IoT System for Pellet Proportioning Based on BAS Intelligent Recommendation Model

In the future, pellet is gradually replacing sinter as the main charge structure of blast furnace ironmaking. In the process of pellet production, the linear regulate and control method is often used in the operation of proportioning scheme, roasting regime, and parameter setting. There are some problems such as low pelletizing rate and poor compressive strength. It is urgent to realize the intelligent pellet manufacturing. In order to realize an intelligent pellet material matching system, the general regression neural network is applied to construct the prediction model of mature pellets compressive strength under the technical framework of industrial Internet of Things (IoT), using beetle antennae search (BAS) algorithm to construct an intelligent recommendation model of pellets with optimal proportioning. The prediction model and the intelligent recommendation model are coupled within the technical framework of IoT. An IoT system of pellet material proportion is implemented, which is dominated by the compressive strength of mature pellet. The system can realize automatic detection of raw materials, automatic quantification processing of index data, automatic start of prediction model, visualization of proportioning results, and x201C;one-clickx201D; operation of proportioning scheme. Recommended system simulation and experimental results show that the prediction model of the compressive strength of cooked balls has the strong interpolation ability and the excellent generalization performance; in the range where the changes of various pellet proportioning are no more than 20x0025;, the intelligent recommended best proportioning scheme can increase the compressive strength of cooked pellets by over 16x0025; on average, and the system runs stably and the simulation results are effective.

Automated summary

In the future, pellets are gradually replacing sinter as the main charge for blast furnace ironmaking. Issues such as low pelletizing rate and poor compressive strength need to be addressed with the urgent need for intelligent pellet manufacturing. The use of intelligent technologies can improve the quality and production efficiency of pellets by enabling precise control over blending and roasting processes.