Optimization Method Based on Hybrid Surrogate Model for Pulse-Jet Cleaning Performance of Bag Filter

Shirong Sun, Libing Liu, Zeqing Yang (), Wei Cui, Chenghao Yang (), Yanrui Zhang and Yingshu Chen
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Shirong Sun: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
Libing Liu: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
Zeqing Yang: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
Wei Cui: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
Chenghao Yang: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
Yanrui Zhang: Experimental Training Center, Hebei University of Technology, Tianjin 300401, China
Yingshu Chen: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China

Energies, 2023, vol. 16, issue 12, 1-17

Abstract: The pulse-jet cleaning process is a critical part of the bag filter workflow. The dust-cleaning effect has a significant impact on the operating stability of bag filters. Aiming at the multi-parameter optimization problem involved in the pulse-jet cleaning process of bag filters, the construction method of hybrid surrogate models based on second-order polynomial response surface models (PRSMs), radial basis functions (RBFs), and Kriging sub-surrogate models is investigated. With four sub-surrogate model hybrid modes, the corresponding hybrid surrogate models, namely PR-HSM, PK-HSM, RK-HSM, and PRK-HSM, are constructed for the multi-parameter optimization involved in the pulse-jet cleaning process of bag filters, and their objective function is the average pressure on the inner side wall of the filter bag at 1 m from the bag bottom. The genetic algorithm is applied to search for the optimal parameter combination of the pulse-jet cleaning process. The results of simulation experiments and optimization calculations show that compared with the sub-surrogate model PRSM, the evaluation indices RMSE, R 2 , and RAAE of the hybrid surrogate model RK-HSM are 9.91%, 4.41%, and 15.60% better, respectively, which greatly enhances the reliability and practicability of the hybrid surrogate model. After using the RK-HSM, the optimized average pressure F on the inner side wall of the filter bag at 1 m from the bag bottom is −1205.1605 Pa, which is 1321.4543 Pa higher than the average pressure value under the initial parameter condition set by experience, and 58.4012 Pa to 515.2836 Pa higher than using the three sub-surrogate models, verifying its usefulness.

Keywords: hybrid surrogate model; performance optimization; Kriging; RBF; genetic algorithm; pulse-jet cleaning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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