# Predicting Ceramic Wool Diameter by Motor Frequency Using Improved BP Neural Network
## 1 Introduction  
Energy was what was keeping our head above water and allowing us to continue to grow. However, the over-consumption of energy and intensive exploitation of natural resources had raised the global concern [1; 2; 3]. Therefore, any effective power-saving technology that assisted efforts to reduce energy consumption was an advisable trend to be followed. In China, slag and mineral wool products had a glorious prospect as the government and communities increasingly had their eye on the fire safety of high-rise buildings [4; 5]. Ceramic wool, a kind of classification of mineral wool, is an inorganic insulation material and was primarily designed for advanced fire prevention applications [6; 7]. Undoubtedly, ceramic wool felt with low moisture absorption, light weight, low thermal conductivity, and non-combustible property was expected to provide a new solution in the fields of industrial, automotive, aerospace, and medical engineering applications under extreme environmental conditions [8; 9; 10; 11]. The common production process of ceramic wool mainly includes two steps: melting and fiber forming. In this method, an ideal highly viscous fluid is formed in the blast furnace with the temperature range of 1823 K to 1973 K and then falls on a spinning wheel with a rotating speed nearly 8000-1000 rpm. Due to the centrifugal force, the fluid is dragged into filaments. The viscosity increases rapidly, and the instability diminishes notably as the string increases in length. A higher temperature blast furnace was invented to produce ceramic wool directly which not only reduced energy consumption but also resulted in a better use of waste energy [12; 13; 14]. Clearly, the ceramic wool felt was prepared by a random distribution of ceramic wool. With the gradual implementation of China's 2025 plan, finer primary wool diameter which dramatically affected the product performance was required for higher quality products with non-combustible property and low thermal conductivity. Many researchers have attached importance to the production process. [PERSON] et al. developed a novel kinematic model for molten slag fiberization, especially on the diameterand length of slag fiber to predict slag fiber properties [15]. Under given process parameters and relevant hypotheses, the prediction model was established which indicated that the slag viscosity and the rotational speed of spinning wheel had a great effect on the fiber dimensions systematically. [PERSON] et al. presented a computer-aided visualization method to study the distribution of mineral wool fiber and a numerical model was used to confirm the relationship of the local aerodynamic characteristic of the airflow and mineral wool thickness [16]. [PERSON] et al. focused on the molten slag fiber generation stochastic modeling and studied the fiber flight trajectory differences under different operating conditions [17]. The strong industrial potential of molten slag fiberization was proved. [PERSON] and [PERSON] devoted all energies to the Coanda effect on the trajectory of fine fibers in production which was discussed under different standoff distances by means of numerical simulation [18]. [PERSON] presented a regression model in the mineral wool production processes which was provided for automated control [19]. Although fibers with different physical characteristics could be empirically produced, the specific process of the melt fiberization remained unclear. The speed of the spinning rotating wheel which was difficult to indicate played a decisive role in the fiber diameters. However, there was no precise theory to explain or predict the mechanical process of the filament formation. Moreover, the ceramic wool diameter could not be effectively obtained directly either which resulted in mismatch risks. The motor frequency of the spinning wheel was always detected to show the rotating speed in the real production.