Publications

Publications

  • All
  • Book/Book Chapter
  • Journal Articles/Technical Reports

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

Technoeconomic evaluation of solid oxide fuel cell hydrogen-electricity co-generation concepts

Eslick, J., Noring, A., Susarla, N., Okoli, C., Allan, D., Wang, M., Ma, J., Zamarripa, M., Iyengar, A., & Burgard, A. (2022). Technoeconomic evaluation of solid oxide fuel cell hydrogen-electricity co-generation concepts (DOE/NETL-2023/4322). Pittsburgh, PA: National Energy Technology Laboratory, U.S. Department of Energy. https://www.osti.gov/biblio/1960782

Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers for chemical looping combustion

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

Search methods for inorganic materials crystal structure prediction

Yin, X., & Gounaris, C. E. (2022). Search methods for inorganic materials crystal structure prediction. Current Opinion in Chemical Engineering, 35. https://doi.org/10.1016/j.coche.2021.100726

Multiscale simulation of integrated energy system and electricity market interactions

Gao, X., Knueven, B., Siirola, J. D., Miller, D. C., & Dowling, A.W. (2022). Multiscale simulation of integrated energy system and electricity market interactions. Applied Energy, 316. https://doi.org/10.1016/j.apenergy.2022.119017

Design space description through adaptive sampling and symbolic computation

Zhao, F., Grossmann, I. E., García Muñoz, S., & Stamatis, S. D. (2022). Design space description through adaptive sampling and symbolic computation. AIChE Journal 2022, 68(5). https://doi.org/10.1002/aic.17604

MatOpt: A Python package for nanomaterials design using discrete optimization

Hanselman, C. L., Yin, X., Miller, D. C., & Gounaris, C. E. (2022). MatOpt: A Python package for nanomaterials design using discrete optimization. Journal of Chemical Information and Modeling, 62(2), 295-308. https://doi.org/10.1021/acs.jcim.1c00984

On representative day selection for capacity expansion planning of power systems under extreme events

Li, C., Conejo, A. J., Siirola, J. D., & Grossmann, I. E. (2022). On representative day selection for capacity expansion planning of power systems under extreme events. International Journal of Electrical Power & Energy Systems, 137. https://doi.org/10.1016/j.ijepes.2021.107697

Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems.

Li., C., Conejo, A. J., Liu, P., Omell, B. P., Siirola, J. D., & Grossmann, I. E. (2022). Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems. European Journal of Operation Research, 297(3), 1071-1082. https://doi.org/10.1016/j.ejor.2021.06.024

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

Serial advanced-multi-step nonlinear model predictive control using an extended sensitivity method

Kim, Y., Thierry, D. M., & Biegler, L. T. (2020). Serial advanced-multi-step nonlinear model predictive control using an extended sensitivity method. J. Process Control, 96, 82-93. https://doi.org/10.1016/j.jprocont.2020.11.002

A discussion on practical considerations with sparse regression methodologies

Sarwar, O., Sauk, B., & Sahinidis, N. V. (2020). A discussion on practical considerations with sparse regression methodologies. Statistical Science, 35(4) 593-601. https://doi.org/10.48550/arXiv.2011.09362

Model order reduction in chemical process optimization

Eason, J. P., & Biegler, L. T. (2020). Model order reduction in chemical process optimization. In Benner, P., Grivet-Talocia, S., Quarteroni, A., Rozza, G., Schilders, W., & Silveira, L. M. (Eds.), Model Order Reduction. Volume 3: Applications, (pp. 1-32). De Gruyter. https://doi.org/10.1515/9783110499001-001

Nonlinear optimization strategies for process separations and process intensification

Biegler, L.T. (2020). Nonlinear optimization strategies for process separations and process intensification. Chemie Ingenieur Technik, 92(7), 867-878. https://doi.org/10.1002/cite.202000014

Nonlinear model predictive control of the hydraulic fracturing process

Lin, K.-H., Eason, J. P., Yu, Z. & Biegler, L. T. (2020). Nonlinear model predictive control of the hydraulic fracturing process. IFAC-PapersOnLine, 53(2), 11428-11433. https://doi.org/10.1016/j.ifacol.2020.12.579

Sensitivity-assisted multistage nonlinear model predictive control with online scenario adaptation.

Thombre, M., Yu, Z., Jaeschke, J., & Biegler, L.T. (2021). Sensitivity-assisted multistage nonlinear model predictive control with online scenario adaptation. Computers and Chemical Engineering, 148. https://doi.org/10.1016/j.compchemeng.2021.107269

A framework for the optimization of chemical looping combustion processes

Okoli, C. O., Ostace, A., Nadgouda, S., Lee, A., Tong, A., Burgard, A. P., Bhattacharyya, D., & Miller, D.C. (2020). A framework for the optimization of chemical looping combustion processes. Powder Technology, 365, 149-162. https://doi.org/10.1016/j.powtec.2019.04.035

A multi-objective reactive distillation optimization model for Fischer–Tropsch synthesis

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

Dynamic optimization of natural gas pipeline networks with demand and composition uncertainty

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

Dynamic optimization for gas blending in pipeline networks with gas interchangeability control

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

An overview of process intensification methods

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

Parallel cyclic reduction decomposition for dynamic optimization problems

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

Modern modeling paradigms using generalized disjunctive programming

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

Optimization-based design of active and stable nanostructured surfaces

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

Nonlinear programming formulations for nonlinear and economic model predictive control

Yu, M., Griffith, D. W., & Biegler, L. T. (2019). Nonlinear programming formulations for nonlinear and economic model predictive control. In Raković, S., Levine, W. (Eds.) Handbook of Model Predictive Control. Control Engineering. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-77489-3_20

Automated learning of chemical reaction networks

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

Optimization opportunities in product development: perspective from a manufacturing company

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

PARMEST: Parameter estimation via Pyomo

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

Dynamic optimization of natural gas network with rigorous thermodynamics under uncertainty

Liu, K., Biegler, L. T., Zhang, B. & Chen Q. (2019). Dynamic optimization of natural gas network with rigorous thermodynamics under uncertainty. 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, (pp. 317-322). https://doi.org/10.1016/B978-0-12-818597-1.50050-3

A framework for optimizing oxygen vacancy formation in doped perovskites

Hanselman, C. L., Tafen, D. Y., Alfonso, D. R., Lekse, J. W., Matranga, C., Miller, D. C., & Gounaris, C.E. (2019). A framework for optimizing oxygen vacancy formation in doped perovskites. Computers & Chemical Engineering, 126, 168-177. https://doi.org/10.1016/j.compchemeng.2019.03.033

Effective Generalized Disjunctive Program optimization models for modular process synthesis

Chen, Q. & Grossmann, I.E. (2019). Effective Generalized Disjunctive Program optimization models for modular process synthesis. Ind. Eng. Chem. Res. 58(15), 5873-5886. https://doi.org/10.1021/acs.iecr.8b04600

Kaibel column: Modeling, optimization, and conceptual design of multi-product dividing wall columns.

Rawlings, E.S., Chen, Q., Grossmann, I. E., & Caballero, J.A. (2019). Kaibel column: Modeling, optimization, and conceptual design of multi-product dividing wall columns. Comp. Chem. Eng. 125, 31-39. https://doi.org/10.1016/j.compchemeng.2019.03.006

Dynamic real-time optimization for a CO2 capture process

Thierry, D. M., & Biegler, L. T. (2019). Dynamic real-time optimization for a CO2 capture process. AIChE Journal, 65(7) 1-11. https://doi.org/10.1002/aic.16511

Large-scale optimization formulations and strategies for nonlinear model predictive control

Biegler, L. T., & Thierry, D. M. (2018). Large-scale optimization formulations and strategies for nonlinear model predictive control. IFAC-Papers OnLine, 51(20), 1–15. https://doi.org/10.1016/j.ifacol.2018.10.167

Benchmarking ADMM in nonconvex NLPs

Rodriguez, J.S., Nicholson, B., Laird, C.D., & Zavala, V.M. (2018). Benchmarking ADMM in nonconvex NLPs. Computers & Chemical Engineering, 119, 315-325. https://doi.org/10.1016/j.compchemeng.2018.08.036

GPU parameter tuning for tall and skinny dense linear least squares problems

Sauk, B., Ploskas, N., & Sahinidis, N. (2018). GPU parameter tuning for tall and skinny dense linear least squares problems. Optimization Methods and Software, 35(3), 638-660. https://doi.org/10.1080/10556788.2018.1527331

Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm

Lara, C.L., Mallapragada, D., Papageorgiou D., Venkatesh, A., & Grossmann, I. E. (2018). Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm. European Journal of Operational Research, 271(3), 1037-1054. https://doi.org/10.1016/j.ejor.2018.05.039

A framework for modeling and optimizing dynamic systems under uncertainty

Nicholson, B.L., & Siirola, J. D. (2018). A framework for modeling and optimizing dynamic systems under uncertainty. Computers & Chemical Engineering, 114, 81-88. https://doi.org/10.1016/j.compchemeng.2017.11.003

Effective GDP optimization models for modular process synthesis

Chen, Q., & Grossmann, I.E. (2018). Effective GDP optimization models for modular process synthesis. Current Opinion in Chemical Engineering. http://egon.cheme.cmu.edu/Papers/Chen_Qi_modular_updated.pdf

Pyomo.dae: A modeling and automatic discretization framework for optimization with differential and algebraic equations

Nicholson, B.L., Siirola, J. D., Watson, J.-P., Zavala, V. M., & Biegler, L.T. (2018). Pyomo.dae: A modeling and automatic discretization framework for optimization with differential and algebraic equations. Math Programming Computation, 10, 187-223. https://doi.org/10.1007/s12532-017-0127-0

A Framework for Modeling and Optimizing Dynamic Systems Under Uncertainty

Nicholson, B.L. and J.D. Siirola, A Framework for Modeling and Optimizing Dynamic Systems Under Uncertainty. In press, special FOCAPO/CPC issue of Computers & Chemical Engineering (2017)

A mathematical optimization framework for the design of nanopatterned surfaces

Hanselman, C. L., & Gounaris, C. E. (2016). A mathematical optimization framework for the design of nanopatterned surfaces. AIChE Journal, 62(9), 3250-3263. https://doi.org/10.1002/aic.15359
  • All
  • Book/Book Chapter
  • Journal Articles/Technical Reports

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
  • All
  • Book/Book Chapter
  • Journal Articles/Technical Reports

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

Technoeconomic evaluation of solid oxide fuel cell hydrogen-electricity co-generation concepts

Eslick, J., Noring, A., Susarla, N., Okoli, C., Allan, D., Wang, M., Ma, J., Zamarripa, M., Iyengar, A., & Burgard, A. (2022). Technoeconomic evaluation of solid oxide fuel cell hydrogen-electricity co-generation concepts (DOE/NETL-2023/4322). Pittsburgh, PA: National Energy Technology Laboratory, U.S. Department of Energy. https://www.osti.gov/biblio/1960782

Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers for chemical looping combustion

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

Search methods for inorganic materials crystal structure prediction

Yin, X., & Gounaris, C. E. (2022). Search methods for inorganic materials crystal structure prediction. Current Opinion in Chemical Engineering, 35. https://doi.org/10.1016/j.coche.2021.100726

Multiscale simulation of integrated energy system and electricity market interactions

Gao, X., Knueven, B., Siirola, J. D., Miller, D. C., & Dowling, A.W. (2022). Multiscale simulation of integrated energy system and electricity market interactions. Applied Energy, 316. https://doi.org/10.1016/j.apenergy.2022.119017

Design space description through adaptive sampling and symbolic computation

Zhao, F., Grossmann, I. E., García Muñoz, S., & Stamatis, S. D. (2022). Design space description through adaptive sampling and symbolic computation. AIChE Journal 2022, 68(5). https://doi.org/10.1002/aic.17604

MatOpt: A Python package for nanomaterials design using discrete optimization

Hanselman, C. L., Yin, X., Miller, D. C., & Gounaris, C. E. (2022). MatOpt: A Python package for nanomaterials design using discrete optimization. Journal of Chemical Information and Modeling, 62(2), 295-308. https://doi.org/10.1021/acs.jcim.1c00984

On representative day selection for capacity expansion planning of power systems under extreme events

Li, C., Conejo, A. J., Siirola, J. D., & Grossmann, I. E. (2022). On representative day selection for capacity expansion planning of power systems under extreme events. International Journal of Electrical Power & Energy Systems, 137. https://doi.org/10.1016/j.ijepes.2021.107697

Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems.

Li., C., Conejo, A. J., Liu, P., Omell, B. P., Siirola, J. D., & Grossmann, I. E. (2022). Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems. European Journal of Operation Research, 297(3), 1071-1082. https://doi.org/10.1016/j.ejor.2021.06.024
  • All
  • Book/Book Chapter
  • Journal Articles/Technical Reports

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
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  • Journal Articles/Technical Reports

Serial advanced-multi-step nonlinear model predictive control using an extended sensitivity method

Kim, Y., Thierry, D. M., & Biegler, L. T. (2020). Serial advanced-multi-step nonlinear model predictive control using an extended sensitivity method. J. Process Control, 96, 82-93. https://doi.org/10.1016/j.jprocont.2020.11.002

A discussion on practical considerations with sparse regression methodologies

Sarwar, O., Sauk, B., & Sahinidis, N. V. (2020). A discussion on practical considerations with sparse regression methodologies. Statistical Science, 35(4) 593-601. https://doi.org/10.48550/arXiv.2011.09362

Model order reduction in chemical process optimization

Eason, J. P., & Biegler, L. T. (2020). Model order reduction in chemical process optimization. In Benner, P., Grivet-Talocia, S., Quarteroni, A., Rozza, G., Schilders, W., & Silveira, L. M. (Eds.), Model Order Reduction. Volume 3: Applications, (pp. 1-32). De Gruyter. https://doi.org/10.1515/9783110499001-001

Nonlinear optimization strategies for process separations and process intensification

Biegler, L.T. (2020). Nonlinear optimization strategies for process separations and process intensification. Chemie Ingenieur Technik, 92(7), 867-878. https://doi.org/10.1002/cite.202000014

Nonlinear model predictive control of the hydraulic fracturing process

Lin, K.-H., Eason, J. P., Yu, Z. & Biegler, L. T. (2020). Nonlinear model predictive control of the hydraulic fracturing process. IFAC-PapersOnLine, 53(2), 11428-11433. https://doi.org/10.1016/j.ifacol.2020.12.579

Sensitivity-assisted multistage nonlinear model predictive control with online scenario adaptation.

Thombre, M., Yu, Z., Jaeschke, J., & Biegler, L.T. (2021). Sensitivity-assisted multistage nonlinear model predictive control with online scenario adaptation. Computers and Chemical Engineering, 148. https://doi.org/10.1016/j.compchemeng.2021.107269

A framework for the optimization of chemical looping combustion processes

Okoli, C. O., Ostace, A., Nadgouda, S., Lee, A., Tong, A., Burgard, A. P., Bhattacharyya, D., & Miller, D.C. (2020). A framework for the optimization of chemical looping combustion processes. Powder Technology, 365, 149-162. https://doi.org/10.1016/j.powtec.2019.04.035

A multi-objective reactive distillation optimization model for Fischer–Tropsch synthesis

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

Dynamic optimization of natural gas pipeline networks with demand and composition uncertainty

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

Dynamic optimization for gas blending in pipeline networks with gas interchangeability control

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
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  • Book/Book Chapter
  • Journal Articles/Technical Reports

An overview of process intensification methods

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

Parallel cyclic reduction decomposition for dynamic optimization problems

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

Modern modeling paradigms using generalized disjunctive programming

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

Optimization-based design of active and stable nanostructured surfaces

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

Nonlinear programming formulations for nonlinear and economic model predictive control

Yu, M., Griffith, D. W., & Biegler, L. T. (2019). Nonlinear programming formulations for nonlinear and economic model predictive control. In Raković, S., Levine, W. (Eds.) Handbook of Model Predictive Control. Control Engineering. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-77489-3_20

Automated learning of chemical reaction networks

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

Optimization opportunities in product development: perspective from a manufacturing company

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

PARMEST: Parameter estimation via Pyomo

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

Dynamic optimization of natural gas network with rigorous thermodynamics under uncertainty

Liu, K., Biegler, L. T., Zhang, B. & Chen Q. (2019). Dynamic optimization of natural gas network with rigorous thermodynamics under uncertainty. 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, (pp. 317-322). https://doi.org/10.1016/B978-0-12-818597-1.50050-3

A framework for optimizing oxygen vacancy formation in doped perovskites

Hanselman, C. L., Tafen, D. Y., Alfonso, D. R., Lekse, J. W., Matranga, C., Miller, D. C., & Gounaris, C.E. (2019). A framework for optimizing oxygen vacancy formation in doped perovskites. Computers & Chemical Engineering, 126, 168-177. https://doi.org/10.1016/j.compchemeng.2019.03.033

Effective Generalized Disjunctive Program optimization models for modular process synthesis

Chen, Q. & Grossmann, I.E. (2019). Effective Generalized Disjunctive Program optimization models for modular process synthesis. Ind. Eng. Chem. Res. 58(15), 5873-5886. https://doi.org/10.1021/acs.iecr.8b04600

Kaibel column: Modeling, optimization, and conceptual design of multi-product dividing wall columns.

Rawlings, E.S., Chen, Q., Grossmann, I. E., & Caballero, J.A. (2019). Kaibel column: Modeling, optimization, and conceptual design of multi-product dividing wall columns. Comp. Chem. Eng. 125, 31-39. https://doi.org/10.1016/j.compchemeng.2019.03.006

Dynamic real-time optimization for a CO2 capture process

Thierry, D. M., & Biegler, L. T. (2019). Dynamic real-time optimization for a CO2 capture process. AIChE Journal, 65(7) 1-11. https://doi.org/10.1002/aic.16511
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  • Book/Book Chapter
  • Journal Articles/Technical Reports

Large-scale optimization formulations and strategies for nonlinear model predictive control

Biegler, L. T., & Thierry, D. M. (2018). Large-scale optimization formulations and strategies for nonlinear model predictive control. IFAC-Papers OnLine, 51(20), 1–15. https://doi.org/10.1016/j.ifacol.2018.10.167

Benchmarking ADMM in nonconvex NLPs

Rodriguez, J.S., Nicholson, B., Laird, C.D., & Zavala, V.M. (2018). Benchmarking ADMM in nonconvex NLPs. Computers & Chemical Engineering, 119, 315-325. https://doi.org/10.1016/j.compchemeng.2018.08.036

GPU parameter tuning for tall and skinny dense linear least squares problems

Sauk, B., Ploskas, N., & Sahinidis, N. (2018). GPU parameter tuning for tall and skinny dense linear least squares problems. Optimization Methods and Software, 35(3), 638-660. https://doi.org/10.1080/10556788.2018.1527331

Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm

Lara, C.L., Mallapragada, D., Papageorgiou D., Venkatesh, A., & Grossmann, I. E. (2018). Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm. European Journal of Operational Research, 271(3), 1037-1054. https://doi.org/10.1016/j.ejor.2018.05.039

A framework for modeling and optimizing dynamic systems under uncertainty

Nicholson, B.L., & Siirola, J. D. (2018). A framework for modeling and optimizing dynamic systems under uncertainty. Computers & Chemical Engineering, 114, 81-88. https://doi.org/10.1016/j.compchemeng.2017.11.003

Effective GDP optimization models for modular process synthesis

Chen, Q., & Grossmann, I.E. (2018). Effective GDP optimization models for modular process synthesis. Current Opinion in Chemical Engineering. http://egon.cheme.cmu.edu/Papers/Chen_Qi_modular_updated.pdf

Pyomo.dae: A modeling and automatic discretization framework for optimization with differential and algebraic equations

Nicholson, B.L., Siirola, J. D., Watson, J.-P., Zavala, V. M., & Biegler, L.T. (2018). Pyomo.dae: A modeling and automatic discretization framework for optimization with differential and algebraic equations. Math Programming Computation, 10, 187-223. https://doi.org/10.1007/s12532-017-0127-0
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  • Journal Articles/Technical Reports

A Framework for Modeling and Optimizing Dynamic Systems Under Uncertainty

Nicholson, B.L. and J.D. Siirola, A Framework for Modeling and Optimizing Dynamic Systems Under Uncertainty. In press, special FOCAPO/CPC issue of Computers & Chemical Engineering (2017)
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  • Book/Book Chapter
  • Journal Articles/Technical Reports

A mathematical optimization framework for the design of nanopatterned surfaces

Hanselman, C. L., & Gounaris, C. E. (2016). A mathematical optimization framework for the design of nanopatterned surfaces. AIChE Journal, 62(9), 3250-3263. https://doi.org/10.1002/aic.15359
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  • Conference Paper
  • Conference Presentation

Optimization of process families for improved deployment of industrial decarbonization processes using machine learning surrogates

Stinchfield, G., Ammari, B., Morgan, J. C., Siirola, J. D., Zamarripa, M. A., & Laird, C. D. (2023). Optimization of process families for improved deployment of industrial decarbonization processes using machine learning surrogates. In Kokossis, A., Georgiadis, M., & Pistikopoulos, S. (Eds.), 33rd European Symposium on Computer Aided Process Engineering. Elsevier.

Multi-scale modeling using IDAES

Burgard, A., Zitney, S, & Omell, B. (2023, April 18-20). Multi-scale modeling using IDAES [Conference presentation]. 2023 FECM / NETL Spring R&D Project Review Meeting, Pittsburgh, PA. https://www.osti.gov/biblio/1971246

Optimal design approaches for rapid, cost-effective manufacturing and deployment of chemical processes

Stinchfield, G., Morgan, J.C., Zamarripa, M., & Laird, C. D. (2023, April 18-20). Optimal design approaches for rapid, cost-effective manufacturing and deployment of chemical processes [Poster presentation]. 2023 FECM / NETL Spring R&D Project Review Meeting, Pittsburgh, PA.

NMPC for setpoint tracking operation of a solid oxide electrolysis cell system

Allan, D. A., Dabadghao, V., Li, M., Eslick, J. C., Ma, J., Bhattacharyya, D., Zitney, S. E., and Biegler, L. T. (2023, January 8-12). NMPC for setpoint tracking operation of a solid oxide electrolysis cell system [Paper presentation]. Foundations of Computer Aided Process Operations / Chemical Process Control (FOCAPO/CPC 2023), San Antonio, TX. https://www.osti.gov/biblio/1964151

Technoeconomic analysis and optimization of low carbon, reforming-based integrated energy systems for the co-production of hydrogen and power

Wang, M., Ma, J., Lewis, E., Myles, P., Brewer, J., Keairns, D., Burgard, A., & Miller, D. (2022, November 13-18). Technoeconomic analysis and optimization of low carbon, reforming-based integrated energy systems for the co-production of hydrogen and power [Conference presentation]. 2022 AICHE Annual Meeting, Phoenix, AZ.

Multiscale design, operations and control optimization of integrated energy systems considering energy market interactions

Dowling, A. W. (2022, November 9). Multiscale design, operations and control optimization of integrated energy systems considering energy market interactions [Conference presentation]. Chemical and Biological Engineering Department Fall Seminar, Illinois Institute of Technology, Chicago, IL.

Multiscale design, operations and control optimization of integrated energy systems considering energy market interactions

Dowling, A. W. (2022, October 27). Multiscale design, operations and control optimization of integrated energy systems considering energy market interactions [Conference presentation]. Instituto Tecnológico de Celaya Graduate Seminar, Virtual.

Optimal design formulations for families of similar processes with applications in energy systems

Stinchfield, G., Zamarripa, M.A., Siirola, J.D., Laird, C.D. (2022, October 16). Optimal design formulations for families of similar processes with applications in energy systems [Conference presentation]. INFORMs Annual Meeting, Indianapolis, IN.

An optimization model for expansion planning of reliable power generation systems

Cho, S. & Grossmann, I.E. (2022). An optimization model for expansion planning of reliable power generation systems. In L. Montastruc & S. Negny (Eds.), 32nd European symposium on Computer Aided Process Engineering (ESCAPE32), Computer-Aided Chemical Engineering, 51 (pp 841-846). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50141-7

An optimization model for expansion planning of reliable power generation systems

Cho, S. & Grossmann, I. E. (2022). An optimization model for the design and operation of reliable power generation systems. In Y. Yamashita & M. Kano (Eds.), 14th International Symposium on Process Systems Engineering (PSE2021+), Computer Aided Chemical Engineering, 49 (pp. 709-714). Elsevier. https://doi.org/10.1016/B978-0-323-85159-6.50118-4

Estimating energy market schedules using historical price data

Cortes, N.P., Gao, X., Knueven, B. & Dowling, A.W. (2022) Estimating energy market schedules using historical price data. In Y. Yamashita & M. Kano (Eds.), 14th International Symposium on Process Systems Engineering (PSE2021+), Computer Aided Chemical Engineering, 49 (pp. 517-522). Elsevier. https://doi.org/10.1016/B978-0-323-85159-6.50086-5

Co-optimizing the design and operation strategy of solid oxide fuel cell-based hydrogen-electricity co-production systems

Cortes, N.C., Eslick, J. C., Noring, A., Susarla, N., Okoli, C. O., Zamarripa-Perez, M. A., Iyengar, A., Burgard, A. P., Miller, D. C., Allan, D., & Dowling., A. W. (2022, July 25-28). Co-optimizing the design and operation strategy of solid oxide fuel cell-based hydrogen-electricity co-production systems [Poster presentation]. The International Conference on Continuous Optimization (ICCOPT), Lehigh University, Bethlehem, PA.

A multi-scale modeling paradigm for energy system operation and design

Gao, X., & Dowling, A. W. (2021, November 7-11). A multi-scale modeling paradigm for energy system operation and design [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/182e-ow-multi-scale-modeling-for-energy-paradigm-energy-system-operation-and-design

Design centering through derivative-free optimization

Zhao, F., Grossmann, I. E., Garcia-Munoz, S., & Stamatis, S. D. (2021, November 7-11). Design centering through derivative-free optimization [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA.

Diagnostic tools for nonlinear algebraic models of dynamic chemical processes in Pyomo.Dae

Parker, R., Nicholson, B., Siirola, J., & Biegler L. T. (2021, November 7-11). Diagnostic tools for nonlinear algebraic models of dynamic chemical processes in Pyomo.Dae [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.osti.gov/servlets/purl/1897361

Nonlinear model predictive control simulations of gas-solid reactors for chemical looping combustion of methane

Parker, R., & Biegler, L. T. (2021, November 7-11). Nonlinear model predictive control simulations of gas-solid reactors for chemical looping combustion of methane [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/492f-nonlinear-model-predictive-control-simulations-gas-solid-reactors-chemical-looping-combustion

On representative day selection for capacity expansion planning of power systems under extreme events

Li, C., Conejo, A. J., Siirola, J., & Grossmann, I. E. (2021, November 7-11). On representative day selection for capacity expansion planning of power systems under extreme events [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/182c-on-representative-day-selection-capacity-expansion-planning-power-systems-under-extreme-events

Regularized subsets: A framework for high-dimensional linear regression with noisy data and optional constraints

Sarwar, O. & Sahinidis, N. V. (2021, November 7-11). Regularized subsets: A framework for high-dimensional linear regression with noisy data and optional constraints [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/415c-regularized-subsets-framework-high-dimensional-linear-regression-noisy-data-and-optional

Rate-based dynamic modeling and optimization of an amine-based carbon capture unit for flexible operation

Akula, P., Eslick, J., Bhattacharyya, D., & Miller, D. C. (2021, November 7-11). Rate-based dynamic modeling and optimization of an amine-based carbon capture unit for flexible operation [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/418g-rate-based-dynamic-modeling-and-optimization-amine-based-carbon-capture-unit-flexible-operation

Conceptual design via superstructure optimization in advanced energy systems using IDAES

Rawlings, E., Susarla, N., Ghouse, J., Laird, C., Zamarripa, M., Bynum, M., Siirola, J., & Miller, D. (2021, November 7-11). Conceptual design via superstructure optimization in advanced energy systems using IDAES [Conference presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://doi.org/10.2172/1899680

Toward future energy generation systems: Multi-scale optimization with market interactions

Jalving, J., Ghouse, J., Knueven, B., Siirola, J., Dowling, A. W., & Miller, D.C. (2021, November 7-11). Toward future energy generation systems: Multi-scale optimization with market interactions [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/418e-toward-future-energy-generation-systems-multi-scale-optimization-market-interactions

A multi-scale modeling paradigm for Integrated Energy System operation and design

Gao, X., & Dowling, A.W. (2021, November 7- 11). A multi-scale modeling paradigm for Integrated Energy System operation and design [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/29e-multi-scale-modeling-paradigm-energy-system-operation-and-design

Towards process-materials co-optimization: automatic generation of optimizable MOF structure-function relationships

Yin, X., & Gounaris, C. E. (2021, November 7-11). Towards process-materials co-optimization: automatic generation of optimizable MOF structure-function relationships [Conference presentation]. AIChE 2021 Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/582f-towards-process-materials-co-optimization-automatic-generation-optimizable-mof-structure

New features and comprehensive benchmarking study of the Pyomo robust optimization solver

Isenberg, N. M., Gounaris, C. E., & Siirola, J. D. (2021, November 7-11). New features and comprehensive benchmarking study of the Pyomo robust optimization solver. 2021 AIChE Annual Meeting, Boston, MA.

Optimization workflows to quantify the resource-grid interactions in wholesale energy markets

Gao, X., Cortes, N., & Dowling, A.W. (2021, October 24-27). Optimization workflows to quantify the resource-grid interactions in wholesale energy markets [Conference presentation]. INFORMS Annual Meeting, Anaheim, CA. https://www.informs.org/academy/conferences/informs-annual-meeting/2021/proceeding/paper/182r-or-workflows-to-quantify-resource-grid-resource-interactions-in-wholesale-energy-markets

A comprehensive performance study of the Pyomo Robust Optimization Solver (PyROS)

Isenberg, N. M., Siirola, J. D., & Gounaris, C. E. (2021, October 24-27). A comprehensive performance study of the Pyomo Robust Optimization Solver (PyROS) [Conference presentation]. 2021 INFORMS Annual Meeting, Anaheim, CA.

IES task 1.0: Design of flexible dynamic energy systems

Bynum, M., Deshpande, A., Siirola, J., Zitney, S.E., & Grossmann, I. (2021, October 14). IES task 1.0: Design of flexible dynamic energy systems [Conference presentation.] Stakeholder Meeting, Virtual. https://www.conference.org/academy/conferences/conference-presentation-stakeholder/2021/proceeding/paper/182f-es-task-Design-of-dynamic-energy-task-of-flexible-dynamic-energy-systems

IDAES enterprise: Generation expansion planning with enhanced requirements for capacity adequacy considering renewable intermittency

Liu, P., Omell, B., Li, C., & Grossmann, I. (2021, July 26-29). IDAES enterprise: Generation expansion planning with enhanced requirements for capacity adequacy considering renewable intermittency [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

Formulation and parameter estimation of chemical looping reaction kinetic models using IDAES

Okoli, C., Ostace, A., Lee, A., Biegler, L. T., Parker, R., Tong, A., Chen, Y.-Y., Bhattacharyya, D., Burgard, A. P., & Miller, D. C. (2021, July 26-29). Formulation and parameter estimation of chemical looping reaction kinetic models using IDAES [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

IDAES dynamic power plant unit model library: Analyzing boiler health during cycling operations

Zamarripa, M., Ma, J., Minh, Q., Bhattacharyya, D., Eslick, J., Wang, M., Zitney, S. E., Burgard, A., & Miller, D. C. (2021, July 26-29). IDAES dynamic power plant unit model library: Analyzing boiler health during cycling operations [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

Conceptual design of molten salt storage system for super-critical power plants

Susarla, N., Ghouse, J., Zamarripa, M., Rawlings, E. S., Laird, C., Bynum, M., Siirola, J., & Miller, D. C. (2021, July 26-29). Conceptual design of molten salt storage system for super-critical power plants [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

Optimizing the dynamic, multi-scale energy systems of the future

Miller, D. C. (2021, July 26-29). Optimizing the dynamic, multi-scale energy systems of the future [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

Advanced-multi-step Moving Horizon Estimation

Biegler, L. T., Kim, Y., Lin, K.-H., & Thierry, D. M. (2021, June 13-16). Advanced-multi-step Moving Horizon Estimation [Conference presentation]. 11th IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM 2021), Virtual.

Nonlinear programming strategies for optimization of dynamic chemical looping reactor models

Parker, R., & Biegler, L. T. (2020, November 16-20). Nonlinear programming strategies for optimization of dynamic chemical looping reactor models [Paper presentation]. 2020 AIChE Annual, Virtual. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2020/proceeding/paper/596a-nonlinear-programming-strategies-optimization-dynamic-chemical-looping-reactor-models

Dynamic optimization of natural gas network with rigorous thermodynamics under uncertainty

Liu, K., Zhang, B. & Chen, Q. (2019, July 14-18). Dynamic optimization of natural gas network with rigorous thermodynamics under uncertainty [Conference presentation]. 9 Intl Conference on Foundations of Computer-Aided Process Design, Copper Mountain, CO.
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  • Conference Paper
  • Conference Presentation

Optimization of process families for improved deployment of industrial decarbonization processes using machine learning surrogates

Stinchfield, G., Ammari, B., Morgan, J. C., Siirola, J. D., Zamarripa, M. A., & Laird, C. D. (2023). Optimization of process families for improved deployment of industrial decarbonization processes using machine learning surrogates. In Kokossis, A., Georgiadis, M., & Pistikopoulos, S. (Eds.), 33rd European Symposium on Computer Aided Process Engineering. Elsevier.

Multi-scale modeling using IDAES

Burgard, A., Zitney, S, & Omell, B. (2023, April 18-20). Multi-scale modeling using IDAES [Conference presentation]. 2023 FECM / NETL Spring R&D Project Review Meeting, Pittsburgh, PA. https://www.osti.gov/biblio/1971246

Optimal design approaches for rapid, cost-effective manufacturing and deployment of chemical processes

Stinchfield, G., Morgan, J.C., Zamarripa, M., & Laird, C. D. (2023, April 18-20). Optimal design approaches for rapid, cost-effective manufacturing and deployment of chemical processes [Poster presentation]. 2023 FECM / NETL Spring R&D Project Review Meeting, Pittsburgh, PA.

NMPC for setpoint tracking operation of a solid oxide electrolysis cell system

Allan, D. A., Dabadghao, V., Li, M., Eslick, J. C., Ma, J., Bhattacharyya, D., Zitney, S. E., and Biegler, L. T. (2023, January 8-12). NMPC for setpoint tracking operation of a solid oxide electrolysis cell system [Paper presentation]. Foundations of Computer Aided Process Operations / Chemical Process Control (FOCAPO/CPC 2023), San Antonio, TX. https://www.osti.gov/biblio/1964151
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Technoeconomic analysis and optimization of low carbon, reforming-based integrated energy systems for the co-production of hydrogen and power

Wang, M., Ma, J., Lewis, E., Myles, P., Brewer, J., Keairns, D., Burgard, A., & Miller, D. (2022, November 13-18). Technoeconomic analysis and optimization of low carbon, reforming-based integrated energy systems for the co-production of hydrogen and power [Conference presentation]. 2022 AICHE Annual Meeting, Phoenix, AZ.

Multiscale design, operations and control optimization of integrated energy systems considering energy market interactions

Dowling, A. W. (2022, November 9). Multiscale design, operations and control optimization of integrated energy systems considering energy market interactions [Conference presentation]. Chemical and Biological Engineering Department Fall Seminar, Illinois Institute of Technology, Chicago, IL.

Multiscale design, operations and control optimization of integrated energy systems considering energy market interactions

Dowling, A. W. (2022, October 27). Multiscale design, operations and control optimization of integrated energy systems considering energy market interactions [Conference presentation]. Instituto Tecnológico de Celaya Graduate Seminar, Virtual.

Optimal design formulations for families of similar processes with applications in energy systems

Stinchfield, G., Zamarripa, M.A., Siirola, J.D., Laird, C.D. (2022, October 16). Optimal design formulations for families of similar processes with applications in energy systems [Conference presentation]. INFORMs Annual Meeting, Indianapolis, IN.

An optimization model for expansion planning of reliable power generation systems

Cho, S. & Grossmann, I.E. (2022). An optimization model for expansion planning of reliable power generation systems. In L. Montastruc & S. Negny (Eds.), 32nd European symposium on Computer Aided Process Engineering (ESCAPE32), Computer-Aided Chemical Engineering, 51 (pp 841-846). Elsevier. https://doi.org/10.1016/B978-0-323-95879-0.50141-7

An optimization model for expansion planning of reliable power generation systems

Cho, S. & Grossmann, I. E. (2022). An optimization model for the design and operation of reliable power generation systems. In Y. Yamashita & M. Kano (Eds.), 14th International Symposium on Process Systems Engineering (PSE2021+), Computer Aided Chemical Engineering, 49 (pp. 709-714). Elsevier. https://doi.org/10.1016/B978-0-323-85159-6.50118-4

Estimating energy market schedules using historical price data

Cortes, N.P., Gao, X., Knueven, B. & Dowling, A.W. (2022) Estimating energy market schedules using historical price data. In Y. Yamashita & M. Kano (Eds.), 14th International Symposium on Process Systems Engineering (PSE2021+), Computer Aided Chemical Engineering, 49 (pp. 517-522). Elsevier. https://doi.org/10.1016/B978-0-323-85159-6.50086-5

Co-optimizing the design and operation strategy of solid oxide fuel cell-based hydrogen-electricity co-production systems

Cortes, N.C., Eslick, J. C., Noring, A., Susarla, N., Okoli, C. O., Zamarripa-Perez, M. A., Iyengar, A., Burgard, A. P., Miller, D. C., Allan, D., & Dowling., A. W. (2022, July 25-28). Co-optimizing the design and operation strategy of solid oxide fuel cell-based hydrogen-electricity co-production systems [Poster presentation]. The International Conference on Continuous Optimization (ICCOPT), Lehigh University, Bethlehem, PA.
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A multi-scale modeling paradigm for energy system operation and design

Gao, X., & Dowling, A. W. (2021, November 7-11). A multi-scale modeling paradigm for energy system operation and design [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/182e-ow-multi-scale-modeling-for-energy-paradigm-energy-system-operation-and-design

Design centering through derivative-free optimization

Zhao, F., Grossmann, I. E., Garcia-Munoz, S., & Stamatis, S. D. (2021, November 7-11). Design centering through derivative-free optimization [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA.

Diagnostic tools for nonlinear algebraic models of dynamic chemical processes in Pyomo.Dae

Parker, R., Nicholson, B., Siirola, J., & Biegler L. T. (2021, November 7-11). Diagnostic tools for nonlinear algebraic models of dynamic chemical processes in Pyomo.Dae [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.osti.gov/servlets/purl/1897361

Nonlinear model predictive control simulations of gas-solid reactors for chemical looping combustion of methane

Parker, R., & Biegler, L. T. (2021, November 7-11). Nonlinear model predictive control simulations of gas-solid reactors for chemical looping combustion of methane [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/492f-nonlinear-model-predictive-control-simulations-gas-solid-reactors-chemical-looping-combustion

On representative day selection for capacity expansion planning of power systems under extreme events

Li, C., Conejo, A. J., Siirola, J., & Grossmann, I. E. (2021, November 7-11). On representative day selection for capacity expansion planning of power systems under extreme events [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/182c-on-representative-day-selection-capacity-expansion-planning-power-systems-under-extreme-events

Regularized subsets: A framework for high-dimensional linear regression with noisy data and optional constraints

Sarwar, O. & Sahinidis, N. V. (2021, November 7-11). Regularized subsets: A framework for high-dimensional linear regression with noisy data and optional constraints [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/415c-regularized-subsets-framework-high-dimensional-linear-regression-noisy-data-and-optional

Rate-based dynamic modeling and optimization of an amine-based carbon capture unit for flexible operation

Akula, P., Eslick, J., Bhattacharyya, D., & Miller, D. C. (2021, November 7-11). Rate-based dynamic modeling and optimization of an amine-based carbon capture unit for flexible operation [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/418g-rate-based-dynamic-modeling-and-optimization-amine-based-carbon-capture-unit-flexible-operation

Conceptual design via superstructure optimization in advanced energy systems using IDAES

Rawlings, E., Susarla, N., Ghouse, J., Laird, C., Zamarripa, M., Bynum, M., Siirola, J., & Miller, D. (2021, November 7-11). Conceptual design via superstructure optimization in advanced energy systems using IDAES [Conference presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://doi.org/10.2172/1899680

Toward future energy generation systems: Multi-scale optimization with market interactions

Jalving, J., Ghouse, J., Knueven, B., Siirola, J., Dowling, A. W., & Miller, D.C. (2021, November 7-11). Toward future energy generation systems: Multi-scale optimization with market interactions [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/418e-toward-future-energy-generation-systems-multi-scale-optimization-market-interactions

A multi-scale modeling paradigm for Integrated Energy System operation and design

Gao, X., & Dowling, A.W. (2021, November 7- 11). A multi-scale modeling paradigm for Integrated Energy System operation and design [Paper presentation]. 2021 AIChE Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/29e-multi-scale-modeling-paradigm-energy-system-operation-and-design

Towards process-materials co-optimization: automatic generation of optimizable MOF structure-function relationships

Yin, X., & Gounaris, C. E. (2021, November 7-11). Towards process-materials co-optimization: automatic generation of optimizable MOF structure-function relationships [Conference presentation]. AIChE 2021 Annual Meeting, Boston, MA. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2021/proceeding/paper/582f-towards-process-materials-co-optimization-automatic-generation-optimizable-mof-structure

New features and comprehensive benchmarking study of the Pyomo robust optimization solver

Isenberg, N. M., Gounaris, C. E., & Siirola, J. D. (2021, November 7-11). New features and comprehensive benchmarking study of the Pyomo robust optimization solver. 2021 AIChE Annual Meeting, Boston, MA.

Optimization workflows to quantify the resource-grid interactions in wholesale energy markets

Gao, X., Cortes, N., & Dowling, A.W. (2021, October 24-27). Optimization workflows to quantify the resource-grid interactions in wholesale energy markets [Conference presentation]. INFORMS Annual Meeting, Anaheim, CA. https://www.informs.org/academy/conferences/informs-annual-meeting/2021/proceeding/paper/182r-or-workflows-to-quantify-resource-grid-resource-interactions-in-wholesale-energy-markets

A comprehensive performance study of the Pyomo Robust Optimization Solver (PyROS)

Isenberg, N. M., Siirola, J. D., & Gounaris, C. E. (2021, October 24-27). A comprehensive performance study of the Pyomo Robust Optimization Solver (PyROS) [Conference presentation]. 2021 INFORMS Annual Meeting, Anaheim, CA.

IES task 1.0: Design of flexible dynamic energy systems

Bynum, M., Deshpande, A., Siirola, J., Zitney, S.E., & Grossmann, I. (2021, October 14). IES task 1.0: Design of flexible dynamic energy systems [Conference presentation.] Stakeholder Meeting, Virtual. https://www.conference.org/academy/conferences/conference-presentation-stakeholder/2021/proceeding/paper/182f-es-task-Design-of-dynamic-energy-task-of-flexible-dynamic-energy-systems

IDAES enterprise: Generation expansion planning with enhanced requirements for capacity adequacy considering renewable intermittency

Liu, P., Omell, B., Li, C., & Grossmann, I. (2021, July 26-29). IDAES enterprise: Generation expansion planning with enhanced requirements for capacity adequacy considering renewable intermittency [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

Formulation and parameter estimation of chemical looping reaction kinetic models using IDAES

Okoli, C., Ostace, A., Lee, A., Biegler, L. T., Parker, R., Tong, A., Chen, Y.-Y., Bhattacharyya, D., Burgard, A. P., & Miller, D. C. (2021, July 26-29). Formulation and parameter estimation of chemical looping reaction kinetic models using IDAES [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

IDAES dynamic power plant unit model library: Analyzing boiler health during cycling operations

Zamarripa, M., Ma, J., Minh, Q., Bhattacharyya, D., Eslick, J., Wang, M., Zitney, S. E., Burgard, A., & Miller, D. C. (2021, July 26-29). IDAES dynamic power plant unit model library: Analyzing boiler health during cycling operations [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

Conceptual design of molten salt storage system for super-critical power plants

Susarla, N., Ghouse, J., Zamarripa, M., Rawlings, E. S., Laird, C., Bynum, M., Siirola, J., & Miller, D. C. (2021, July 26-29). Conceptual design of molten salt storage system for super-critical power plants [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

Optimizing the dynamic, multi-scale energy systems of the future

Miller, D. C. (2021, July 26-29). Optimizing the dynamic, multi-scale energy systems of the future [Conference presentation]. The 45th International Technical Conference on Clean Energy, Clearwater, FL.

Advanced-multi-step Moving Horizon Estimation

Biegler, L. T., Kim, Y., Lin, K.-H., & Thierry, D. M. (2021, June 13-16). Advanced-multi-step Moving Horizon Estimation [Conference presentation]. 11th IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM 2021), Virtual.
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Nonlinear programming strategies for optimization of dynamic chemical looping reactor models

Parker, R., & Biegler, L. T. (2020, November 16-20). Nonlinear programming strategies for optimization of dynamic chemical looping reactor models [Paper presentation]. 2020 AIChE Annual, Virtual. https://www.aiche.org/academy/conferences/aiche-annual-meeting/2020/proceeding/paper/596a-nonlinear-programming-strategies-optimization-dynamic-chemical-looping-reactor-models
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Dynamic optimization of natural gas network with rigorous thermodynamics under uncertainty

Liu, K., Zhang, B. & Chen, Q. (2019, July 14-18). Dynamic optimization of natural gas network with rigorous thermodynamics under uncertainty [Conference presentation]. 9 Intl Conference on Foundations of Computer-Aided Process Design, Copper Mountain, CO.
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