Publications
To cite IDAES, please use this reference:
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
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- Book/Book Chapter
- Journal Articles/Technical Reports
Linear Model Decision Trees as Surrogates in Optimization of Engineering Applications
Ammari, B.L., E.S. Johnson, G. Stinchfield, T.Kim, M. Bynum, W.E. Hart, J. Pulsipher, C.D. Laird, “Linear Model Decision Trees as Surrogates in Optimization of Engineering Applications”, Computers and Chemical Engineering, Volume 178, October 2023, 108347.
Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates
Jalving, J., Ghouse, J., Cortes, N., Gao, X., Knueven, B., Agi, D., Martin, S., Chen, X., Guittet, D., Tumbalam-Gooty, R., Bianchi, L., Beattie, K., Gunter, D., Siirola, J.D., Miller, D.C., Dowling, A.W. Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates. Applied Energy. 2023. 351, 121767.
A Mixed Integer Linear Programming Approach for the Design of Chemical Process Families
Stinchfield, G., J.C Morgan, S. Naik, L.T. Biegler, J.C. Eslick, C. Jacobson, D.C. Miller, J.D. Siirola, M.A. Zamarripa, C. Zhang, Q. Zhang, C.D. Laird, “A Mixed Integer Linear Programming Approach for the Design of Chemical Process Families”. Submitted to Computers and Chemical Engineering, FOCAPO/CPC23 Invited Special Issue.
Market Optimization and Technoeconomic Analysis of Hydrogen-Electricity Co-production Systems
Cortes, N.P., Eslick, J.C., Noring, A., Susarla, N., Okoli, C., Zamarripa-Perez, M.A., Allan, D., Brewer, J.H., Iyengar, A.K.S., Burgard, A.P., Miller, D.C., Dowling, A.W. Market Optimization and Technoeconomic Analysis of Hydrogen-Electricity Co-production Systems. Submitted.
Optimization of Process Families for Improved Deployment of Industrial Decarbonization Processes using Machine Learning Surrogates
Stinchfield, G., B.L., Ammari, J. Morgan, J. Siirola, M. Zamarripa, C.D. Laird, “Optimization of Process Families for Improved Deployment of Industrial Decarbonization Processes using Machine Learning Surrogates,” Computer Aided Chemical Engineering, Volume 52, 2023, 1331-1337.
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
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- Book/Book Chapter
- Journal Articles/Technical Reports
Linear Model Decision Trees as Surrogates in Optimization of Engineering Applications
Ammari, B.L., E.S. Johnson, G. Stinchfield, T.Kim, M. Bynum, W.E. Hart, J. Pulsipher, C.D. Laird, “Linear Model Decision Trees as Surrogates in Optimization of Engineering Applications”, Computers and Chemical Engineering, Volume 178, October 2023, 108347.
Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates
Jalving, J., Ghouse, J., Cortes, N., Gao, X., Knueven, B., Agi, D., Martin, S., Chen, X., Guittet, D., Tumbalam-Gooty, R., Bianchi, L., Beattie, K., Gunter, D., Siirola, J.D., Miller, D.C., Dowling, A.W. Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates. Applied Energy. 2023. 351, 121767.
A Mixed Integer Linear Programming Approach for the Design of Chemical Process Families
Stinchfield, G., J.C Morgan, S. Naik, L.T. Biegler, J.C. Eslick, C. Jacobson, D.C. Miller, J.D. Siirola, M.A. Zamarripa, C. Zhang, Q. Zhang, C.D. Laird, “A Mixed Integer Linear Programming Approach for the Design of Chemical Process Families”. Submitted to Computers and Chemical Engineering, FOCAPO/CPC23 Invited Special Issue.
Market Optimization and Technoeconomic Analysis of Hydrogen-Electricity Co-production Systems
Cortes, N.P., Eslick, J.C., Noring, A., Susarla, N., Okoli, C., Zamarripa-Perez, M.A., Allan, D., Brewer, J.H., Iyengar, A.K.S., Burgard, A.P., Miller, D.C., Dowling, A.W. Market Optimization and Technoeconomic Analysis of Hydrogen-Electricity Co-production Systems. Submitted.
Optimization of Process Families for Improved Deployment of Industrial Decarbonization Processes using Machine Learning Surrogates
Stinchfield, G., B.L., Ammari, J. Morgan, J. Siirola, M. Zamarripa, C.D. Laird, “Optimization of Process Families for Improved Deployment of Industrial Decarbonization Processes using Machine Learning Surrogates,” Computer Aided Chemical Engineering, Volume 52, 2023, 1331-1337.
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
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- 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
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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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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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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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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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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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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
An Optimization Formulation for Designing Process Families with Cost Savings from Economies of Numbers
Stinchfield, S. Jan, J.C. Morgan, M.A. Zamarripa, C.D. Laird. “An Optimization Formulation for Designing Process Families with Cost Savings from Economies of Numbers”, Abstract accepted to Foundations of Computer Aided Process System Design (FOCAPD), 2024.
Progressive Hedging Decomposition for Solutions of Large-Scale Process Family Design Problems
Stinchfield, J.P. Watson, Carl D. Laird, “Progressive Hedging Decomposition for Solutions of Large-Scale Process Family Design Problems”, Abstract accepted to the joint conference of the European Symposium on Computer Aided Process Engineering (ESCAPE) and Process Systems Engineering (PSE) 2024.
Economies of Numbers Formulations for Optimal Process Family Design of Carbon Capture Systems
Stinchfield, J.C. Morgan, M.A. Zamarripa, C.D. Laird. “Economies of Numbers Formulations for Optimal Process Family Design of Carbon Capture Systems.” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Optimal Operation of Solid Oxide Electrolysis Cell Considering Long Term Physical and Chemical Degradation and System Performance
Giridhar, N., Q.M. Le, D. Bhattacharyya, D.A. Allan, and S.E. Zitney, “Optimal Operation of Solid Oxide Electrolysis Cell Considering Long Term Physical and Chemical Degradation and System Performance,” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Dynamic Modeling of the Synergistic Effects of Chemical and Thermo-Mechanical Degradation of Solid Oxide Cells
Giridhar, N., Q.M. Le, D. Bhattacharyya, D.A. Allan, and S.E. Zitney, “Dynamic Modeling of the Synergistic Effects of Chemical and Thermo-Mechanical Degradation of Solid Oxide Cells,” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Optimization of Infrastructure Planning of Reliable and Carbon-neutral Power Systems: Application to San Diego County
Cho, S., J. Tovar-Facio, I.E. Grossmann, B.P. Omell, C.D. McLean, R. Tumbalam-Gooty, P.A. Tominac, A.P. Burgard, J.D. Siirola, J. Shinn, “Optimization of Infrastructure Planning of Reliable and Carbon-neutral Power Systems: Application to San Diego County,” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023
Augmented Control Using Reinforcement Learning and Conventional Process Control
Beahr, D., D. Bhattacharyya, D.A. Allan, and S.E. Zitney, “Augmented Control Using Reinforcement Learning and Conventional Process Control,” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Mixed-Integer Programming Representations of Linear Model Decision Tree Surrogates
L. Ammari, E.S. Johnson, G. Stinchfield, T. Kim, M. Bynum, W.E. Hart, J. Pulsipher, C.D. Laird, “Mixed-Integer Programming Representations of Linear Model Decision Tree Surrogates”, Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Learning to Branch with Interpretable Machine Learning Models
Bayramoglu, S., G. Nemhauser, and N. Sahinidis, “Learning to Branch with Interpretable Machine Learning Models”, 2023 INFORMS Annual Meeting, Phoenix, AZ, October 15-18, 2023.
Multi-scale Modeling and AI/ML Tools for the Optimization of Advanced Energy Systems
S. Zitney, “Multi-scale Modeling and AI/ML Tools for the Optimization of Advanced Energy Systems”, SAMI Tech Talk, Aug 16, 2023.
An Optimization Formulation for Designing Process Families with Cost Savings from Economies of Numbers
Stinchfield, S. Jan, J.C. Morgan, M.A. Zamarripa, C.D. Laird. “An Optimization Formulation for Designing Process Families with Cost Savings from Economies of Numbers”, Abstract accepted to Foundations of Computer Aided Process System Design (FOCAPD), 2024.
Progressive Hedging Decomposition for Solutions of Large-Scale Process Family Design Problems
Stinchfield, J.P. Watson, Carl D. Laird, “Progressive Hedging Decomposition for Solutions of Large-Scale Process Family Design Problems”, Abstract accepted to the joint conference of the European Symposium on Computer Aided Process Engineering (ESCAPE) and Process Systems Engineering (PSE) 2024.
Economies of Numbers Formulations for Optimal Process Family Design of Carbon Capture Systems
Stinchfield, J.C. Morgan, M.A. Zamarripa, C.D. Laird. “Economies of Numbers Formulations for Optimal Process Family Design of Carbon Capture Systems.” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Optimal Operation of Solid Oxide Electrolysis Cell Considering Long Term Physical and Chemical Degradation and System Performance
Giridhar, N., Q.M. Le, D. Bhattacharyya, D.A. Allan, and S.E. Zitney, “Optimal Operation of Solid Oxide Electrolysis Cell Considering Long Term Physical and Chemical Degradation and System Performance,” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Dynamic Modeling of the Synergistic Effects of Chemical and Thermo-Mechanical Degradation of Solid Oxide Cells
Giridhar, N., Q.M. Le, D. Bhattacharyya, D.A. Allan, and S.E. Zitney, “Dynamic Modeling of the Synergistic Effects of Chemical and Thermo-Mechanical Degradation of Solid Oxide Cells,” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Optimization of Infrastructure Planning of Reliable and Carbon-neutral Power Systems: Application to San Diego County
Cho, S., J. Tovar-Facio, I.E. Grossmann, B.P. Omell, C.D. McLean, R. Tumbalam-Gooty, P.A. Tominac, A.P. Burgard, J.D. Siirola, J. Shinn, “Optimization of Infrastructure Planning of Reliable and Carbon-neutral Power Systems: Application to San Diego County,” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023
Augmented Control Using Reinforcement Learning and Conventional Process Control
Beahr, D., D. Bhattacharyya, D.A. Allan, and S.E. Zitney, “Augmented Control Using Reinforcement Learning and Conventional Process Control,” Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Mixed-Integer Programming Representations of Linear Model Decision Tree Surrogates
L. Ammari, E.S. Johnson, G. Stinchfield, T. Kim, M. Bynum, W.E. Hart, J. Pulsipher, C.D. Laird, “Mixed-Integer Programming Representations of Linear Model Decision Tree Surrogates”, Abstract accepted to AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Learning to Branch with Interpretable Machine Learning Models
Bayramoglu, S., G. Nemhauser, and N. Sahinidis, “Learning to Branch with Interpretable Machine Learning Models”, 2023 INFORMS Annual Meeting, Phoenix, AZ, October 15-18, 2023.
Multi-scale Modeling and AI/ML Tools for the Optimization of Advanced Energy Systems
S. Zitney, “Multi-scale Modeling and AI/ML Tools for the Optimization of Advanced Energy Systems”, SAMI Tech Talk, Aug 16, 2023.
Optimization model for expansion planning of reliable and resilient power systems
Cho, J. Tovar-Facio, I. E. Grossmann, “Optimization model for expansion planning of reliable and resilient power systems”, The 23rd Conference of the International Federation of Operational Research Societies (IFORS2023), Santiago, Chile, July 10-14, 2023.
Beyond Price Taker: Using Machine Learning Surrogate Models to Optimization Electricity and Hydrogen Co-Production in Wholesale Energy Markets
Dowling, A.W., Beyond Price Taker: Using Machine Learning Surrogate Models to Optimization Electricity and Hydrogen Co-Production in Wholesale Energy Markets, Institution of Chemical Engineers (IChemE) Invited Webinar, July 6, 2023
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.
Learning to Branch with Interpretable Machine Learning Models
Bayramoglu, S., G. Nemhauser, and N. Sahinidis, “Learning to Branch with Interpretable Machine Learning Models”, Poster presented at the 24th Conference on Integer Programming and Combinatorial Optimization (IPCO XXIV), Madison, Wisconsin, June 21-23, 2023.
Optimization of Process Families for Deployment of Carbon Capture Processes using Machine Learning Surrogates
Stinchfield, G., B.L. Ammari, J.C. Morgan, J.D. Siirola, M.A. Zamarripa, C.D. Laird, “Optimization of Process Families for Deployment of Carbon Capture Processes using Machine Learning Surrogates”, 33rd Annual European Symposium on Computer-Aided Process Engineering (ESCAPE), Athens, Greece, June 18-21, 2023. (Won the best presentation award from 200+ presentations.)
Market-Based Technoeconomic Optimization of Integrated Energy Systems that Co-Produce Hydrogen and Electricity, Market-Based Technoeconomic Optimization of Integrated Energy Systems that Co-Produce Hydrogen and Electricity
Dowling, A.W., “Market-Based Technoeconomic Optimization of Integrated Energy Systems that Co-Produce Hydrogen and Electricity, Market-Based Technoeconomic Optimization of Integrated Energy Systems that Co-Produce Hydrogen and Electricity”, Hydrogen Program Annual Merit Review, Arlington, VA, June 8, 2023.
Technoeconomic Evaluation of SOFC/SOEC-based Integrated Energy Systems for the Co-Production of Electricity and Hydrogen
Burgard, A.P., “Technoeconomic Evaluation of SOFC/SOEC-based Integrated Energy Systems for the Co-Production of Electricity and Hydrogen”, Poster shown at Hydrogen Program Annual Merit Review, Arlington, VA, June 8, 2023.
Optimization model for expansion planning of reliable and resilient power systems under extreme scenarios
Cho, I. E. Grossmann, “Optimization model for expansion planning of reliable and resilient power systems under extreme scenarios”, 11th World Congress of Chemical Engineering (WCCE11), Buenos Aires, Argentina, June 4-8, 2023.
Optimal Design Approaches for Rapid, Cost-Effective Manufacturing and Deployment of Chemical Processes
Stinchfield, G., J.C. Morgan, M. Zamarripa, C.D. Laird, “Optimal Design Approaches for Rapid, Cost-Effective Manufacturing and Deployment of Chemical Processes”, Poster presented at 2023 Fossil Energy and Carbon Management (FECM)/NETL Spring R&D Project Review Meeting, Pittsburgh, PA, April 18-20, 2023.
Physical and Chemical Degradation Modeling of Solid Oxide Cells
Giridhar, N., M. Le, D.A. Allan, D. Bhattacharyya, and S.E. Zitney, “Physical and Chemical Degradation Modeling of Solid Oxide Cells,” Poster presented at 2023 Fossil Energy and Carbon Management (FECM)/NETL Spring R&D Project Review Meeting, Pittsburgh, PA, April 18-20, 2023.
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
Multi-scale Modeling and Optimization Using IDAES
Burgard, A., E. Zitney, and B.P. Omell, “Multi-scale Modeling and Optimization Using IDAES,” 2023 Fossil Energy and Carbon Management (FECM)/NETL Spring R&D Project Review Meeting, Pittsburgh, PA, April 18-20, 2023.
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.