###### Abstract
Additionally, the establishment of ternary phase diagrams is based on the assumption of equilibrium under steady-state conditions, where the internal components reach a stable state within the entire system. Yet, in practical scenarios, the composition and temperature may change, rendering phase diagrams unable to fully depict dynamic processes and thus having limitations in practical use. With the demonstrated effectiveness of machine learning and intelligent algorithms in handling nonlinear modeling and optimization problems, researchers have made significant strides in various industrial applications. [PERSON] et al. [14] utilized a Generalized Regression Neural Network to model and predict melting points, coupled with genetic algorithms to determine optimal elemental compositions. This approach significantly improved work efficiency and reduced melting points compared to traditional phase diagram methods. [PERSON] et al. [17] introduced the Grey Wolf Optimization algorithm into industrial settings to optimize parameters and instructions in production processes. notably enhanced production efficiency and solution accuracy. [PERSON] et al. [18] established welding effect prediction models in magnetic pulse welding processes and employed the Imperialist Competitive Algorithm to optimize process parameters, leading to a substantial enhancement in welding effects. These algorithms each possess strengths and weaknesses, primarily attributed to their different evolutionary mechanisms. Genetic algorithms offer simple optimization methods with good performance but suffer from serious premature convergence issues. Consequently, researchers have introduced many improved variants of genetic algorithms [19; 20]. The Grey Wolf Algorithm is a widely used efficient heuristic algorithm in engineering applications, but its performance depends on its control parameters. Different problems require different parameter adjustments, which can be time-consuming and make optimization results unstable [21; 22]. In recent years, The Snake Optimization (SO) algorithm has shown considerable potential in engineering applications [23], emulating behaviors observed in snakes, including predation, reproduction, and evasion. This biomimetic trait renders the algorithm more nature-centric, facilitating effective exploration of solution spaces. However, SO predominantly depends on positional data from superior and inferior individuals in the snake population to update locations, resulting in decreased utilization rates of other individuals and generating algorithmic instability, suggesting potential areas for enhancement. Based on the above analysis, to address the problems of instability, slow convergence speed, complexity of melting point influencing factors, and insufficient modeling accuracy of traditional methods of the existing optimization methods in the field, this study firstly analyzes the relationship between relevant slag compositions and melting point. Subsequently, the prediction model of the optimization algorithm was established. Then, the improved serpentine optimization algorithm was used for optimization. The optimal process parameters obtained from the optimization were confirmed through validation experiments. The main contributions and highlights of this study are as follows. First, by utilizing historical production data, we employed a Tree-structured Parzen Estimator (TPE) hyperparameter-optimized CatBoost model for predictive modeling. This approach not only enhances prediction accuracy but also significantly reduces the operational cost and time consumption compared to traditional methods. Second, the performance of the Improved Snake Optimization (ISO) algorithm was bolstered through the integration of Halton sequence initialization, elite reverse learning, and dynamic updating mechanisms of Cauchy-Gaussian mutation. These enhancements improved the algorithm's capabilities in both global exploration and local exploitation stages.