Application of Improved Whale Algorithm to Optimize Dephosphorization Process Parameters in Converter Steelmaking

Regulating the process parameters in converter steelmaking is crucial for reducing the phosphorus content in molten steel and enhancing its quality. However, immoderate alteration may result in raised production costs and the occurrence of phosphorus return. This study addresses process parameter op...

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Veröffentlicht in:Applied sciences Jg. 15; H. 8; S. 4277
Hauptverfasser: Wu, Congrui, Kong, Yueping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.04.2025
Schlagworte:
Algorithms
Cetacea
Chemical reactions
converter steelmaking
endpoint phosphorus content
Manufacturing costs
Mathematical optimization
Metallurgy
Neural networks
Optimization algorithms
Phosphorus content
Process controls
process parameter optimization
Production costs
Raw materials
Steel industry
Steel production
whale optimization algorithm
ISSN:2076-3417, 2076-3417
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Zusammenfassung:Regulating the process parameters in converter steelmaking is crucial for reducing the phosphorus content in molten steel and enhancing its quality. However, immoderate alteration may result in raised production costs and the occurrence of phosphorus return. This study addresses process parameter optimization challenges in converter steelmaking by proposing an improved multi-objective whale optimization algorithm (IMOWOA) that synergistically integrates metallurgical thermodynamics with data-driven modeling. The methodology constructs a physics-informed objective function linking process parameters to optimization targets, thereby resolving the disconnect between mechanistic and data-driven modeling approaches. The algorithm innovatively combines Sobol quasi-random sequences with grey wolf social hierarchy strategies to prevent premature convergence in high-dimensional search spaces while maintaining Pareto front diversity, supplemented by a reward mechanism to ensure strict adherence to multi-objective constraints. Experimental validation using steel plant production data demonstrates IMOWOA’s efficacy, achieving a 10.8% reduction in endpoint phosphorus content and a 5.79% decrease in production costs per ton of steel. Comparative analyses further confirm its superior feasibility and stability in quality-cost co-optimization, evidenced by a 12.6% improvement in hypervolume (HV) over conventional swarm intelligence benchmarks, establishing a robust framework for industrial metallurgical process optimization.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15084277