Use of an Artificial Neural Network in determination of iron ore pellet bed permeability

Abstract The thermal processing of iron ore pellets in pelletizing plants is a decisive stage regarding final product quality and knowledge of its characteristics has a fundamental importance in its process optimization. This study evaluated the variable sensitivity involved in pellet bed formations and their permeability using the artificial neural networks method. The model stated that standard diameter deviation, sphericity and pellet bed height mostly affect bed permeability. The computational model was able to predict pellet bed backpressure by means of pellet geometrical features, thus allowing improving green pellet generation, in order to ensure fuel and energy consumption reduction, final quality improvement and better productivity.摘要 球团厂中铁矿球团的热处理工序是决定最终产品质量的关键环节，掌握该工序的特性对工艺优化具有根本性意义。本研究采用人工神经网络（Artificial Neural Networks）方法，评估了球团料层形成及其透气性相关的变量敏感性。模型结果表明，直径标准偏差、球度以及球团料层高度是影响料层透气性的主要因素。该计算模型可通过球团的几何特性预测球团料层背压，从而助力优化生球制备工序，实现燃料与能源消耗降低、最终产品质量提升以及生产效率改善。