温度和水分影响下的粮食真菌污染预测及优化控制

Prediction and Optimal Control of Fungal Contamination of Food Under Effects of Temperature and Moisture

构建以温度和水分为影响因素的真菌生长模型预测真菌生长趋势,并提出对粮食储藏过程温度和水分的优化控制方法。在现有基于温度的微生物生长动力学模型基础上,加入水分影响因素,并通过改进二维正态分布模型将菌落直径生长模型转换为真菌孢子数增长模型;通过对模型离散化、增量化实现对真菌生长趋势的预测;根据粮食真菌污染的特点,改进预测控制模型,建立以真菌生长为预测变量、以温度和水分为控制变量的优化控制目标函数,并采用不动点法迭代求解实现菌落生长速率和温度、水分控制成本的全局最小化。采用稻谷储藏实验数据验证,真菌生长模型拟合的确定系数达到0.7553,真菌生长速率降低到0.0263×10^(6)个/g·d。

In this paper,a fungal growth model with temperature and moisture was constructed as the influencing factors to predict the fungal growth trend and the optimal control method of temperature and moisture during grain storage were proposed.On the basis of the existing temperature-based microbial growth dynamics model,water influencing factors were added,and the transformation relationship model between colony diameter and fungal spore number was proposed by improving the two-dimensional normal distribution model.Then,the growth trend of fungi was predicted by discretizing and incrementing the model.On this basis,in accordance with the characteristics of grain fungus pollution,the predictive control theory was improved,and the optimal control objective function with fungus growth as the predictive variable,temperature and water as the control variables was established.Fixed point method was used to solve iteratively to minimize the objective function.The experimental data of rice storage were used to verify the proposed modeling method.The fitting coefficient of fungal growth model reached 0.7553,The growth rate of fungi decreased to 0.0263×10^(6) pieces/g·d.