[Objectives] To address the issues of significant pipeline network losses in the steam heating system and the uneven steam parameters of heat users
a steam heating system based on recompression is proposed to reduce pipeline network losses and improve operational benefits.[Methods] By establishing a coupled simulation model of the steam heating system and the steam compressor
a comprehensive quantitative analysis is conducted on the thermal and economic performance of the system. The influence of key factors such as the degree of pressure reduction and temperature drop on the system performance is also explored.[Results] Compared with the conventional steam heating system
the maximum reduction in pipeline heat loss of the recompressed steam heating system is 10.59%
with an average reduction of 9.62%. The heat loss power of the steam pipeline network decreases by 110.22 kW. The return period of the new equipment for this system is 3.65 years. Under the condition of the 25-year service life of the units
the net present value of the system can reach 7.533 million yuan.[Conclusions] The system utilizes a steam compressor to perform steam recompression for local heat users. In doing so
it not only improves the steam parameters of heat users
but also effectively reduces the heating parameters of the main pipeline and the heat loss in the pipeline network
thereby enhancing the economic performance of the system. The proposed system provides a new approach for improving the comprehensive performance of the steam heating system.
Flow Channel Reconstruction and Thermal Energy Storage Performance Analysis of Wide-Temperature-Range Solid Particle Packed Beds
Multi-Agent Collaborative Optimization in New Power Systems Driven by Integration of Evolutionary Game and Federated Learning
Low-Carbon Performance Evaluation of Renewable Energy From a Life Cycle Perspective
Equivalent Modeling for Active Distribution Network Based on Hierarchical Double Deep Q Network
Distribution Networks Fault Recovery Strategy Fused With Convolutional Neural Networks-Bi-Directional Long Short-Term Memory-Attention Net Load Forecasting