Lattice Boltzmann Method and Back-Propagation Artificial Neural Network-Based Coke Mapping of Solid Acid Catalyst in Fructose Conversion

Mapping and understanding humin coking during carbohydrate conversion is crucial for improving solid catalyst systems. However, models for coke mapping are still in need of development. In this study, a lattice Boltzmann method-based back-propagation artificial neural network reduced-order model (ROM) is developed to map the humin distribution during the conversion process. The ROM reveals three configurations of intraparticle coking distribution (surface focus, middle-layer focus, and central focus coking). The experimental feature of surface-focus configuration is the timely decreasing trend in the macroscopic coke accumulation rate, especially under extreme conditions (surface humins/central humins >10). Catalyst load, pellet size, substrate concentration (LSC), and temperature collectively influence the coking configurations. As the temperature increases (100–160 °C), the configuration with the highest occupancy in the LSC coordinate space changes from central to middle-layer and finally to surface configuration. Increasing the catalyst loading, reducing the particle size, and lowering the substrate concentration under a threshold number (φ_LSC) in LSC space helps prevent the catalyst from working under the extreme surface coking configuration status.