The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understanding the real-time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on Curvelet transform to establish theory model of twin Curvelet support vector machine. In order to improve the prediction precision of the twin Curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant.
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Zhao, B., Qin, D., Gao, D. et al. Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine. Soft Comput 25, 15391–15401 (2021). https://doi.org/10.1007/s00500-021-06151-z
- Energy saving diagnosis
- Petrochemical plant
- Twin Curvelet support vector machine