LAPSE:2025.0439
Published Article

LAPSE:2025.0439
Network Theoretical Analysis of the Reaction Space in Biorefineries
June 27, 2025
Abstract
The analysis of large chemical reaction space sheds light on reaction patterns between molecules and can inform subsequent reaction pathway planning. With the aim to discover more sustainable production systems, it became worthwhile to explicitly model the reaction space reachable from biobased feedstocks. In particular, the space that reactions in integrated biorefineries span for optimised biorefinery planning is of interest. In this work we show a network-theoretical analysis of biorefinery reaction data. We utilise the directed all-to-all mapping between reactants and products to compare the reaction space obtained from biorefineries with the entire network of organic chemistry (NOC). In our results, we find that despite having 1000 times fewer molecules, the constructed network resembles the NOC in terms of its scale-free nature and shares similarities regarding its small-world property. Additionally, we analyse the coverage rate of the biorefinery reaction data and find that many common second and third-level intermediates are present, while the querying strategy leads to a lack of data on value-added refinery end-products and on feedstock molecules, requiring more advanced and combined data querying strategies in the future.
The analysis of large chemical reaction space sheds light on reaction patterns between molecules and can inform subsequent reaction pathway planning. With the aim to discover more sustainable production systems, it became worthwhile to explicitly model the reaction space reachable from biobased feedstocks. In particular, the space that reactions in integrated biorefineries span for optimised biorefinery planning is of interest. In this work we show a network-theoretical analysis of biorefinery reaction data. We utilise the directed all-to-all mapping between reactants and products to compare the reaction space obtained from biorefineries with the entire network of organic chemistry (NOC). In our results, we find that despite having 1000 times fewer molecules, the constructed network resembles the NOC in terms of its scale-free nature and shares similarities regarding its small-world property. Additionally, we analyse the coverage rate of the biorefinery reaction data and find that many common second and third-level intermediates are present, while the querying strategy leads to a lack of data on value-added refinery end-products and on feedstock molecules, requiring more advanced and combined data querying strategies in the future.
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Suggested Citation
Kontak J, Weber JM. Network Theoretical Analysis of the Reaction Space in Biorefineries. Systems and Control Transactions 4:1787-1793 (2025) https://doi.org/10.69997/sct.168427
Author Affiliations
Kontak J: Delft University of Technology, Department of Intelligent Systems, Delft, The Netherlands
Weber JM: Delft University of Technology, Department of Intelligent Systems, Delft, The Netherlands
Weber JM: Delft University of Technology, Department of Intelligent Systems, Delft, The Netherlands
Journal Name
Systems and Control Transactions
Volume
4
First Page
1787
Last Page
1793
Year
2025
Publication Date
2025-07-01
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Original Submission
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PII: 1787-1793-1441-SCT-4-2025, Publication Type: Journal Article
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LAPSE:2025.0439
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https://doi.org/10.69997/sct.168427
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References Cited
- Kamm, B, Kamm, MJAM. Principles of biorefineries. Applied microbiology and biotechnology 64(2) 137-145 (2004) https://doi.org/10.1007/s00253-003-1537-7
- Ioannou, I, D'Angelo, SC, Galán-Martín, Á, Pozo, C, Pérez-Ramírez, J, Guillén-Gosálbez, G. Process modelling and life cycle assessment coupled with experimental work to shape the future sustainable production of chemicals and fuels. Reaction Chemistry & Engineering 6(7) 1179-1194 (2021) https://doi.org/10.1039/D0RE00451K
- Voll, A, Marquardt, W. Reaction network flux analysis: Optimization-based evaluation of reaction pathways for biorenewables processing. AIChE Journal 58(6) 1788-1801 (2012). https://doi.org/10.1002/aic.12704
- Ulonska, K, Skiborowski, M, Mitsos, A, Viell, J. Early-stage evaluation of biorefinery processing pathways using process network flux analysis. AIChE Journal 62(9) 3096-3108 (2016) https://doi.org/10.1002/aic.15305
- Zhang, D, del Rio-Chanona, EA, Shah, N. Screening synthesis pathways for biomass-derived sustainable polymer production. ACS Sustainable Chemistry & Engineering 5(5) 4388-4398 (2017) https://doi.org/10.1021/acssuschemeng.7b00429
- Weber, JM, Guo, Z, Zhang, C, Schweidtmann, AM, Lapkin, AA. Chemical data intelligence for sustainable chemistry. Chemical Society Reviews 50(21) 12013-12036 (2021) https://doi.org/10.1039/D1CS00477H
- Jacob, PM, Yamin, P, Perez-Storey, C, Hopgood, M, Lapkin, AA. Towards automation of chemical process route selection based on data mining. Green Chemistry 19(1) 140-152 (2017) https://doi.org/10.1039/C6GC02482C
- Weber, JM, Guo, Z, & Lapkin, AA. Discovering Circular Process Solutions through Automated Reaction Network Optimization. ACS Engineering Au 2(4) 333-349 (2022) https://doi.org/10.1021/acsengineeringau.2c00002
- Szymkuc, S, Gajewska, EP, Klucznik, T, Molga, K, Dittwald, P, Startek, M, Bajczyk, M, Grzybowski, BA. Computer-assisted synthetic planning: the end of the beginning. Angewandte Chemie International Edition 55(20) 5904-5937 (2016) https://doi.org/10.1002/anie.201506101
- Jacob, PM., Lapkin, A. Statistics of the network of organic chemistry. Reaction Chemistry & Engineering 3(1) 102-118 (2018) https://doi.org/10.1039/C7RE00129K
- Mann, V, Venkatasubramanian, V. AI-driven hypergraph network of organic chemistry: network statistics and applications in reaction classification. Reaction Chemistry & Engineering 8(3) 619-635 (2023) https://doi.org/10.1039/D2RE00309K
- Weber, JM, Schweidtmann, AM, Nolasco, E, Lapkin, AA. Modelling circular structures in reaction networks: Petri nets and reaction network flux analysis. In Computer Aided Chemical Engineering 48 1843-1848 (2020) https://doi.org/10.1016/B978-0-12-823377-1.50308-6
- REAXYS fact sheet. June 2018
- Hagberg, A, Swart, PJ, Schult, DA. Exploring network structure, dynamics, and function using NetworkX (No. LA-UR-08-05495; LA-UR-08-5495). Los Alamos National Laboratory (LANL), Los Alamos, NM (United States) (2008)
- Alstott, J, Bullmore, E, Plenz, D. Powerlaw: a Python package for analysis of heavy-tailed distributions. PloS one 9(1) e85777 (2014) https://doi.org/10.1371/journal.pone.0085777
- Tey, TO, Chen, S, Cheong, ZX, Choong, ASX, Ng, LY, Chemmangattuvalappil, NG. Synthesis of a sustainable integrated biorefinery to produce value-added chemicals from palm-based biomass via mathematical optimisation. Sustainable Production and Consumption 26 288-315 (2021) https://doi.org/10.1016/j.spc.2020.10.012
- Grzybowski, BA, Bishop, KJ, Kowalczyk, B, Wilmer, CE. The'wired'universe of organic chemistry. Nature Chemistry 1(1) 31-36 (2009) https://doi.org/10.1038/nchem.136
- Bonato A, Chung F. Global clustering coefficients. In: Handbook of Graph Theory. Ed: Gross JL, Yellen J, Zhang P. CRC Press/Taylor and Francis, Boca Raton, FL, 2nd edn. (2014), pp. 1456-1476.
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