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Operation optimization for a CHP system using an integrated approach of ANN and simulation database
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AI
DOI：10.1016/j.applthermaleng.2025.125771.png)
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Abstract
En 中文
Combined heat and power (CHP) systems are thermodynamically and economically viable options for satisfying the rapid increase in the heating demands of society. To improve the flexibility and reduce heat consumption of CHP systems is significant to broaden their applications. In this paper, an integrated approach using an artificial neural network and a simulation database is proposed to achieve operating optimization for a CHP system with high back-pressure and steam extraction structures. Simulations were conducted to search for key operational variables under part-load conditions. The results showed that heat and electrical load dispatch has the potential to enhance the performance of the CHP system using the chaos swarm optimization algorithm. The relative error of the optimal total heat consumption was less than 0.2 % indicating that the wild point, which elucidates the artificial neural network model is reliable for predicting the performance of the CHP system. Moreover, both the high back-pressure and extraction heating units have the potential to respond to high heat and electrical loads with a satisfactory total heat consumption of 2886.70 MW. Generally, the CHP system exhibited better part-load performance when employing the integrated approach of an artificial neural network and a simulation database for operation optimization.
Keywords:
Combined heat and power system
Part-load performance
Operation optimization
Artificial neural network
Simulation database
Journal
IF:
6.9
Papers:
2.7W
Citations:
10.6W