{'fraction_non_alphanumeric': 0.015397775876817793, 'fraction_numerical': 0.0, 'mean_word_length': 5.062176165803109, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Application of Support Vector Regression for Data-Driven Modeling of Melt Temperature and Carbon Content in LD Converter

Oxygen steel making process is a method of primary steelmaking in which carbon-rich molten pig iron is made into steel. Blowing oxygen through molten pig iron lowers the carbon content of the alloy and changes it into low-carbon steel. The temperature of melt inside the LD converter cannot be continually measured directly by thermocouples due to the destructive high temperature. Therefore, methods for estimating temperature using the model are developed. This paper presents a proposal for a non-contact soft-sensing based on the support vector regression (SVR). The proposed machine learning model can predict melt temperature and carbon content in the melt continually during steelmaking. The data-driven model is based on a large number of direct measurements at the start and end points of the melt. It was found that the polynomial kernel used in SVR gives more often better qualitative and quantitative prediction results. Achieved results have shown that this approach may be a potential alternative to traditional metallurgical models.