screw speed (rpm), length-to-diameter (L/D) ratio, barrel temperature (oC), and feed moisture content (%). The pelletized aqua feed was analyzed for physical properties like water stability (WS), true density (TD), and expansion ratio (ER). Extrusion experimental data was collected by based on central composite design. The experimental data was further analyzed using response surface methodology (RSM) and genetic algorithm (GA) for maximizing feed properties.Results: Regression equations developed for the experimental data has adequately described the effect of process variables on the physical properties with coefficient of determination values (R2) of > 0.95. RSM analysis indicated WS, ER, and TD were maximized at L/D ratio of 12-13, screw speed of 60-80 rpm, feed moisture content of 30-40%, and barrel temperature of ≤ 80oC for ER and TD and > 90oC for WS. Based on GA analysis, a maximum WS of 98.10% was predicted at a screw speed of 96.71 rpm, L/D ratio of 13.67, barrel temperature of 96.26oC, and feed moisture content of 33.55%. Maximum ER and TD of 0.99 and 1346.9 kg/m3 was also predicted at screw speed of 60.37 and 90.24 rpm, L/D ratio of 12.18 and 13.52, barrel temperature of 68.50 and 64.88oC, and medium feed moisture content of 33.61 and 38.36%.Conclusion: The present data analysis indicated that WS is mainly governed by barrel temperature and feed moisture content, which might have resulted in formation of starch-protein complexes due to denaturation of protein and gelatinization of starch. Screw speed coupled with temperature and feed moisture content controlled the ER and TD values. Higher screw speeds might have reduced the viscosity of the feed dough resulting in higher TD and lower ER values. Based on RSM and GA analysis screw speed, barrel temperature and feed moisture content were the interacting process variables influencing maximum WS followed by ER and TD.
机译:目的:本案例研究是使用响应面方法和遗传算法来最大化水产饲料的性能。研究设计:分析挤压工艺变量(如螺杆速度,L / D比,料筒温度和饲料水分含量)的影响以最大程度地提高水产饲料的性质,例如水稳定性,真实密度和膨胀比。研究地点和持续时间:该研究在印度哈拉格布尔的印度技术学院农业与食品工程系进行。方法:可变长单螺杆研究中使用了挤出机。选择的工艺变量是螺杆速度(rpm),长径比(L / D),料筒温度(oC)和进料水分含量(%)。对粒状水产饲料的物理性质进行了分析,例如水稳定性(WS),真实密度(TD)和膨胀比(ER)。通过基于中央复合设计收集挤出实验数据。使用响应面方法(RSM)和遗传算法(GA)进一步分析了实验数据,以使饲料性能最大化。结果:为实验数据开发的回归方程式充分说明了工艺变量对物性的影响以及确定系数。 (R2)> 0.95。 RSM分析表明WS,ER和TD在L / D比为12-13,螺杆速度为60-80 rpm,进料水分含量为30-40 %,料筒温度≤80oC时达到最大。对于WS> 90oC。根据GA分析,在96.71 rpm的螺杆转速,13/67的L / D比,96.26oC的料筒温度和33.55 %的进料含水量的情况下,预计最大WS为98.10%。在60.37和90.24 rpm的螺杆速度,12/18和13.52的L / D比,68.50和64.88oC的料筒温度以及33.61和38.36 的中等进料含水量的情况下,预计最大ER和TD分别为0.99和1346.9 kg / m3。结论:目前的数据分析表明,WS主要受料筒温度和饲料水分含量的影响,这可能由于蛋白质的变性和淀粉的糊化而导致淀粉-蛋白质复合物的形成。螺杆转速,温度和进料水分含量控制着ER和TD值。较高的螺杆转速可能会降低饲料面团的粘度,从而导致较高的TD和较低的ER值。根据RSM和GA分析,螺杆转速,料筒温度和进料水分是影响最大WS的相互作用过程变量,其次是ER和TD。

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