HybridTuner: Tuning with hybrid derivative-free optimization initialization strategies

Sauk, B., & Sahinidis, N. V. (2021). HybridTuner: Tuning with hybrid derivative-free optimization initialization strategies. In Simos, D.E., Pardalos, P.M., & Kotsireas, I.S. (Eds.), LION 2021: Learning and Intelligent Optimization, Lecture Notes in Computer Science 12931, (pp. 379-393). https://doi.org/10.1007/978-3-030-92121-7_29

Advanced-multi-step Moving Horizon Estimation

Yeonsoo Kim, Y., Kuan-Han Lin, K.-H., Thierry, D. M., & Biegler, L. T. (2021). Advanced-multi-step Moving Horizon Estimation. IFAC-PapersOnLine, 54(3), 269-274. https://doi.org/10.1016/j.ifacol.2021.08.253

Backward stepwise elimination: Approximation guarantee, a batched GPU algorithm, and empirical investigation

Sauk, B., & Sahinidis, N. V. (2021). Backward stepwise elimination: Approximation guarantee, a batched GPU algorithm, and empirical investigation. SN Computer Science, 2. https://doi.org/10.1007/s42979-021-00788-1

Model development, validation, and part-load optimization of an MEA-based post-combustion CO2 capture process under part-load and variable capture operations

Akula, P., Eslick, J., Bhattacharyya, D., & Miller, D. C. (2021). Model development, validation, and part-load optimization of an MEA-based post-combustion CO2 capture process under part-load and variable capture operations. Industrial & Engineering Chemistry Research, 60(14), 5176–5193. https://doi.org/10.1021/acs.iecr.0c05035

A perspective on nonlinear model predictive control

Biegler, L.T. (2022). A perspective on nonlinear model predictive control. Korean Journal of Chemical Engineering, 38, 1317–1332. https://doi.org/10.1007/s11814-021-0791-7

The IDAES process modeling framework and model library—Flexibility for process simulation and optimization

Lee, A., Ghouse, J. H., Eslick, J.C., Laird, C.D., Siirola, J.D., Zamarripa, M.A., Gunter, D., Shinn, J. H., Dowling, A. W., Bhattacharyya, D., Biegler, L. T., Burgard, A. P., & Miller, D.C. (2021). The IDAES process modeling framework and model library—Flexibility for process simulation and optimization. Journal of Advanced Manufacturing and Processing, 3(3), 1-30. https://doi.org/10.1002/amp2.10095

Decomposing optimization-based bounds tightening problems via graph partitioning

Bynum, M. L., Castillo, A., Kneuven, B., Laird, C. D., Siirola, J. D., Watson, & J. P. (2021). Decomposing optimization-based bounds tightening problems via graph partitioning. Journal of Global Optimization. https://www.osti.gov/biblio/1834338

A generalized cutting-set approach for nonlinear robust optimization in process systems engineering applications

Isenberg, N.M., Akula, P., Eslick, J.C., Bhattacharyya, D., Miller, D. C., & Gounaris, C. E. (2021). A generalized cutting-set approach for nonlinear robust optimization in process systems engineering applications. AIChE Journal, 67(5). https://doi.org/10.1002/aic.17175