Collaborative Optimization of Ladle Furnace Operating Parameters Using Prediction Models and Case-Guided Genetic–Tabu Search

Intelligent control of the ladle furnace (LF) process and its endpoint is essential for product quality and stable continuous casting. Existing studies mainly address endpoint prediction or operating-parameter recommendation. Prediction models rarely provide multivariable operating schemes directly, whereas recommendation models often suffer from insufficient coordination among modules and complex commissioning. This study proposes a collaborative LF operating-parameter optimization method combining endpoint prediction with case-guided genetic–tabu search. Given the initial heat state and target endpoint temperature, the method treats electric energy input, power-on duration, and key material additions as decision variables, evaluates each candidate using temperature and composition prediction models, and coordinates the variables through a unified objective. A dynamic weighting mechanism coupling generational annealing with feasible-population temperature-error feedback balances endpoint quality against resource input. Case-based reasoning guides population initialization, while a real-coded genetic algorithm and tabu search strengthen global exploration and local exploitation. For 400 independent historical heats, the method obtained a recommendation satisfying all model constraints for every heat. Relative to the corresponding historical operations, the mean recommended quantities of lime, slag agent, aluminum granules, high-carbon ferromanganese, electric energy input, and power-on duration were reduced by 6.98%, 12.79%, 9.34%, 8.85%, 7.89%, and 8.95%, respectively. Case-guided initialization improved first-generation solution quality and early convergence, whereas tabu search enhanced mid-to-late local refinement. The method converts existing endpoint-prediction capability into coordinated multivariable operating recommendations.