Element yield rate prediction in ladle furnace based on improved GA-ANFIS

The traditional prediction methods of element yield rate can be divided into experience method and data-driven method. But in practice, the experience formulae are found to work only under some specific conditions, and the sample data that are used to establish data-driven models are always insufficient. Aiming at this problem, a combined method of genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) is proposed and applied to element yield rate prediction in ladle furnace (LF). In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples, smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method. For facilitating the combination of fuzzy rules, feature construction method based on GA is used to reduce input dimension, and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima. The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.