Expert System for Control Guidance of Grate-Kiln Pellet Production

Abstract

The characteristics of large-scale equipments, continuous process, close system and severe environment make grate-kiln process hard for parameter detection and difficult to control. The accuracy and normalization of grate-kiln process control need to be improved. Therefore, an expert system providing real time control guidance for grate-kiln pellet production is presented. The "roasting temperature of pellet in kiln & gas temperature in gas hood of preheating section" dominated control strategy is put forward and control rules for grate-kiln process are established based on expertise. A multistage reasoning strategy of "parameter status estimation -reason analysis -adjustment measure selection" is developed and forward reasoning is adopted. The application of software, which is programmed with Visual C++, is outlined. The results show that, with the assistance of expert system, stability rate of grate-kiln pellet production is increased from 91.0% to 94.2%, FeO content of finished pellet is 0.05% lower, compressive strength is 86 N.pellet -1 higher, first grade rate of pellet is increased by 2.54%.

The Deposit Formation Mechanism in Coal-Fired Rotary Kiln for Iron Ore Pellet Production: A Review

“…This system consists of five parts, respectively, data preprocessing, simulation model, knowledge base, inference engine and global database. The "Expert System" software is programmed and developed by Visual C++, which would realize stable production by controlling the temperature of the pellet roasting in the kiln [59,60].…”

An integrated optimal control model for the drying and preheating process in iron ore pellet manufacturing

“…Additionally, a multi-objective algorithm non-dominated sorting genetic algorithm II was employed to establish a parameter optimisation model for grate temperature, rotary kiln speed, and coal injection flow. Wang et al 16 proposed an all-encompassing control strategy for the moving grate-rotary kiln process, which focuses on stabilising heat supply and rationally adjusting heat distribution. The stability control of pelletising production was achieved using a static fuzzy model and time series correlation model, and the corresponding software was developed.…”