Sulfur oxidative coupling of methane process development and its modeling via machine learning
Sulfur oxidative coupling of methane (SOCM) has seen a significant improvement in catalyst design and performances, but there is still a lack of development at process level. We propose an optimized SOCM process flow diagram, with integrated waste heat recovery system and an efficient separation technique. The outcomes of the simulated process were used to design a data-driven modeling approach, based on machine learning methods, and to evaluate its interpolation accuracy. The simultaneous multi-input/multioutput relationship between the different parameters of the SOCM system were determined, revealing the optimum reaction conditions for the maximum benzene, toluene and xylene yield, at the minimum CH4 and H2S recycling rate.
Other Information
Published in: AIChE Journal
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: http://dx.doi.org/10.1002/aic.17793
Funding
Open Access funding provided by the Qatar National Library.
History
Language
- English
Publisher
WileyPublication Year
- 2022
License statement
This Item is licensed under the Creative Commons Attribution 4.0 International License.Institution affiliated with
- Hamad Bin Khalifa University
- Qatar Environment and Energy Research Institute - HBKU
