茯苓丁真空脉动干燥特性及多目标优化
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(1. 北京工商大学人工智能学院,北京 100048;2. 中国农业大学工学院,北京 100083;3. 中国农业大学中国农业机械化发展研究中心,北京 100083; 4. 河北经贸大学生物科学与工程学院,河北 石家庄 050061)

作者简介:

张卫鹏,男,北京工商大学讲师,博士。

通讯作者:

郑志安(1965—),男,中国农业大学副教授,博士。E-mail: zhengza@cau.edu.cn

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基金项目:

国家自然基金(编号:31772026);现代农业产业技术体系建设专项资金资助(编号:CARS-21);北京市自然科学基金(编号:6204035);北京市优秀人才培养资助(青年骨干个人)项目(编号:2018000020124G034);河北省高等学校科学研究项目(编号:QN2021054)


Pulsed vacuum drying characteristics and multi-objective optimization of Poria cubes
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(1. College of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; 2. College of Engineering, China Agricultural University, Beijing 100083, China; 3. China Research Center for Agricultural Mechanization Development, China Agricultural University, Beijing 100083, China; 4. College of Bioscience and Engineering, Hebei University of Economics and Business, Shijiazhuang, Hebei 050061, China)

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    摘要:

    目的:探索真空脉动干燥过程中,干燥温度、真空时间、常压时间及其交互作用对茯苓丁干燥时间、单位能耗、多糖含量、破碎率的影响,以期得到较佳工艺参数,提高茯苓丁干燥效率和品质。方法:选取干燥温度(55~95 ℃)、真空时间(0~20 min)、常压时间(0~8 min)为自变量,设计中心复合响应面试验,分析影响规律;建立干燥时间、单位能耗、多糖含量、破碎率的二次回归模型;构建适应度函数,分别用遗传算法、隶属度法进行多目标优化,通过比较2种优化方法的结果,得到最佳工艺参数并进行实验验证。结果:干燥条件均可显著影响干燥时间、单位能耗、多糖含量、破碎率;建立的干燥时间、单位能耗、多糖含量、破碎率回归模型具有统计学意义(P<0.001),可用于对茯苓丁干燥评价指标的分析和预测;以适应度为评价指标,得出遗传算法的优化结果较优;遗传算法优化工艺为干燥温度80.88 ℃、真空时间7.68 min、常压时间5.04 min,该工艺条件下的干燥时间为443.3 min、单位能耗为4.43 kJ·h/kg、多糖含量为3.27 mg/g、破碎率为7.42%,节能增效作用显著(P<0.05)。结论:真空时间和常压时间的合理配置可显著缩短干燥时间,并降低单位能耗和破碎率;茯苓丁内部温度随干燥室压力波动变化,适宜干燥温度可提高茯苓多糖含量;优化后的真空脉动干燥工艺,具有干燥时间短、品质好、破碎率低的优点。

    Abstract:

    Objective: In order to explore the effects of drying temperature, vacuum duration, atmospheric duration and their interactions on drying time, specific energy consumption, polysaccharide content and broken ratio of Poria cubes in pulsed vacuum drying process, so as to obtain better drying parameters and improve the drying efficiency and quality of Poria cocos cubes. Methods: the drying temperature (55~95 ℃), vacuum duration (0~20 min) and atmospheric duration (0~8 min) were selected as independent variables, and central composite design of response surface method was used to analyze the drying process; The quadratic regression model of drying time, specific energy consumption, polysaccharide content and broken ratio was established; The fitness function was constructed, and the genetic algorithm and membership degree method were used to optimize the multi-objective process. By comparing the results of the two optimization methods, the optimal drying parameters were obtained and verified. Results: Drying time, specific energy consumption, polysaccharide content and broken ratio were significantly affected by drying conditions; The regression model of Drying time, specific energy consumption, polysaccharide content and broken ratio was statistically significant (P<0.001), which could be used to analyze and predict the drying process of poria cocos; Results proved that the genetic algorithm was better, the optimum drying conditions were as follows: drying temperature 80.88 ℃, vacuum duration 7.68 min, atmospheric duration 5.04 min. The drying time, specific energy consumption, polysaccharide content and broken ratio were 443.3 min, 4.43 kJ·h/kg, 3.27 mg/g, 7.42%, respectively. The optimum drying conditions can significant(P<0.05) save energy and improve the drying effeciency. Conclusion: Reasonable configuration of vacuum duration, atmospheric duration can significantly shorten the drying time, and specific energy consumption, and broken ratio; The internal temperature of poria cubes fluctuates with the pressure change of drying chamber, and the appropriate drying temperature can increase polysaccharide content; The optimized pulsed vacuum drying process has the advantages of shorter drying time, better quality and lower broken ratio, which can provide theoretical basis and technical support for the industrial processing of poria cubes.

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引用本文

张卫鹏,陈浩然,范晓志,等.茯苓丁真空脉动干燥特性及多目标优化[J].食品与机械,2021,(9):91-98.
ZHANGWeipeng, CHENHaoran, FANXiaozhi, et al. Pulsed vacuum drying characteristics and multi-objective optimization of Poria cubes[J]. Food & Machinery,2021,(9):91-98.

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  • 收稿日期:2021-03-27
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  • 在线发布日期: 2023-02-15
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