A non-invasive learning-based method for pipeline overhaul on fertilizer production plants.

Fertilizers are fundamental compounds to balance nutrients in the soil, ensuring its fertility for food production. In the industry of fertilizers, a common task is the overhaul of the pipelines that convey the material through production lines, which need to be performed periodically, to avoid duct blockages. Traditionally, this task is carried out manually, which requires interruption of production. Therefore, it implies time consumption and waste of money, in the case of unnecessary inspection. To avoid needless production stoppage, in this paper is presented a non-invasive overhaul method for sediment detection in the pipelines of fertilizer production lines based in neural networks. The proposed model uses thermal images to estimate the volume of sediments into pipelines. Furthermore, as it is difficult to obtain images of several pipeline blockage conditions, a methodology for artificial dataset creation is also provided. The results indicate the feasibility of the proposed methodology.