Publications

For the latest list, see Lirias · Google Scholar.

Mores, W., Bhonsale, S., Floros, S., Logist, F., & Impe, J. V. (2026). Recursive exploration of metabolic yield space (p. 2026.05.28.728453). bioRxiv. https://doi.org/10.64898/2026.05.28.728453

Lapiedra Carrasquer, P., Muñoz López, C., Dockx, K., Van Impe, J., & Bhonsale, S. (2026). Modeling continuous direct compression processes with inherent delay using SINDYc and bayesian inference. Computers & Chemical Engineering, (109646). https://doi.org/10.1016/j.compchemeng.2026.109646

Wang, J., Hashem, I., Bhonsale, S., & Van Impe, J. (2026). Signaling dynamics in microbial communities: A reduced-complexity mathematical analysis. Chemical Engineering Science, 319(1). https://doi.org/10.1016/j.ces.2025.121962

Van Impe, J., Leonard, G., Bhonsale, S., Polanska, M., & Logist, F. (Eds.). (2025a). 35th european symposium on computer aided process engineering (ESCAPE 35): Book of short papers. EUROSIS-ETI.

Van Impe, J., Leonard, G., Bhonsale, S., Polanska, M., & Logist, F. (Eds.). (2025b). Proceedings of the 35th european symposium on computer aided process engineering (ESCAPE 35) (Vol. 4). PSE Press: Hamilton. https://doi.org/10.69997/sct.173479

Mores, W., Bhonsale, S., Logist, F., & Van Impe, J. (2025). Accelerated enumeration of extreme rays through a positive-definite elementarity test. Bioinformatics (Oxford, England), 41(1). https://doi.org/10.1093/bioinformatics/btae723

Quillo, G., Bhonsale, S., Collas, A., Van Impe, J., & Xiouras, C. (2025). Hybrid semi-mechanistic and machine learning solubility regression modeling for crystallization process development. CRYSTAL GROWTH & DESIGN, 25(4), 1111–1127. https://doi.org/10.1021/acs.cgd.4c01451

Wang, J., Hashem, I., Bhonsale, S., & Van Impe, J. (2025). Individual-based modeling unravels spatial and social interactions in bacterial communities. ISME JOURNAL, 19(1). https://doi.org/10.1093/ismejo/wraf116

Wang, J., Hashem, I., Bhonsale, S., Verheyen, D., Luo, H., & Van Impe, J. (2025). Individual-based modelling (IbM) in food microbiology: A comprehensive guideline. FOOD RESEARCH INTERNATIONAL, 213(ARTN 116408). https://doi.org/10.1016/j.foodres.2025.116408

Beitia, E., Ebert, E., Plank, M., Chanos, P., Hertel, C., Bhonsale, S., Van Impe, J., Heinz, V., Aganovic, K., & Valdramidis, V. (2024). Modelling of Salmonella Enteritidis inactivation in liquid whole egg under dynamic manothermosonication treatments. Innovative Food Science & Emerging Technologies, 92(103597). https://doi.org/10.1016/j.ifset.2024.103597

Katsini, L., Bhonsale, S., Roufou, S., Griffin, S., Valdramidis, V., Akkermans, S., Polanska, M., & Van Impe, J. (2024). Milk contamination in Europe under anticipated climate change scenarios. Frontiers in Sustainable Food Systems, 8(1468698). https://doi.org/10.3389/fsufs.2024.1468698

Katsini, L., Lopez, C., Bhonsale, S., Roufou, S., Griffin, S., Valdramidis, V., Akkermans, S., Polanska, M., & Van Impe, J. (2024). Modeling climatic effects on milk production. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 225(ARTN 109218). https://doi.org/10.1016/j.compag.2024.109218

De Buck, V., Sbarciog, M., Cras, J., Bhonsale, S., Polanska, M., & Van Impe, J. (2023). Critical analysis of the use of white-box versus black-box models for multi-objective optimisation of small-scale biorefineries. Frontiers in Food Science and Technology, 3(1154305). https://doi.org/10.3389/frfst.2023.1154305

Quillo, G., Bhonsale, S., Collas, A., Xiouras, C., & Van Impe, J. (2023). Iterative model-based optimal experimental design for mixture-process variable models to predict solubility. CHEMICAL ENGINEERING RESEARCH & DESIGN, 189, 768–780. https://doi.org/10.1016/j.cherd.2022.12.006

Bhonsale, S., Nimmegeers, P., Akkermans, S., Telen, D., stamati, I., Logist, F., & Van Impe, J. (2022). Optimal experiment design for dynamic processes. In Simulation and optimization in process engineering (pp. 243–271). Elsevier. https://doi.org/10.1016/B978-0-323-85043-8.00010-6

Mores, W., Nimmegeers, P., Hashem, I., Bhonsale, S., & Van Impe, J. (2022). Multi-objective optimization under parametric uncertainty: A Pareto ellipsoids-based algorithm. Computers & Chemical Engineering, 169(108099). https://doi.org/10.1016/j.compchemeng.2022.108099

Sbarciog, M., De Buck, V., Akkermans, S., Bhonsale, S., Polanska, M., & Van Impe, J. (2022). Design, implementation and simulation of a small-scale biorefinery model. Processes, 10(5). https://doi.org/10.3390/pr10050829

Bhonsale, S., Mores, W., & Van Impe, J. (2021). Dynamic optimisation of beer fermentation under parametric uncertainty. Fermentation-Basel, 7(4). https://doi.org/10.3390/fermentation7040285

Bhonsale, S., Scott, L., Ghadiri, M., & Van Impe, J. (2021). Numerical simulation of particle dynamics in a spiral jet mill via coupled CFD-DEM. Pharmaceutics, 13(7). https://doi.org/10.3390/pharmaceutics13070937

Katsini, L., Bhonsale, S., Akkermans, S., Roufou, S., Griffin, S., Valdramidis, V., Misiou, O., Koutsoumanis, K., Muñoz Lopéz, C., Polanska, M., & Van Impe, J. (2021). Quantitative methods to predict the effect of climate change on microbial food safety: A needs analysis. Trends In Food Science & Technology, 126, 113–125. https://doi.org/10.1016/j.tifs.2021.07.041

Lunardon Quillo, G., Bhonsale, S., Gielen, B., Van Impe, J., Collas, A., & Xiouras, C. (2021). Crystal growth kinetics of an industrial active pharmaceutical ingredient: Implications of different representations of supersaturation and simultaneous growth mechanisms. Crystal Growth & Design, 1–18. https://doi.org/10.1021/acs.cgd.1c00677

Nimmegeers, P., Vercammen, D., Bhonsale, S., Logist, F., & Van Impe, J. (2021). Metabolic reaction network-based model predictive control of bioprocesses. Applied Sciences-Basel, 11(20). https://doi.org/10.3390/app11209532

Bhonsale, S., Stokbroekx, B., & Van Impe, J. (2020). Assessment of the parameter identifiability of population balance models for air jet mills. Computers & Chemical Engineering, (107056). https://doi.org/10.1016/j.compchemeng.2020.107056

Muñoz López, C., Bhonsale, S., Peeters, K., & Van Impe, J. (2020). Manifold learning and clustering for automated phase identification and alignment in data driven modeling of batch processes. Frontiers in Chemical Engineering. https://doi.org/10.3389/fceng.2020.582126

Nimmegeers, P., Bhonsale, S., Telen, L., & Van Impe, J. (2020). Optimal experiment design under parametric uncertainty: A comparison of a sensitivities based approach versus a polynomial chaos based stochastic approach. Chemical Engineering Science, (115651). https://doi.org/10.1016/j.ces.2020.115651

Bhonsale, S., Muñoz López, C., & Van Impe, J. (2019). Global sensitivity analysis of a spray drying process. Processes, 7(9). https://doi.org/10.3390/pr7090562

Bhonsale, S., Telen, D., Stokbroekx, B., & Van Impe, J. (2019). Comparison of numerical solution strategies for population balance model of continuous cone mill. POWDER TECHNOLOGY, 345, 739–749. https://doi.org/10.1016/j.powtec.2019.01.043

Bhonsale, S., Telen, D., Stokbroekx, B., & Van Impe, J. (2018). An analysis of uncertainty propagation methods applied to breakage population balance. Processes, 6(12). https://doi.org/10.3390/pr6120255