Silicon carbide foam packed-bed reactor for solar-driven biomass pyrolysis: Robust design under variable solar input
Abstract
Solar-driven biomass pyrolysis combines renewable feedstock with solar heat, but variable irradiation challenges reactor performance. This study evaluates whether a silicon carbide (SiC) foam-enhanced packed-bed reactor can maintain favorable performance across an empirical distribution of solar input and identifies the foam porosity and carrier-gas nominal residence time best suited to that variability. An 85-case computational fluid dynamics dataset was used to train machine-learning surrogate models linking solar input, foam porosity, and nominal residence time to effective conversion rate (ECR) and volatile energy fraction (VEF). Multi-objective optimization identified six candidate designs, which were evaluated using Monte Carlo samples drawn from five years of Zurich irradiance data and bagging-based surrogate predictions. Results show that pronounced performance sensitivity was concentrated in the lowest 20% of the empirical solar-input distribution. Under conservative estimates, all six candidates had a joint probability above 50% of achieving ECR > 70% and VEF > 80%. The combination of foam porosity 0.90 and nominal residence time 60 s achieved the highest joint probability (61%). These findings demonstrate that a fixed SiC foam reactor can maintain favorable performance under variable solar input and provide practical guidance for uncertainty-aware reactor design.
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BibTeX
@article{liu2026silicon,
title = {{Silicon carbide foam packed-bed reactor for solar-driven biomass pyrolysis: Robust design under variable solar input}},
author = {Yang Liu and Ruming Pan and Renaud Ansart and Gerald Debenest},
journal = {Energy and AI},
year = {2026},
month = oct,
doi = {10.1016/j.egyai.2026.100947},
url = {https://hal.science/hal-05837032v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Silicon carbide foam packed-bed reactor for solar-driven biomass pyrolysis: Robust design under variable solar input
AU - Yang Liu
AU - Ruming Pan
AU - Renaud Ansart
AU - Gerald Debenest
PY - 2026
DA - 2026/10/23
JO - Energy and AI
DO - 10.1016/j.egyai.2026.100947
UR - https://hal.science/hal-05837032v1
AB - Solar-driven biomass pyrolysis combines renewable feedstock with solar heat, but variable irradiation challenges reactor performance. This study evaluates whether a silicon carbide (SiC) foam-enhanced packed-bed reactor can maintain favorable performance across an empirical distribution of solar input and identifies the foam porosity and carrier-gas nominal residence time best suited to that variability. An 85-case computational fluid dynamics dataset was used to train machine-learning surrogate models linking solar input, foam porosity, and nominal residence time to effective conversion rate (ECR) and volatile energy fraction (VEF). Multi-objective optimization identified six candidate designs, which were evaluated using Monte Carlo samples drawn from five years of Zurich irradiance data and bagging-based surrogate predictions. Results show that pronounced performance sensitivity was concentrated in the lowest 20% of the empirical solar-input distribution. Under conservative estimates, all six candidates had a joint probability above 50% of achieving ECR > 70% and VEF > 80%. The combination of foam porosity 0.90 and nominal residence time 60 s achieved the highest joint probability (61%). These findings demonstrate that a fixed SiC foam reactor can maintain favorable performance under variable solar input and provide practical guidance for uncertainty-aware reactor design.
SN - 2666-5468
ER -