Application of Deep Convolutional Neural Networks for Analysis of Apparent Density and Porosity of Iron Ore Pellets

• DOI: 10.22533/at.ed.3322316053

• Palavras-chave: -

• Keywords: Convolutional Neural Networks, Apparent Porosity, Computer Vision, Mask R-CNN, Artificial Intelligence

• Abstract:

  Abstract. The porosity and apparent density of iron ore pellets directly interfere with the blast furnace process and, therefore, need to be known to assist in its control and optimization. These characteristics are generally calculated using a pycnometer that uses mercury under pressure to fill the pores of the pellet. Considering the need to preserve the environment and the safety of operators, proposals were made to replace this process, but there are several complaints about the repeatability of results achieved, in addition to the time spent in preparing and executing these essays. At the same time, it is possible to observe a remarkable development in Computer Vision and Artificial Intelligence, mainly through Convolutional Neural Networks, which can extract patterns from a set of images and detect these same patterns in images subsequently exposed to this network. In addition to performing classification and detection, the Mask R-CNN network can perform pixel-by-pixel segmentation of objects in images. In its evaluation, the network presented a significantly high mAP and accuracy, demonstrating a satisfactory result for the segmentation and obtaining of porosity and apparent density values, with results similar to the essays currently used.