Dynamic Modelling and Optimisation of Heat Exchange Networks for Enhanced Energy Efficiency in Industrial Processes
Keywords:
Heat exchanger networks, Dynamic modeling, Energy optimization, Genetic algorithms, Energy efficiencyAbstract
This study proposes a novel approach to improve energy efficiency in industrial heat exchange networks by integrating dynamic modeling and genetic algorithm-based optimization. The developed dynamic model depicts more realistic thermal dynamics in a heat exchanger system while optimization is performed to minimize energy consumption and heat loss. Simulation results show that the optimal design can reduce energy consumption by 23%-25% compared to traditional heat exchanger network designs. In addition, this approach also allows for a reduction in the number of heat exchange units required, which contributes to a decrease in investment and operational costs. This approach is applied to various industrial sectors such as chemical, oil and gas, and manufacturing, each showing significant energy-saving potential. Compared with previous studies, it shows that energy savings can be increased by 10%-15% higher than conventional optimization. In addition, this study also provides an efficiency improvement strategy for existing heat exchanger networks, with energy savings of up to 20%-25% through adjustment of operational configurations. These results demonstrate that the approach integrating dynamic modeling and optimization is not only efficient in the design of new networks but also effective in improving energy efficiency in systems that are already in operation. In conclusion, this research provides a practical and efficient solution to improve energy performance in industrial applications, with significant energy savings and reduced operational costs.