面向室内温湿度同时控制的直膨式空调系统混合建模
发布时间：2018-11-17 17:52
【摘要】：目前的家用空调通常只能控制室内温度,在低纬度夏季含湿量较大的地区,仅控制室内温度同时室内相对湿度过高会导致人体不适。理论上通过直膨式空调压缩机和蒸发器风机变频可以实现室内温湿度同时控制,空调系统中的传热传质耦合是实现这一目标的最大障碍。本文主要内容即建立合适的直膨式空调系统模型进行解耦,预测系统在不同工况下的制冷能力和除湿能力。本文对直膨式空调系统各部件建立了物理模型,组合起来的直膨式空调系统物理模型迭代次数过多,导致误差相对较大,响应时间长,不适合单独用于开发面向控制的算法,故本文进一步尝试应用经验建模的方法。另外本文对焓差法下的肋片效率公式进行了简化,得到了精度较高,运算量较小的经验公式。已经有研究者采用当下最常用的经验建模方法——人工神经网络模型对某一工况下直膨式空调系统Φs和Φl(显热冷量和潜热冷量)进行了预测,得到了不错的预测结果。但经验模型的缺点在于当工况改变时,根据原工况下实验数据建立的模型预测误差较大。因此本文对工况漂移时人工神经网络模型的预测能力进行了验证,Φs和Φl平均误差分别为1.3%和18.3%,预测结果误差较大,说明人工神经网络模型也不适合单独用于建立面向控制的直膨式空调模型。综上,本文尝试将物理模型和人工神经网络模型结合起来,综合物理模型能解耦物理过程和人工神经网络模型快速准确的优点。直膨式空调系统传热传质耦合发生在与室内空气直接接触的蒸发器上,因此本文对直膨式空调系统中的蒸发器建立了物理子模型,对压缩机、冷凝器和电子膨胀阀建立了一个人工神经网络子模型。经过细致分析后确定了将两个子模型结合起来的输入输出,并分别对两个子模型进行了验证,都得到了不错的预测结果。最后验证了混合模型在工况漂移时的预测精度,Φs和Φl平均误差分别为5.8%和2.8%,预测效果远好于同样条件下的人工神经网络模型,可见本文建立的面向控制的直膨式空调系统混合模型是有效的。在对包含耦合物理过程的系统进行建模时可以参考本文对直膨式空调系统建立混合模型的过程,对物理过程存在强耦合的部件建立物理模型,对系统的其他部件整体建立一个经验模型。
[Abstract]:At present, domestic air conditioning can only control indoor temperature. In areas with high summer humidity in low latitudes, only indoor temperature control and indoor relative humidity are too high will lead to human discomfort. Theoretically, the indoor temperature and humidity can be controlled simultaneously by the frequency conversion of the compressor and the evaporator fan, and the coupling of heat and mass transfer in the air conditioning system is the biggest obstacle to achieve this goal. The main content of this paper is to establish a suitable model of direct expansion air conditioning system to decouple and predict the refrigeration capacity and dehumidification capacity of the system under different working conditions. In this paper, the physical model of the components of the direct expansion air conditioning system is established. The combined physical model of the direct expansion air conditioning system has too many iterations, resulting in a relatively large error and a long response time, so it is not suitable for the development of control-oriented algorithms alone. Therefore, this paper further tries to apply the method of empirical modeling. In addition, in this paper, the efficiency formula of rib plate under enthalpy difference method is simplified, and the empirical formula with higher precision and less operation is obtained. Some researchers have used the most commonly used empirical modeling method, artificial neural network (Ann), to predict 桅 s and 桅 l (sensible heat cooling and latent heat cooling) of direct expansion air conditioning system under a certain working condition, and good prediction results have been obtained. But the disadvantage of the empirical model is that the prediction error of the model established according to the experimental data under the original working condition is large when the working condition is changed. Therefore, this paper verifies the prediction ability of the artificial neural network model when the working condition drifts. The average errors of 桅 s and 桅 l are 1.3% and 18.3%, respectively. It is suggested that the artificial neural network model is not suitable for the establishment of a control-oriented direct expansion air conditioning model either. In summary, this paper attempts to combine the physical model with the artificial neural network model, which combines the advantages of the physical model and the artificial neural network model to decouple the physical process and the artificial neural network model quickly and accurately. The heat and mass transfer coupling of direct-expansion air conditioning system occurs on the evaporator in direct contact with indoor air, so the physical sub-model of evaporator in direct-expansion air conditioning system is established in this paper. An artificial neural network submodel was established for condenser and electronic expansion valve. After careful analysis, the input and output of the two sub-models are determined, and the two sub-models are verified, and good prediction results are obtained. Finally, the prediction accuracy of the mixed model is verified. The average errors of 桅 s and 桅 l are 5.8% and 2.8% respectively, which is much better than the artificial neural network model under the same conditions. Therefore, the control-oriented hybrid model of direct expansion air-conditioning system is effective. When modeling the system with coupled physical processes, we can refer to the process of establishing a hybrid model for the direct expansion air conditioning system in this paper, and establish a physical model for the components with strong coupling in the physical process. Establish an empirical model for other parts of the system as a whole.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB657.2
本文编号：2338619
[Abstract]:At present, domestic air conditioning can only control indoor temperature. In areas with high summer humidity in low latitudes, only indoor temperature control and indoor relative humidity are too high will lead to human discomfort. Theoretically, the indoor temperature and humidity can be controlled simultaneously by the frequency conversion of the compressor and the evaporator fan, and the coupling of heat and mass transfer in the air conditioning system is the biggest obstacle to achieve this goal. The main content of this paper is to establish a suitable model of direct expansion air conditioning system to decouple and predict the refrigeration capacity and dehumidification capacity of the system under different working conditions. In this paper, the physical model of the components of the direct expansion air conditioning system is established. The combined physical model of the direct expansion air conditioning system has too many iterations, resulting in a relatively large error and a long response time, so it is not suitable for the development of control-oriented algorithms alone. Therefore, this paper further tries to apply the method of empirical modeling. In addition, in this paper, the efficiency formula of rib plate under enthalpy difference method is simplified, and the empirical formula with higher precision and less operation is obtained. Some researchers have used the most commonly used empirical modeling method, artificial neural network (Ann), to predict 桅 s and 桅 l (sensible heat cooling and latent heat cooling) of direct expansion air conditioning system under a certain working condition, and good prediction results have been obtained. But the disadvantage of the empirical model is that the prediction error of the model established according to the experimental data under the original working condition is large when the working condition is changed. Therefore, this paper verifies the prediction ability of the artificial neural network model when the working condition drifts. The average errors of 桅 s and 桅 l are 1.3% and 18.3%, respectively. It is suggested that the artificial neural network model is not suitable for the establishment of a control-oriented direct expansion air conditioning model either. In summary, this paper attempts to combine the physical model with the artificial neural network model, which combines the advantages of the physical model and the artificial neural network model to decouple the physical process and the artificial neural network model quickly and accurately. The heat and mass transfer coupling of direct-expansion air conditioning system occurs on the evaporator in direct contact with indoor air, so the physical sub-model of evaporator in direct-expansion air conditioning system is established in this paper. An artificial neural network submodel was established for condenser and electronic expansion valve. After careful analysis, the input and output of the two sub-models are determined, and the two sub-models are verified, and good prediction results are obtained. Finally, the prediction accuracy of the mixed model is verified. The average errors of 桅 s and 桅 l are 5.8% and 2.8% respectively, which is much better than the artificial neural network model under the same conditions. Therefore, the control-oriented hybrid model of direct expansion air-conditioning system is effective. When modeling the system with coupled physical processes, we can refer to the process of establishing a hybrid model for the direct expansion air conditioning system in this paper, and establish a physical model for the components with strong coupling in the physical process. Establish an empirical model for other parts of the system as a whole.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB657.2
【参考文献】
相关期刊论文 前2条
1 Minyoung Kim;Charles P.Gerba;Christopher Y.Choi;;Assessment of physically-based and data-driven models to predict microbial water quality in open channels[J];Journal of Environmental Sciences;2010年06期
2 ;STUDY ON THERMODYNAMIC MODEL OF A COMPRESSOR WITH ARTIFICIAL NEURAL NETWORKS[J];Chinese Journal of Mechanical Engineering(English Edition);1999年01期
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