Mohammadi, S., & Cremaschi, S. (2019). Efficiency of Uncertainty Propagation Methods for Estimating Output Moments. Computer Aided Chemical Engineering, 47, 487-492. https://doi.org/10.1016/b978-0-12-818597-1.50078-3
Tian, Y., Sam Mannan, M., Kravanja, Z., & Pistikopoulos, E. N. (2018). Towards the synthesis of modular process intensification systems with safety and operability considerations - application to heat exchanger network. 28 European Symposium on Computer Aided Process Engineering, 43, 705-710. https://doi.org/10.1016/B978-0-444-64235-6.50125-X (Original work published 2025)
Demirel, S. E., Li, J., & Hasan, M. M. F. (2019). A General Framework for Process Synthesis, Integration, and Intensification. Industrial & Engineering Chemistry Research, 58, 5950-5967. https://doi.org/10.1021/acs.iecr.8b05961 (Original work published 2025)
Arora, A., Iyer, S. S., Bajaj, I., & Hasan, M. M. F. (2018). Optimal Methanol Production via Sorption-Enhanced Reaction Process. Industrial & Engineering Chemistry Research, 57, 14143-14161. https://doi.org/10.1021/acs.iecr.8b02543 (Original work published 2025)
Tian, Y., & Pistikopoulos, E. N. (2019). Generalized Modular Representation Framework for the Synthesis of Extractive Separation Systems. Computer Aided Chemical Engineering, 47, 475-480. https://doi.org/10.1016/b978-0-12-818597-1.50076-x
Arora, A., Li, J., Zantye, M. S., & Hasan, M. M. F. (2020). Design standardization of unit operations for reducing the capital intensity and cost of small-scale chemical processes. AIChE Journal, 66, e16802. https://doi.org/10.1002/aic.16802 (Original work published 2025)
Tula, A. K., Eden, M. R., & Gani, R. (2019). Hybrid method and associated tools for synthesis of sustainable process flowsheets. Computers and Chemical Engineering, 131, 106572. https://doi.org/10.1016/j.compchemeng.2019.106572 (Original work published 2025)
Avraamidou, S., & Pistikopoulos, E. N. (2019). A Bi-Level Formulation And Solution Method For The Integration Of Process Design And Scheduling. Computer Aided Chemical Engineering, 47, 17-22. https://doi.org/10.1016/B978-0-12-818597-1.50003-5 (Original work published 2025)
Kim, S. H., & Boukouvala, F. (2019). Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques. Optimization Letters, 14, 989-1010. https://doi.org/10.1007/s11590-019-01428-7 (Original work published 2025)
Tian, Y., Demirel, S. E., Hasan, M. M. F., & Pistikopoulos, E. N. (2018). An overview of process systems engineering approaches for process intensification: State of the art. Chemical Engineering and Processing - Process Intensification, 133, 160-210. https://doi.org/10.1016/j.cep.2018.07.014 (Original work published 2025)
