生物洁净室动态环境下多变量耦合的颗粒物浓度预测模型

Multivariable coupled prediction model for particulate matter concentration in the dynamic environment of a biologically clean room

本研究模拟制药生产车间搭建了一个C级环境的生物洁净室,通过空调系统变频调节,分别在10~40 h^(-1)换气次数的运行风量下,对静止、轻度、重度3种活动类型,人员数量为1~3人的颗粒物浓度进行36组测试.将测试值与标准限值对比,发现生物洁净室在40 h^(-1)换气次数下运行时,0.5μm颗粒物浓度标准限值与测试值之比最高达718,洁净度存在较高的冗余,空调系统节能潜力巨大.通过实验数据分析,总结人员数量、活动类型、换气次数与洁净室颗粒物浓度变化规律,给出换气次数为10~40 h^(-1)下人员数量增长与颗粒物浓度增长率比例关系,可在设计阶段提供较准确的颗粒物浓度参考值.根据实测数据,采用多元回归的方法,建立两种多变量耦合的颗粒物浓度预测模型,两种模型P值均小于0.001.对于0.5和5μm颗粒物,考虑变量交互作用的模型2的决定系数R2分别为0.95和0.85,未考虑变量交互作用的模型1的决定系数R2分别为0.73和0.76,模型2与实测值拟合度更高.可根据模型2预测值对洁净室换气次数进行动态调节,为生物洁净室空调系统节能运行策略的制定提供数据参考.

To ensure the cleanliness of a biologically clean room,the air change rate applied by an air conditioning system is usually high,resulting in high energy consumption during its operation.One of the important reasons is that the change law of particle concentration for a clean room is not clear,and the dynamic adjustment of the air volume cannot be performed in the air conditioning system according to the production load.In this study,a biological clean room with a C-class environment was constructed to simulate a pharmaceutical production plant.Under an operating air change rate of 10,20,30,and 40h^(-1),36 groups of particulate matter concentrations corresponding to stationary,mild,and severe activity types and 1-3 people were tested for the frequency conversion adjustment of the air conditioning system.A comparison of the test and standard limit values shows that when the biological clean room runs at 40h^(-1)ventilation times,the test value of the 0.5μm particulate concentration is 1/718 of the standard limit,indicating high redundancy in cleanliness.The air conditioning system has great energy-saving potential.The clean redundancy observed for 5μm particles is low when the air conditioning system of the biological clean room runs at low air volume.The ratio of 0.5 and 5μm particle concentrations to the standard limit should be used as a criterion for determining the air change rate of the air conditioning system.The analysis of the experimental data provided the number of personnel,activity types,air change rate,and the change rule of the clean room particulate matter concentration.The proportional relationship between the personnel growth and particulate matter concentration growth rates under an air change rate of 10−40h^(-1)was given,offering a more accurate reference value for particulate matter concentration in the design stage.Based on the concentration values of 0.5 and 5μm particles,the relationship between the concentration of particles in the light and heavy-activity types and that in the stationary activity type was summarized for a group of 1-3 people.Compared with previous studies,which suggested that the concentration of particles in the light and heavy-activity types was 2-5 and 5-10 multiple that in the stationary type,respectively,the data in this study are of more practical reference significance.Based on the measured data,a multiple regression method was adopted to establish two multivariable coupled prediction models for determining particulate matter concentration.Model 1 is a prediction model without any interactions between the variables,while Model 2 is a prediction model that considers the interaction between the variables.In terms of R2,Model 2 has a higher degree of fitting.Compared with Model 1,Model 2 is used to predict the concentration of particulate matter under different production conditions,which is closer to the real situation.Thus,the air change rate required to maintain the cleanliness of a biologically clean room can be obtained using this prediction model,providing data reference for developing an energy-saving operation strategy for the air conditioning system in such rooms.