LAPSE:2026.0397
Published Article

LAPSE:2026.0397
Unveiling Reaction Patterns in Thermal and Catalytic Biomass Pyrolysis Using PCA and Multivariate Analysis
June 12, 2026
Abstract
Understanding the relationships between operating conditions and product formation pathways in biomass pyrolysis remains challenging due to the complex interactions among temperature, catalytic effects, and feedstock composition. In this work, principal component analysis (PCA) was applied to investigate the combined influence of temperature and catalyst-to-biomass ratio on the pyrolysis of sugarcane bagasse and Salicornia. To preserve mechanistic interpretability, two complementary analyses were performed: one considering only catalytic experiments and a second integrating both thermal and catalytic conditions. Separate PCA were conducted for product yields, gas and liquid compositions, and solid-phase FTIR features. The results reveal that thermal conditions promote severe cracking and solid carbonization, whereas catalytic operation favors secondary pathways associated with controlled dehydration and partial stabilization of liquid products. Distinct patterns between the two feedstocks were also identified, reflecting their intrinsic compositional differences. Based on the multivariate trends identified, a conceptual reaction scheme is proposed to describe the interplay between thermal and catalytic pathways.
Understanding the relationships between operating conditions and product formation pathways in biomass pyrolysis remains challenging due to the complex interactions among temperature, catalytic effects, and feedstock composition. In this work, principal component analysis (PCA) was applied to investigate the combined influence of temperature and catalyst-to-biomass ratio on the pyrolysis of sugarcane bagasse and Salicornia. To preserve mechanistic interpretability, two complementary analyses were performed: one considering only catalytic experiments and a second integrating both thermal and catalytic conditions. Separate PCA were conducted for product yields, gas and liquid compositions, and solid-phase FTIR features. The results reveal that thermal conditions promote severe cracking and solid carbonization, whereas catalytic operation favors secondary pathways associated with controlled dehydration and partial stabilization of liquid products. Distinct patterns between the two feedstocks were also identified, reflecting their intrinsic compositional differences. Based on the multivariate trends identified, a conceptual reaction scheme is proposed to describe the interplay between thermal and catalytic pathways.
Record ID
Keywords
Subject
Suggested Citation
Rodríguez-Fragoso M, González-Arias S, Elizalde-Solis O, Ramírez-Jiménez E. Unveiling Reaction Patterns in Thermal and Catalytic Biomass Pyrolysis Using PCA and Multivariate Analysis. Systems and Control Transactions 5:1539-1550 (2026) https://doi.org/10.69997/sct.125260
Author Affiliations
Rodríguez-Fragoso M: Department of Chemical Petroleum Engineering, ESIQIE, Instituto Politécnico Nacional, Mexico City, 07738, Mexico [ORCID]
González-Arias S: Department of Chemical Petroleum Engineering, ESIQIE, Instituto Politécnico Nacional, Mexico City, 07738, Mexico [ORCID]
Elizalde-Solis O: Department of Chemical Petroleum Engineering, ESIQIE, Instituto Politécnico Nacional, Mexico City, 07738, Mexico [ORCID]
Ramírez-Jiménez E: Department of Chemical Petroleum Engineering, ESIQIE, Instituto Politécnico Nacional, Mexico City, 07738, Mexico [ORCID]
[Login] to see author email addresses.
González-Arias S: Department of Chemical Petroleum Engineering, ESIQIE, Instituto Politécnico Nacional, Mexico City, 07738, Mexico [ORCID]
Elizalde-Solis O: Department of Chemical Petroleum Engineering, ESIQIE, Instituto Politécnico Nacional, Mexico City, 07738, Mexico [ORCID]
Ramírez-Jiménez E: Department of Chemical Petroleum Engineering, ESIQIE, Instituto Politécnico Nacional, Mexico City, 07738, Mexico [ORCID]
[Login] to see author email addresses.
Journal Name
Systems and Control Transactions
Volume
5
First Page
1539
Last Page
1550
Year
2026
Publication Date
2026-06-12
Version Comments
Original Submission
Other Meta
PII: 1539-1550-60-SCT-5-2026, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2026.0397
This Record
External Link

https://doi.org/10.69997/sct.125260
Publisher Version
Download
Meta
Record Statistics
Record Views
137
Version History
[v1] (Original Submission)
Jun 12, 2026
Verified by curator on
Jun 12, 2026
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2026.0397
Record Owner
PSE Press
Links to Related Works
References Cited
- Collard FX, Blin J. A review on pyrolysis of biomass constituents: mechanisms and composition of the products obtained from the conversion of cellulose, hemicelluloses and lignin. Renewable and Sustainable Energy Reviews 38:594-608 (2014) https://doi.org/10.1016/j.rser.2014.06.013
- Vuppaladadiyam AK, Varsha Vuppaladadiyam SS, Sikarwar VS, Ahmad E, Pant KK, S M, Pandey A, Bhattacharya S, Sarmah A, Leu SY. A critical review on biomass pyrolysis: reaction mechanisms, process modeling and potential challenges. Journal of the Energy Institute 108:101236 (2023) https://doi.org/10.1016/j.joei.2023.101236
- Wang S, Dai G, Yang H, Luo Z. Lignocellulosic biomass pyrolysis mechanism: a state-of-the-art review. Progress in Energy and Combustion Science 62:33-86 (2017) https://doi.org/10.1016/j.pecs.2017.05.004
- Aguado R, Elordi G, Arrizabalaga A, Artetxe M, Bilbao J, Olazar M. Principal component analysis for kinetic scheme proposal in the thermal pyrolysis of waste HDPE plastics. Chemical Engineering Journal 254:357-364 (2014) https://doi.org/10.1016/j.cej.2014.05.131
- Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, van Kerkwijk MH, Brett M, Haldane A, del Río JF, Wiebe M, Peterson P, Gérard-Marchant P, Sheppard K, Reddy T, Weckesser W, Abbasi H, Gohlke C, Oliphant TE. Array programming with numpy. Nature 585:357-362 (2020) https://doi.org/10.1038/s41586-020-2649-2
- McKinney W. Data structures for statistical computing in python. Proceedings of the Python in Science Conference :56-61 (2010) https://doi.org/10.25080/majora-92bf1922-00a
- Pedregosa F, Varoquaux, G.Scikit-learn: Machine learning in python. JMLR 12:2825-2830 (2011)
- Ranzi E, Cuoci A, Faravelli T, Frassoldati A, Migliavacca G, Pierucci S, Sommariva S. Chemical kinetics of biomass pyrolysis. Energy Fuels 22:4292-4300 (2008) https://doi.org/10.1021/ef800551t
(0.08 seconds)
[0.09 s]

