Optimization of Zinc Smelting Slag Melting Point Based on Catboost and Improved Snake Optimization Algorithm

• Published in: Applied sciences Vol. 14; no. 11; p. 4603
• Main Authors: Kong, Yueping, Liu, Ziyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Algorithms; Catboost; Costs; Efficiency; Energy consumption; Flue gas; Genetic algorithms; improved snake optimization algorithm; Industrial efficiency; Mathematical optimization; melting point optimization; Metallurgy; Methods; Optimization algorithms; Silver; Slag; Temperature; Zinc; zinc smelting slag; United Kingdom; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14114603

The regulation of the melting point of zinc smelting slag has an important impact on the subsequent smelting processes of the metal. In actual production, uncontrollable melting points may result in inconsistent product quality, which has a great negative impact on the smelter’s efficiency and environmental protection. However, the regulation mechanism of the melting point of the smelting slag is complex, with many influencing factors, and there is no recognized high-precision calculation method. In response to these challenges, this study introduces an innovative approach for optimizing the melting point of zinc smelting slag based on the improved Snake Optimization (ISO) algorithm. The melting point of zinc smelting slag is modeled using the Catboost algorithm, and the model parameters are optimized using the Tree-structured Parzen Estimator (TPE) to improve the accuracy of the model. Next, the ISO algorithm is employed to conduct optimization calculations, determining the optimal values of various production process parameters that minimize the melting point. The effectiveness of this approach was evaluated using diverse modeling algorithms and test functions, subsequently applied to optimize and validate actual production data from a smelter in Shaanxi, China. Statistical analyses reveal that the TPE-optimized Catboost model exhibits an R2 of 93.89%, an RMSE of 7.02 °C, an MAE of 6.19 °C, and an MRE of 7.88%, surpassing performance metrics of alternative algorithms. Regarding optimization efficacy, the proposed ISO algorithm achieves an average reduction of 65 °C in the melting point and demonstrates superior robustness compared to both actual production data and alternative algorithms.