[12] employed a support vector machine (SVM) to develop a model correlating zircon brick density with the raw material ratio. Subsequently, they utilized an adaptive particle swarm algorithm to refine the model and derive the best possible raw material ratio. As for raw coal blending, [PERSON] et al. [13] introduced a method based on interval programming. By utilizing interval numbers to depict the variability ranges of ash and sulfur contents across different density levels of raw coal, they developed a prediction model for the quantity and quality of dense medium cyclone separator products and established a mixed-integer nonlinear programming model. For blast furnaces, the prevailing approach involves modeling blast furnace ironmaking based on the principles of ironmaking and material balance. [PERSON] and [PERSON] [14] enhanced the genetic algorithm and penalty function via leveraging the principles of blast furnace ironmaking and material balance and developed an intelligent system for the burden structure across the entire sintering and blast furnace ironmaking process. [PERSON] et al. [15] proposed a two-stage decision-making method for burden distribution parameters based on recognizing the conditions. [PERSON] et al. [16] employed the GA-SVR method to identify the five most informative features for fuel ratio adjustment during blast furnace ironmaking, enabling operators to proactively adjust input parameters. [PERSON] et al. [17] introduced an automatic sintering ore-blending model to quickly configure the sintering of raw materials according to the requirements of the production line. [PERSON] et al. [18] developed A GA-RNN quality prediction model with raw material composition as input parameters and physical and metallurgical properties of sinter as output. [PERSON] et al. [19] proposed an intelligent integrated batching system for the field of ore batching. By understanding the chemical and physical properties of batching, the require ments for batching were determined. Firstly, a proportioning scheme was established, and then a cascaded integrated quality prediction model was designed. The model used the prediction indicators as feedback for the proportioning scheme, thereby determining the satisfactory proportioning scheme. However, the results of these studies still require human involvement and cannot be updated online, or they have not been truly implemented. As a new industry, the raw material composition for rotary hearth furnaces is complex and frequently changes, so the batching process is more complex. As far as we know, a rotary hearth furnace intelligent batching system is rarely used in the world. In this study, an intelligent batching system with self-learning ability for rotary hearth furnaces is proposed, which consists of four parts: data preprocessing, AP offline formula learning, AP online formula updating, and expert experience-based formula fine adjustment:  
* In the first part, outliers of the ingredient composition in historical ratio data are found via the local factor algorithm [20], and measurement noise is filtered by Kalman filtering [21]; the one-hot method [22] encodes the types of raw materials to form data sets with the contents of raw material ingredients and ratio of raw materials. * In the second part, AP clustering analysis is used to obtain many clustering groups from the above data sets and generate a machine learning formula library. Each cluster group represents a type of raw material's composition characteristics, which corresponds to a type of ratio. Through unsupervised machine learning of historical data, valuable human formula experience is mined. * In the third part, the incremental AP clustering algorithm is used to update clustering groups during the continuous operation of the system and consequently to update the machine learning formula library.