Version 1
: Received: 9 December 2021 / Approved: 10 December 2021 / Online: 10 December 2021 (15:04:59 CET)
How to cite:
Quan, L.; Li, A.; Cui, G.; Xie, S. Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF. Preprints2021, 2021120192. https://doi.org/10.20944/preprints202112.0192.v1
Quan, L.; Li, A.; Cui, G.; Xie, S. Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF. Preprints 2021, 2021120192. https://doi.org/10.20944/preprints202112.0192.v1
Quan, L.; Li, A.; Cui, G.; Xie, S. Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF. Preprints2021, 2021120192. https://doi.org/10.20944/preprints202112.0192.v1
APA Style
Quan, L., Li, A., Cui, G., & Xie, S. (2021). Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF. Preprints. https://doi.org/10.20944/preprints202112.0192.v1
Chicago/Turabian Style
Quan, L., Guimei Cui and Shaofeng Xie. 2021 "Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF" Preprints. https://doi.org/10.20944/preprints202112.0192.v1
Abstract
：An effective technology for predicting the end-point phosphorous content of basic oxygen furnace (BOF) can provide theoretical instruction to improve the quality of steel via controlling the hardness and toughness. Given the slightly inadequate prediction accuracy in the existing prediction model, a novel hybrid method was suggested to more accurately predict the end-point phosphorus content by integrating an enhanced sparrow search algorithm (ESSA) and a multi-strategy with a deep extreme learning machine (DELM) as ESSA-DELM in this study. To begin with, the input weights and hidden biases of DELM were randomly selected, resulting in that DELM inevitably had a set of non-optimal or unnecessary weights and biases. Therefore, the ESSA was used to optimize the DELM in this work. For the ESSA, the Trigonometric substitution mechanism and Cauchy mutation were introduced to avoid trapping in local optima and improve the global exploration capacity in SSA. Finally, to evaluate the prediction efficiency of ESSSA-DELM, the proposed model was tested on process data of the converter from the Baogang steel plant. The efficacy of ESSA-DELM was more superior to that of other DELM-based hybrid prediction models and conventional models. The result demonstrated that the hit rate of end-point phosphorus content within ±0.003%, ±0.002%, and ±0.001% was 91.67%, 83.33%, and 63.55%, respectively. The proposed ESSA-DELM model could possess better prediction accuracy compared with other models, which could guide field operations.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
