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.

中文翻译：

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甲烷工艺开发的硫氧化耦合及其机器学习建模

甲烷的硫氧化偶联（SOCM）在催化剂设计和性能方面有了显着改善，但在工艺层面仍缺乏发展。我们提出了优化的 SOCM 工艺流程图，具有集成的废热回收系统和高效的分离技术。模拟过程的结果用于设计基于机器学习方法的数据驱动建模方法，并评估其插值精度。确定了SOCM系统不同参数之间的同时多输入/多输出关系，揭示了在最小CH ₄和H ₂ S回收率下最大苯、甲苯和二甲苯收率的最佳反应条件。