Patents by Inventor Philipp BERMES

Philipp BERMES has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Computer system and method providing operating instructions for thermal control of a blast furnace
    Patent number: 12535782
    Abstract: Computer system, computer-implemented method and computer program product are provided for training a reinforcement learning model to provide operating instructions for thermal control of a blast furnace, where a domain adaptation machine learning model generates a first domain invariant dataset from historical operating data obtained as multivariate time series and reflecting thermal states of respective blast furnaces of multiple domains, a transient model of a generic blast furnace process is used to generate artificial operating data as multivariate time series reflecting a thermal state of a generic blast furnace for a particular thermal control action, a generative deep learning network generates a second domain invariant dataset by transferring the features learned from the historical operating data 21 to the artificial operating data, where the reinforcement learning model determines a reward for the particular thermal control action in view of a given objective function by processing the combined fir
    Type: Grant
    Filed: September 28, 2021
    Date of Patent: January 27, 2026
    Assignee: PAUL WURTH S.A.
    Inventors: Cédric Schockaert, Fabrice Hansen, Lionel Hausemer, Maryam Baniasadi, Philipp Bermes
  • COMPUTER SYSTEM AND METHOD PROVIDING OPERATING INSTRUCTIONS FOR THERMAL CONTROL OF A BLAST FURNACE
    Publication number: 20230359155
    Abstract: Computer system, computer-implemented method and computer program product are provided for training a reinforcement learning model to provide operating instructions for thermal control of a blast furnace, where a domain adaptation machine learning model generates a first domain invariant dataset from historical operating data obtained as multivariate time series and reflecting thermal states of respective blast furnaces of multiple domains, a transient model of a generic blast furnace process is used to generate artificial operating data as multivariate time series reflecting a thermal state of a generic blast furnace for a particular thermal control action, a generative deep learning network generates a second domain invariant dataset by transferring the features learned from the historical operating data 21 to the artificial operating data, where the reinforcement learning model determines a reward for the particular thermal control action in view of a given objective function by processing the combined fir
    Type: Application
    Filed: September 28, 2021
    Publication date: November 9, 2023
    Inventors: Cédric SCHOCKAERT, Fabrice HANSEN, Lionel HAUSEMER, Maryam BANIASADI, Philipp BERMES