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

TL;DR

This paper introduces a machine learning framework to model and optimize catalytic steam reforming of volatile compounds, demonstrating high prediction accuracy and identifying key factors influencing process efficiency.

Contribution

It is the first to develop a machine-learning-based framework for modeling, understanding, and optimizing catalytic steam reforming of volatile matter compounds.

Findings

Ensemble machine learning achieved R2 > 0.976 in predictions.

Optimal tar conversion >77.2% at specific temperature and catalyst conditions.

Operating conditions and catalyst properties are equally important in modeling.

Abstract

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.