Tag: Journal Articles/Technical Reports

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

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

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

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.

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

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

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

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

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.

Ostace, A., Chen, Y., Parker, R., Okoli, C. O., Lee, A., Tong, A., Fan, L.-S., Biegler, L. T., Burgard, A. P., Miller, D. C., Mebane, D. S., & Bhattacharyya, D. (2022). Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers for chemical looping combustion. Chemical Engineering Science, 252. https://doi.org/10.1016/j.ces.2022.117512

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