  Chemical and biomass processing systems release volatile matter compounds
into the environment daily. Catalytic 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 {\deg}C, with a steam-to-carbon molar ratio of 5.81-7.15 and a catalyst
BET surface area 476.03-638.55 m2/g. The feature importance 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.
