A machine-learning reduced kinetic model for H2S thermal conversion process

Dell'Angelo A., ANDOĞLU E. M., Kaytakoglu S., Manenti F.

Chemical Product and Process Modeling, vol.18, no.1, pp.117-133, 2023 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.1515/cppm-2021-0044
  • Journal Name: Chemical Product and Process Modeling
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Compendex, Food Science & Technology Abstracts, INSPEC
  • Page Numbers: pp.117-133
  • Keywords: Claus process, H2S to H2, H2S to syngas, hydrogen sulfide, kinetic model
  • Bilecik Şeyh Edebali University Affiliated: Yes

Abstract

© 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.H2S is becoming more and more appealing as a source for hydrogen and syngas generation. Its hydrogen production potential is studied by several research groups by means of catalytic and thermal conversions. While the characterization of catalytic processes is strictly dependent on the catalyst adopted and difficult to be generalized, the characterization of thermal processes can be brought back to wide-range validity kinetic models thanks to their homogeneous reaction environments. The present paper is aimed at providing a reduced kinetic scheme for reliable thermal conversion of H2S molecule in pyrolysis and partial oxidation thermal processes. The proposed model consists of 10 reactions and 12 molecular species. Its validation is performed by numerical comparisons with a detailed kinetic model already validated by literature/industrial data at the operating conditions of interest. The validated reduced model could be easily adopted in commercial process simulators for the flow sheeting of H2S conversion processes.