基于均匀设计—偏最小二乘回归建模的秸秆型颗粒饲料部分加工参数研究

目的]探讨秸秆型颗粒饲料的部分最优加工参数,为甘肃省河西地区加工生产秸秆型颗粒饲料提供参考.方法]采用均匀设计方法设计两种类型的秸秆型颗粒饲料：（A）秸秆颗粒饲料和（B）秸秆精粗颗粒饲料,通过研究不同组别的秸秆型颗粒饲料加工过程中制粒水分、秸秆粉碎粒度、粘结剂添加比例、干燥冷却时间等因素对颗粒密度、成型率及水分等指标的影响,建立回归方程,并采用偏最小二乘回归分析法确定两种秸秆型颗粒饲料的最优加工参数.结果]当制粒水分为16.63％、秸秆粉碎粒度为5.0 mm、粘结剂添加比例为0.58％、干燥冷却时间为15.74 min时,秸秆颗粒饲料3个成品指标的综合效果最佳;当制粒水分为12.00％、秸秆粉碎粒度为2.0 mm、粘结剂添加比例为1.92％、干燥冷却时间为25.00 min时,秸秆精粗颗粒饲料3个成品指标的综合效果最佳.结论]在制粒机本身性能不变的条件下,饲料配方、粉碎粒度、调质及冷却干燥时间等因素对秸秆型颗粒饲料质量指标均有一定影响;利用均匀设计—偏最小二乘回归建模设计试验,可在取得较好代表性结果的同时减少试验工作量,提高试验效率.

Partial processing parameters of straw pellet feed based on uniform design and partial-least square regression modeling

Objective]The optimal partial processing parameters of straw pellet feed were studied in order to provide references for the processing and production of straw pellet in Hexi region of Gansu Province.Method]Using uniform design,two types of straw pellet feed were designed,which were straw pellet （A）,and straw pellet for TMR （B）.Effects of granulation water content,grinding particle of straw,the amount of added binder,cooling and drying time on particle density,modeling rate and moisture content were evaluated to establish regression equation.Partial-least square regression analysis was used to determine the optimal partial processing parameters of straw pellet feed.Result]When granulation moisture was 16.63％,the straw crushed particle size was 5 mm,the added binder was 0.58％ and dry-cooling time was 15.74 min,the combined effect of three indicators of straw pellets was the best.When granulation moisture content was 12.00％,crushed straw particle size was 2.0 mm,added binder was 1.92％,dry cooling time was 25.00 min and the combined effect of the three indicators of straw pellets for TMR was the best.Conclusion]In the same conditions,feed formulation,granularity,quenching and cooling and drying time had effects on indicators of straw pellet quality.Using uniform design and partial-least square regression modeling for the research can obtain a better representation of the results,and can reduce pilot workload and improve test efficiency.