基于分布参数模型的降膜式蒸发器性能智能仿真研究

To carry out high-precision design calculations and optimization analyses for a large horizontal-tube falling film evaporator, an intelligent simulation model for heat exchange of the falling film evaporator is established, and performance analysis research is conducted on the evaporator. Firstly, a simulation model for heat exchange of a single-outlet horizontal-tube falling film evaporator is constructed based on a distributed parameter model. Secondly, the Bayesian optimization algorithm and experimental data from the evaporator are used to identify the parameters of the model. This approach improves the prediction accuracy and adaptability of the simulation model for heat exchange. Finally, the changes in heat transfer coefficient and heat flux along the axis of the tube are explored, and the influence of the proportion of heat transfer area in the flooded zone on the heat transfer of the evaporator is examined. The research findings show that the intelligent simulation model, integrating the distributed parameter method and Bayesian optimization algorithm, achieves a +6% prediction error for heat transfer in the evaporator across 19 operating conditions. Within a wide range of heat exchange, the optimal proportion of heat transfer area in the flooded zone remains unchanged. Optimization from the perspectives of convective heat transfer inside the tube and secondary liquid distribution will effectively improve the performance of heat transfer of the falling film evaporator.