摘要
以豆渣为原料,利用氢氧化钾与湿法磷酸联合处理制备粗微晶纤维素(Crude Microcrystalline Cellulose, CMCC)。以结晶指数为响应值利用单因素实验、Plackett-Burman实验、响应面优化实验对工艺条件进行优化。结果表明:碱解温度、酸解温度、酸解时间对CMCC的结晶指数影响较为显著,最适宜工艺条件为:碱解温度90℃、碱解时间60 min、氢氧化钾溶液浓度4%(质量分数)、湿法磷酸质量分数35%(P_(2)O_(5)的质量分数,下同)、酸解时间60 min、酸解温度95℃。验证试验表明最适宜条件下所得CMCC产品结晶指数为42.89%,仅比预测值低0.12%,表明利用响应面优化法得到的优化结果合理可行。
In this study,crude microcrystalline cellulose(CMCC)was prepared from soybean residue(okara)through combined treatment of potassium hydroxide and wet-process phosphoric acid.The crystallinity index was chosen as the response value and single factor experiments,Plackett-Burman experiments,and response surface methodology were used to optimize process conditions.The results suggested that alkali treatment temperature,acid treatment temperature,and acid treatment time had a significant impact on crystallinity index of CMCC and the optimal process conditions were identified as alkali treatment temperature 90℃,alkali treatment time 60 min,potassium hydroxide solution concentration 4%,wet-process phosphoric acid concentration 35%(P_(2)O_(5)),acid treatment time 60 min,and acid treatment temperature 95℃.The confirmatory experiment indicated the crystallinity index of CMCC was 42.89%under optimal conditions,which was only 0.12 percentage points lower than the predicted value,indicating that the optimization results obtained by the response surface optimization method are reasonable and feasible.
作者
徐露
孙崇庆
许德华
严正娟
李雪
钟本和
王辛龙
XU Lu;SUN Chongqing;XU Dehua;YAN Zhengjuan;LI Xue;ZHONG Benhe;WANG Xinlong(Ministry of Education Research Center for Comprehensive Utilization and Clean Processing Engineering of Phosphorus Resources,School of Chemical Engineering,Sichuan University,Chengdu 610065,China)
出处
《化学工业与工程》
北大核心
2025年第1期1-8,共8页
Chemical Industry and Engineering
基金
原位增值水溶肥及中微量元素肥料创制与产业化(2023YFD1700204)
陕煤-秦岭基础科学研究五年行动计划协同创新合作协议(22H1098)
四川大学磷资源高效利用工科特色团队(2020SCUNG113)
关键词
豆渣
粗微晶纤维素
结晶指数
Plackett-Burman实验
响应面优化实验
soybean residue
crude microcrystalline cellulose
crystallinity index
Plackett-Burman experiment
response surface optimization experiment
