## 1 Introduction  
China is advocating coal washing and quality improvement processing as well as promoting coal energy low-carbon development. Coal slime is a byproduct of the coal washing process and is characterized by fine particle size, high water holding capacity, high viscosity, high ash content, and low heat generation [1]. If the coal slime is not correctly disposed of, it will harm the environment. The harmful substances contained in coal slime can seep into the ground and cause soil contamination. If coal slime is discharged into rivers and lakes, it will cause severe pollution to water quality. Circulating fluidized bed combustion (CFB) technology, one of the clean coal technologies, has strong adaptability to medium-quality and low coal fuel, with combustion efficiency reaching 95-99% [2]. Therefore, CFB coal slime blending is recognized as the best way to handle coal slime. However, coal slime blending affects the stability of CFB units significantly. Ideally, the water content of coal slime is constant in each pump of the coal slime pump. In actual operation, the coal slime falls and splashes water on the level meter, causing the alarm. The alarm causes the discharge screw to stop working, emptying the pump. As a result, the uneven mixture of coal slime and water content significantly fluctuates in boiler input energy. The bed pressure of the coal slime blending unit shows a wide range of fluctuations due to the unevenness of the coal feed in the furnace. In the pant-leg CFB units, the unstable bed pressure will lead to the inventory overturn accident, threatening the stable operation of the unit. Accurate prediction models improve the safety of unit operation. By obtaining future information through forecasting, operators can make timely adjustments to their operations, thereby reducing accidents. Therefore, it is necessary to monitor and predict bed pressure. Most studies in bed pressure only focus on mechanism research. The experimental relationship between the lateral pressure difference of the upper furnace and the lateral material exchange flow rate was established by [PERSON] [3]. [PERSON] also found the critical role of the lateral pressure difference of the upper furnace in material fluctuation, which was also verified by [PERSON] [4]. Many researchers focused on the solid flow model in the furnace to study bed pressure [5]. [PERSON] [6] developed a computational particle fluid dynamics model to simulate solid exchange behavior between two half beds in the bench-scale two-dimensional dual-leg fluidized bed. [PERSON] [7] proposed an empirical correlation of the non-uniformity index to quantify the non-uniformity of gas-solid two-phase flow. The computational particle fluid dynamics model of a pilot-scale CFB was used to numerically simulate its gas-solid flow characteristics by [PERSON] [8]. [PERSON] [9] established a CFB axial pressure distribution prediction model based on the particle method to predict bed pressure. Current research also shows that the airflow rate in the furnace has a significant influence on the bed pressure [10]. However, the structure of these mechanism models is complex, and it is difficult to obtain some of the data in the mechanism models. In addition, the empty pump phenomenon makes the measured value of feed quantity in the unit deviate from the actual value. Therefore, the mechanism models make it challenging to achieve accurate bed pressure prediction in coal slime blending CFB units. Driven by the development of intelligent algorithms, data-driven modeling technology for complex industrial processes is attracting the attention of researchers [11; 12]. The data-driven models are applied and migrated without considering the design parameters of units. Such models only need to use operational data to adjust and train the parameters of the model. The error backpropagation neural network (BPNN) [13], least squares support vector machine (LSSVM) [14], and other algorithms are commonly used in the industrial process [15; 16].