Predictive study of drying process for limonite pellets using MLP artificial neural network model

Abstract

Due to the decline in high-grade iron ore production, the utilization of low-grade iron ore, such as limonite, has become necessary. Limonite contains a significant amount of bound water, which requires a drying process prior to use. Excessive heat stress caused by the evaporation of bound and free water during the drying of limonite pellets can lead to pellet disintegration and adversely affect gas-solid reactions. In recent years, artificial neural network (ANN) has been developing continuously in the fields of modeling and intelligent control, and has been widely used. Many predecessors used artificial neural network model to study the drying process of natural organic matter, and analyzed the factors affecting the drying rate of organic matter. In this study, we employed big data analysis, specifically Multilayer Perceptron (MLP) artificial neural networks, to analyze the drying process of limonite pellets and successfully established a predictive drying model applicable to limonite pellets. The MLP artificial neural network demonstrated excellent fitting between predicted and experimental values, with a maxi-mum R2 value of 0.999. The artificial neural network for drying developed in this study provides technical guidance for industrial material drying, reduces the workload of manual measurements, and minimizes energy consumption.