Turning hazardous volatile matter compounds into fuel by catalytic steam reforming: An evolutionary machine learning approach

文献类型： 外文期刊

第一作者： Shafizadeh, Alireza

作者： Shafizadeh, Alireza;Shahbeik, Hossein;Peng, Wanxi;Tabatabaei, Meisam;Shafizadeh, Alireza;Aghbashlo, Mortaza;Shahbeik, Hossein;Lam, Su Shiung;Tabatabaei, Meisam;Nadian, Mohammad Hossein;Gupta, Vijai Kumar;Gupta, Vijai Kumar;Nizami, Abdul-Sattar;Lam, Su Shiung;Pan, Junting;Tabatabaei, Meisam

作者机构：

关键词： Volatile matter; Catalytic steam reforming; Toluene; Syngas; Ensemble machine learning; Biomass conversion

期刊名称：JOURNAL OF CLEANER PRODUCTION （ 影响因子：11.1； 五年影响因子：11.0 ）

ISSN： 0959-6526

年卷期： 2023 年 413 卷

页码：

收录情况： SCI

摘要： Chemical and biomass processing systems release volatile matter compounds into the environment daily. Cat-alytic reforming can convert these compounds into valuable fuels, but developing stable and efficient catalysts is challenging. Machine learning can handle complex relationships in big data and optimize reaction conditions, making it an effective solution for addressing the mentioned issues. This study is the first to develop a machine -learning-based research framework for modeling, understanding, and optimizing the catalytic steam reforming of volatile matter compounds. Toluene catalytic steam reforming is used as a case study to show how chemical/ textural analyses (e.g., X-ray diffraction analysis) can be used to obtain input features for machine learning models. Literature is used to compile a database covering a variety of catalyst characteristics and reaction conditions. The process is thoroughly analyzed, mechanistically discussed, modeled by six machine learning models, and optimized using the particle swarm optimization algorithm. Ensemble machine learning provides the best prediction performance (R2 > 0.976) for toluene conversion and product distribution. The optimal tar conversion (higher than 77.2%) is obtained at temperatures between 637.44 and 725.62 degrees C, with a steam-to -carbon molar ratio of 5.81-7.15 and a catalyst BET surface area of 476.03-638.55 m2/g. The feature impor-tance analysis satisfactorily reveals the effects of input descriptors on model prediction. Operating conditions (50.9%) and catalyst properties (49.1%) are equally important in modeling. The developed framework can expedite the search for optimal catalyst characteristics and reaction conditions, not only for catalytic chemical processing but also for related research areas.

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