130 results about "Atmospheric ozone" patented technology

The invention discloses a mixed refrigerant with GWP value being lower than 150. The mixed refrigerant comprises the following components in percentage by mass: 52%-90% of 2,3,3,3-tetrafluoropropene (HFO-1234yf), 5%-30% of Trans-1,3,3,3-tetrafluoropropene (HFO-1234ze(E)) and 5%-18% of F-methyl methyl (CF3OCH3 and HFE-143a). The mixed refrigerant does not damage atmospheric ozone layers, is low in greenhouse effect value, and excellent in environment-friendly performance; thermal performance of the mixed refrigerant is comparable with that of HFC-134a; drop-in substitution can be realized directly without change of any parts of an originally used HFC-134a system, the energy-efficiency ratio can be increased, the charging amount can be reduced, and the mixed refrigerant has the advantages of being low in substitution cost, resource-saving and energy-saving, and can be used as a long-term substitute of HFC-134a in an automobile air conditioner.

The invention discloses an environment-friendly refrigerant replacing CFC-12. The refrigerant contains three components of fluoroethane (HFC-161), 1,1-difluoroethane (HFC-152a) and 1,1,1,2-tetrafluoroethane (HFC-134a). Its mass percentage is: HFC-161: 1%-50%; HFC-152a: 1%-50%; HFC-134a: 30%-90%. The ODP value of this refrigerant is zero, and it does not destroy the atmospheric ozone layer. Compared with CFC-12 and its existing main substitute HFC-134a, the GWP value is smaller, and it is more in line with the requirements of environmental protection. The working pressure and pressure ratio of this refrigerant are similar to that of HFC-134a, the main substitute of CFC-12. Under the premise of basically not changing the main components of the system, it can be directly used in the system that originally used HFC-134a, and it can reduce the charge. Filling volume. This refrigerant can be used as a long-term substitute for CFC-12.

The invention relates to an atmospheric ozone concentration prediction method based on a hybrid CNN (convolutional neural network)-Transform model, and designs a hybrid CNN-Transform model, and the model is formed by combining a convolutional neural network and an improved Transform model. The convolutional neural network is composed of two one-dimensional convolutional layers, the Transform model is composed of encoders and decoders, each encoder is provided with three encoding layers, and the decoders are provided with three decoding layers. Meanwhile, on the basis of a traditional Transform encoder decoder architecture, a cross multi-head attention layer from encoder to encoder is added between different encoding layers of the encoder, and association of encoding information between the different encoding layers is further mined. The CNN model can well extract effective information on the feature dimension, and the problem that in a Transform model, the information extraction capacity of an encoder is insufficient is solved. According to the prediction method, the influence of multivariate data on the ozone concentration can be reflected more truly, and the influence mode is learned through the CNN-Transform model, so that a more accurate ozone concentration prediction result is given.