针对结晶器无氟保护渣黏度值预测复杂、预测精度低的问题，提出了一种基于改进鲸鱼优化算法的极限学习机模型并用于无氟保护渣黏度值预测。首先，构建无氟保护渣成分数据集，并对保护渣中成分与黏度值进行相关性分析；然后，利用改进Tent混沌映射和反向学习策略初始化鲸鱼优化算法的种群，融合非线性收敛因子和自适应t分布变异策略提高算法对极限学习机中超参数的寻优能力；最后，对无氟保护渣数据集进行黏度值预测对比实验，验证了改进算法的有效性。结果表明：与反向传播神经网络（back propagation neural network, BPNN）、极限学习机（extreme learning machine, ELM）模型相比，平均绝对百分比误差平均降低了29.50%，在寻优精度、预测精度和稳定性方面取得较大提升。

Aiming at the problems of complexity and low prediction accuracy of fluorine-free mold fluxes viscosity prediction in crystallizer, an extreme learning machine model based on improved whale optimization algorithm was proposed and used for fluorine-free mold fluxes viscosity prediction. Firstly, the fluorine-free mold fluxes composition data set was constructed, and the correlation analysis of the composition and viscosity value in the slag was carried out. Then, the population of whale optimization algorithm was initialized by using the improved Tent chaotic mapping and the inverse learning strategy, and the convergence factor of nonlinear convergence and adaptive t-distribution variation strategy were integrated to improve the algorithm's optimization ability of hyper-parameters in the extreme learning machine. Finally, the viscosity value prediction comparison experiments were conducted on the fluorine-free mold fluxes dataset to verify the effectiveness of the improved algorithm. The results indicate that compared to models such as BPNN (back propagation neural network) and ELM (extreme learning machine), the average absolute percentage error is reduced by 29.50% on average, and the optimization accuracy, prediction accuracy and stability are greatly improved.