Tag: Journal Articles/Technical Reports

Zhang, Y., He, N., Masuku, C. M., & Biegler, L. T. (2020). A multi-objective reactive distillation optimization model for Fischer–Tropsch synthesis. Computers & Chemical Engineering, 135. https://doi.org/10.1016/j.compchemeng.2020.106754

Liu, K., Biegler, L. T., Zhang, B., & Chen, Q. (2020). Dynamic optimization of natural gas pipeline networks with demand and composition uncertainty. Chemical Engineering Science, 215. https://doi.org/10.1016/j.ces.2019.115449

Liu, K., Kazi, S. R., Biegler, L. T., Zhang, B., & Chen, Q. (2020). Dynamic optimization for gas blending in pipeline networks with gas interchangeability control. AIChE Journal, 66(5). https://doi.org/10.1002/aic.16908

Sitter, S., Chen, Q., & Grossmann, I. E. (2019). An overview of process intensification methods. Current Opinion in Chemical Engineering, 25, 87-94. https://doi.org/10.1016/j.coche.2018.12.006

Wan, W., Eason, J. P., Nicholson, B., & Biegler, L. T. (2019). Parallel cyclic reduction decomposition for dynamic optimization problems. Computers and Chemical Engineering, 120, 54-69. https://doi.org/10.1016/j.compchemeng.2017.09.023

Chen, Q., & Grossmann, I. E. (2019). Modern modeling paradigms using generalized disjunctive programming. Processes, 7(11), 839. https://doi.org/10.3390/pr7110839

C.L. Hanselman, W. Zhong, K. Tran, Z.W. Ulissi and C.E. Gounaris (2019). Optimization-based design of active and stable nanostructured surfaces. The Journal of Physical Chemistry C, 123(48), 29209-29218. https://doi.org/10.1021/acs.jpcc.9b08431

Wilson, Z. & Sahinidis, N. V. (2019). Automated learning of chemical reaction networks. Computers & Chemical Engineering, 127, 88-98. https://doi.org/10.1016/j.compchemeng.2019.05.020

Biegler, L. T., & Jacobson, C. A. (2019). Optimization opportunities in product development: perspective from a manufacturing company. In Garcia Muñoz, S., Laird, C. D., Realff, M. J. (Eds.), Computer Aided Chemical Engineering, Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design, 47, 275-286. https://doi.org/10.1016/B978-0-12-818597-1.50044-8

Klise, K., Nicholson, B., Straid, A., & Woodruff, D. L. (2019). PARMEST: Parameter estimation via Pyomo. Computer Aided Chemical Engineering, 47, 41-46. https://doi.org/10.1016/B978-0-12-818597-1.50007-2