The final carbon content is the key factor in determining the quality of steel

and is one of the core variables to be controlled in the process of converter steel-making.Based on the Levy whale optimization algorithm (LWOA) and least squares support vector machine (LSSVM)

a comprehensive prediction model of carbon content at the end of the steel-making process is established.When the random selection of the parameters of the traditional whale optimization algorithm (WOA) is replaced with the Levy flight algorithm

the ability to jump out of the local optimum is optimized.Changing the method of coefficient vector convergence results in improvements to the generalization ability

prediction precision and convergence speed of the WOA.Data simulation results show that the proposed LWOA-LSSVM forecasting model not only overcomes the local optimization to obtain the global optimal solution

but also achieves faster convergence speed and higher prediction accuracy.Prediction results of the model

concerning root mean square error

mean absolute error

and mean absolute percentage error

show noticeable improvements when compared to those of the genetic algorithm and back propagation (BP) neural network

the genetic algorithm and LSSVM

and the traditional WOA and LSSVM.At the same time

through adjustments of the target hit ratio and the number of training sample entries

the prediction model is proven to be more robust than the aforementioned algorithms.