A two-phase flashing flow model is developed to predict the distributions of pressure, temperature, velocity and evaporation rate in a transfer line, which is a typical example of a two-phase flow pipe in the petrochemical industry. The model is proposed based on the pressure drop model and the multi-stage flash model. The results indicate that pressure drop, temperature drop, and change of evaporation rate mainly occur in the transition section and the junction site of the transfer line. The predictions of the model have been tested with reliable field data and the good agreement obtained may lead to a better understanding of the twophase flashing flow phenomenon, as well as demonstrating the feasibility of applying the model into the design and optimization of pipelines.
In order to solve the problem of the traditional gray prediction model (GM) during determination of the accuracy of buildings’ energy savings and its poor fitting of data, the idea of a fractional model based on the traditional first-order one-variable GM(1,1) model is applied. We use the GM–backpropagation (GM-BP) neural network to solve the optimal fractional order and establish a fractional GM(1,1) model based on the GM-BP neural network. Example calculation shows that the fractional GM(1,1) model can improve the prediction accuracy of buildings’ energy savings, and selecting the optimal order can further improve the prediction accuracy and decrease the error level when using the GM-BP neural network. This work shows that the fractional GM(1,1) model based on the GM-BP neural network has an important guiding role in the energy savings of buildings.
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