Data mining, and in particular topological data analysis (TDA), had proven to be successful inabstracting insights from big arrays of data. This thesis utilizes the TDA software AyasdiTM inorder to improve the accuracy of the energy model of an Electric Arc Furnace (EAF), pinpointingthe causes of a wrong calculation of the steel temperature. Almost 50% of the charges analyzedpresented an underestimation of temperature, while under 30% an overestimation.First a dataset was created by filtering the data obtained by the company. After an initialscreening, around 700 charges built the dataset, each one characterized by 104 parameters. Thedataset was subsequently used to create a topological network through the TDA software. Bycomparing the distribution of each parameter with the distribution of the wrong temperatureestimation, it was possible to identify which parameters provided a biased trend. In particular, itwas found that an overestimation of temperature was caused by an underestimation of themelting energy of materials not having through a melting test. It was also found a possible biasedtrend in some distribution of parameters like %O in steel and slag weight, which it is believedare all connected together. Despite not finding a global solution for the reasons behind theunderestimation of temperature, it is believed that a different settings more focused around thematerials used as scrap mix can highlight more on that subject. In conclusion TDA proved itselfefficient as a problem solving technique in the steel industry.