Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems

Cho, S., Tovar-Facio, J., & Grossmann, I.E. (2023) Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems. Computers and Chemical Engineering, 174, 108243. https://doi.org/10.1016/j.compchemeng.2023.108243

Scalable parallel nonlinear optimization with PyNumero and Parapint

Rodriguez, J. S., Parker, R., Laird, C., Nicholson, B., Siirola, J., & Bynum, M. (2023). Scalable parallel nonlinear optimization with PyNumero and Parapint. INFORMS Journal on Computing, 35(2), 265-517. https://doi.org/10.1287/ijoc.2023.1272

A complementarity-based vapor-liquid equilibrium formulation for equation-oriented simulation and optimization

Dabadghao, V., Ghouse, J., Eslick, J., Lee, A., Burgard, A.P., Miller, D., & Biegler, L. (2023). A complementarity-based vapor-liquid equilibrium formulation for equation-oriented simulation and optimization. AIChE Journal, 69 (4). https://doi.org/10.1002/aic.18029

Dynamic modeling and nonlinear model predictive control of a moving bed chemical looping combustion reactor

Parker, R., & Biegler, L. T. (2022). Dynamic modeling and nonlinear model predictive control of a moving bed chemical looping combustion reactor. IFAC-PapersOnLine, 55(7), 400-405. https://doi.org/10.1016/j.ifacol.2022.07.476.

An implicit function formulation for nonlinear programming with index-1 differential algebraic equation systems

Parker, R., Nicholson, B. L., Siirola, J., Laird, C. D., & Biegler, L. T. (2022). An implicit function formulation for nonlinear programming with index-1 differential algebraic equation systems. In Y. Yamashita & M. Kano (Eds.), 14th International Symposium on Process Systems Engineering (PSE2021+), Computer Aided Chemical Engineering, (Vol. 49, pp. 1141-1146). Elsevier. https://doi.org/10.1016/B978-0-323-85159-6.50190-1

Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization

Cho, S., Li, C., & Grossmann, I.E. (2022). Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization. Computers and Chemical Engineering, 165, 1079245. https://doi.org/10.1016/j.compchemeng.2022.107924

Application of an equation-oriented framework to the formulation and parameter estimation of chemical looping reaction kinetic models

Okoli, C. O., Parker, R., Chen, Y., Ostace, A., Lee, A., Bhattacharyya, D., Tong, A., Biegler, L. T., Burgard, A. P., & Miller, D. C. (2022). Application of an equation-oriented framework to the formulation and parameter estimation of chemical looping reaction kinetic models. AIChE Journal 2022, 68(10). https://doi.org/10.1002/aic.17796

Hyperparameter tuning of programs with HybridTuner

Sauk, B., & Sahinidis, N. V. (2022). Hyperparameter tuning of programs with HybridTuner. Annals of Mathematics and Artificial Intelligence, 91, 133–151. https://doi.org/10.1007/s10472-022-09793-3

Predictive modeling of an existing subcritical pulverized-coal power plant for optimization: data reconciliation, parameter estimation, and validation

Eslick, J. C., Zamarripa, M. A., Ma, J., M. Wang, M., Bhattacharya, I., Rychener, B., Pinkston, P., Bhattacharyya, D., Zitney, S. E., Burgard, A. P., & Miller, D. C. (2022). Predictive modeling of an existing subcritical pulverized-coal power plant for optimization: data reconciliation, parameter estimation, and validation. Applied Energy, 319. https://doi.org/10.1016/j.apenergy.2022.119226.

Don’t search – Solve! Process optimization modeling with IDAES

Biegler, L. T., D. C. Miller, D. C., & Okoli, C. O. (2022). Don’t search – Solve! Process optimization modeling with IDAES. In Bortz, M., & Asprion, N. (Eds.), Simulation and Optimization in Process Engineering, (pp. 33-53). Elsevier. https://doi.org/10.1016/B978-0-323-85043-8.00005-2