摘要
传统曲房在大曲发酵过程中受曲房空间、堆曲并房的影响,不同位置之间存在较大的温湿度差,最大温差可达22.90℃,最大湿度差可达41.40%相对湿度(relative humidity,RH),严重影响大曲的质量和一致性。T-S模糊神经网络控制具有响应速度快、超调量小等特点,能有效解决温湿度调节延迟的问题,从而使曲房内各区域温湿度保持均匀。因此,该文提出了将T-S模糊神经网络控制与曲房控制系统相结合的方法来调控曲房温湿度。搭建曲房的硬件与软件系统,设计T-S模糊神经网络控制器,并利用粒子群算法优化该控制器的参数,对控制器进行仿真和运用控制系统进行实际测试。结果表明,该控制器工作稳定,频率响应快;控制系统能够对曲房的温湿度进行实时测控,使发酵过程中不同发酵部位之间的最大温度差降低至6.30℃,最大湿度差降低至8.69%RH。通过该控制系统实现了大曲发酵过程中曲房内部各区域温湿度保持均匀,保证了大曲质量的一致性。
The space of the Daqu workshop,the stacking and combining of the Daqu,as well as other factors all had an impact on the fermentation of Daqu in the traditional Daqu workshop.As a result,there was a significant temperature and humidity difference between Daqu in various positions,with a maximum temperature difference of 22.90℃and a maximum relative humidity difference of 41.40%.This had a significant impact on the quality and consistency of Daqu.T-S fuzzy neural network control,with its quick response speed and modest overshoot,could effectively solve the problem of temperature and humidity adjustment delay and ensure that each region of the Daqu workshop maintains uniform temperature and humidity.As a result,this research presented a method for controlling the temperature and humidity of the Daqu workshop by combining T-S fuzzy neural network control with the Daqu workshop control system.First,the hardware and software system of the Daqu workshop were built in this study.Second,the T-S fuzzy neural network controller was built,and the particle swarm algorithm was utilized to optimize the controller's parameters.Finally,this study simulated the controller and uses the control system for actual testing.The results demonstrated the stability and quick frequency response of the controller.The control system is able to monitor and adjust the temperature and humidity of the Daqu workshop in real time,resulting in a maximum temperature difference of 6.30℃and a maximum relative humidity difference of 8.69%between the various fermentation parts of the fermentation process.Through the use of this control system,the temperature and humidity of every space inside the Daqu workshop were kept constant while Daqu was fermenting,guaranteeing the consistency of Daqu quality.
作者
周书宇
田建平
高剑
韩李鹏
胡新军
黄丹
补友华
何立新
ZHOU Shuyu;TIAN Jianping;GAO Jian;HAN Lipeng;HU Xinjun;HUANG Dan;BU Youhua;HE Lixin(College of Mechanical Engineering,Sichuan University of Science&Engineering,Yibin 644002,China;College of Bioengineering,Sichuan University of Science&Engineering,Yibin 644002,China;Inner Mongolia First Machinery Group Co.Ltd.,Baotou 014030,China)
出处
《食品与发酵工业》
CAS
CSCD
北大核心
2023年第18期148-156,共9页
Food and Fermentation Industries
基金
四川省科技厅重点研发项目(2022YFS0552)
酿酒生物技术及应用四川省重点实验室开放课题(NJ2022-04)。
关键词
温湿度控制
曲房
T-S模糊神经网络
温湿度均匀性
一致性
temperature and humidity control
Daqu workshop
T-S fuzzy neural network
uniformity of temperature and humidity
consistency
