- All
- Book/Book Chapter
- Journal Article
- Technical Reports
- All
- Book/Book Chapter
- Journal Article
- 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 Article
- 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 Article
- 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 Article
- 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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- Book/Book Chapter
- Journal Article
- 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 Article
- 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 Article
- 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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- Book/Book Chapter
- Journal Article
- Technical Reports
Advanced PSE Stakeholder Summit and Technical Meeting
The Advanced Process Systems Engineering Stakeholder Summit is scheduled for Oct 11th -12th, 2023. The Technical Team Meeting will follow on October 13th, 2023,
Agenda (draft): Advanced PSE Stakeholder Summit I Agenda Event Address: The Westin Tysons Corner, 7801 Leesburg Pike, Falls Church, VA, 22043, (703) 893-1340
Register
Hotel Reservations
IDAES Integrated Platform 2.1.0 released
Today we’re announcing the 2.1.0 release of the IDAES Integrated Platform, this is the first update on the 2.X line of IDAES releases.
Highlights
New IDAES Examples Repository
Starting with this release, the IDAES examples are developed in the new IDAES/examples repository.Along with many content and usability improvements, the most significant changes are:
To install the examples, after installing IDAES, run: pip install idaes-examples
The idaes get-examples command, previously used for this, has been removed
The HTML version is now available at https://idaes-examples.readthedocs.io
The previous URL, https://idaes.github.io/examples-pse, will not be updated and may be removed at some point in the future
For more details, refer to the resources available at IDAES/examples.
Removal of Non-Functional Apps
A review of the code in the idaes/apps and idaes/models_extra folders was undertaken, and a number of tools were identified as being outdated or non-functional and no longer supported by their development teams. Due to this, the following tools have been removed:
idaes/apps/alamopy_depr (note that the new ALAMOpy interface remains avaialble in idaes/core/surrogates)
idaes/apps/helmet
idaes/apps/ripe
idaes/apps/roundingRegression
idaes/models_extra/carbon_capture
Pyomo 6.6
This version of IDAES is the first requiring Pyomo 6.6. This version of Pyomo contains multiple internal improvements and refactorings.While for the majority of cases this should …
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
IDAES Integrated Platform 2.0.0 beta 2 released
Today we’re announcing the 2.0.0b2 release of the IDAES Integrated Platform.
Highlights
This is the second of two pre-releases in preparation for the full 2.0.0 release expected March of 2023
Use this release to migrate your models to the 2.0 API, with an optional backward compatibility mechanism to aid the migration
Detailed documentation on how to migrate is available: IDAES v2 resources
Virtual Office hours are also available by reservation for individual support
New Features
Improved support for defining new components for use in Helmholtz equations of state
Standardized naming in heat exchanger models
Documentation of process costing tools and examples
Implementation of an IDAES Performance Testing Suite, which has been used to establish baseline performance metrics
Improved metadata structure for defining Units of Measurement in property packages and updated all code to use the new format
Plans for Next Release
Drop support for Python 3.7
Add support for Python 3.11
Help and Support
You are invited to make a reservation for one of our monthly virtual/zoom office hours if you would like individual help with the migration or use of the platform. Just send an email to idaes-support@idaes.org with details on your question, issue or situation. The idaes-users@idaes.org email list and github discussion forums are still available at our …
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
IDAES Integrated Platform 2.0.0 alpha 2 released
Today we’re announcing the 2.0.0a2 release of the IDAES Integrated Platform.
This is the first of two public alpha releases before the full 2.0 release of the IDAES IP coming late November / early December.
Please see the details below on the changes in this release and how the goal of this release (and the next alpha release in August/Sept) is to help people prepare for the 2.0 release.
IDAES-PSE 2.0.0a2 Release Highlights
Deprecations and Moved Files:
In preparation for the upcoming IDAES v2.0 release, the IDAES repository has been significantly reorganized to better group content and reduce the number of top-level directories
In order to facilitate a smooth transition, the existing folder structure will remain in place until the v2.0 release in November with redirection links to the new location of most modules
Importing a module from its old location will be redirected to the new location and a deprecation warning logger with a pointer to the new location
A new API has been implemented for costing of process equipment in IDAES
The old costing methods will continue to be supported with deprecation warnings until the v2.0 release in November after which they will be removed
Users of these tools should begin converting their code to use the …
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
IDAES IP 1.13.0 Released and Notice For Upcoming 2.0
Two pieces of news:
We have a new 1.13.0 release of the IDAES Integrated Platform
We are planning a major new 2.0 release in November
ADVANCE CHANGE NOTICE: Upcoming IDAES v2.0
First we want to announce that development is now underway for IDAES 2.0 introducing significant improvements and API changes with respect to the current (v1) codebase. In order to provide advance warning of theses upcoming changes and minimize their impact on functionality during this transition, the following schedule is planned for the next 9 months:
The first stable version of IDAES v2, release 2.0.0, is currently planned for November 2022
The current release series, 1.13, will be the last for the IDAES v1 codebase
Over the intervening months, as part of our quarterly release cycle, we will be releasing preview versions of the v2 codebase.
If applicable, we will also distribute bugfixes that are compatible with the v1 codebase as patch releases to the 1.13 release series (1.13.1, 1.13.2)
During this time, changes to the IDAES API introduced by v2 will be documented as deprecation warnings in the codebase and/or as part of the release notes
Once the 2.0.0 release is out, all deprecation warnings will be removed, and development will switch exclusively to IDAES v2
We encourage our users …
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
IDAES Integrated Platform 1.12.0 released
Highlights for this release are:
Improved workflow and interface for developing surrogate models and incorporating these into flowsheets
Improved wrapper for ALAMO using new surrogate interface
New Unit models:
Condenser and Reboiler for packed column applications
0-D Heat Exchanger using the NTU method
Ability to use external variable for length in 1-D control volumes
Thermophysical and transport properties:
New supported properties: Cp, Cv, diffusivity, thermal conductivity, speed of sound, surface tension, viscosity
Improved formulation of log expressions
Surrogate modeling using Keras
This requires tensorflow, which is an optional dependency of IDAES. To install it, use any one of the following commands:
conda install tensorflow
pip install tensorflow
pip install idaes-pse[optional] # this will install all optional dependencies for IDAES, including tensorflow
Deprecation warnings slated to be removed (and become errors) in next release (1.13.0):
Python 3.6 support is deprecated and it will be removed with the next IDAES release
Deprecation warning: generic_models.unit_models.distillation folder renamed to generic_models.unit_models.column_models
Removed support for directly setting state_block_class and reaction_block_class in property and reaction packages
Removed support for property packages which do not use Phase and Component objects
Removed support for defining units of measurement via strings.
Removed support for dynamic simulations without specifying units for time domain
For detailed instructions on how to get started using the the Integrated Platform, as well as examples, tutorials and references, …
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
IDAES Stakeholders Meeting 10/7 + 10/14
This is our opportunity to present a comprehensive review of progress and directions for IDAES and to gain your valuable feedback. Sadly we are COVID restricted from having the opportunity to do this in person once again this year (Please know how much myself and the entire IDAES team miss the pleasure of interacting more directly with all of you). As we did last October, we are dividing the event into two days a week apart to make it more readily digestible and to offer you time for contemplation of your reactions and input.
To see the full agenda, please login to the Stakeholders -> Meetings page.
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
IDAES Integrated Platform 1.11.0 released
Highlights for this release are:
New component: SkeletonUnitModel – a generic unit model object for surrogate and custom unit models
Improving access to time domain: All Flowsheets now have a ‘time’ property which will return the local time domain
Support for additional properties in Generic Property Framework: Ideal solids, Henry’s Law, molar volume, osmotic pressure, diffusivity and viscosity
Improved scaling methods for Mixer and Separator
Updating some imports from Pyomo due to changes in Pyomo 6.1
Prototype support for precipitation reactions using solubility products (still needs refinement)
Deprecation warnings slated to be removed (and become errors) in next release (1.12.0):
Directly setting state_block_class and reaction_block_class when creating property packages will no longer be allowed: users should set _state_block_class and _reaction_block_class instead.
Old style property packages that do not use Phase and Component objects will no longer be supported (i.e. those that create the phase_list and component_set directly).
IDAES will no longer support using strings to defining units of measurement in property packages. Property developers should use Pyomo units (or None) instead.
Users will need to define units for the time domain in dynamic simulations.
For detailed instructions on how to get started using the the Integrated Platform, as well as examples, tutorials and references, take a look at our online documentation.
As …
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
IDAES PSE Framework version 1.10.0 released
For detailed instructions on how to get started using the Framework, as well as examples, tutorials and references, take a look at our online documentation.
Highlights for this release are:
Updates to be compliant with Pyomo 6
New get_solver tool for creating solver objects which supports definition of default solvers and options
Improved handling of solver options during initialization
Additional properties in Generic Property framework, including multiple flow bases, internal energy, and constant volume heat capacity.
New Generic Property module for constant pure component properties
Extension of core framework to support aqueous solutions of ions, including support for true and apparent species compositions
Performance curves in isentropic pressure changers
New utility functions for accessing scaled and unscaled Jacobians and problem condition number
Support for automatic scaling tools in Generic Property and Reaction packages
Numerous improvements to automatic scaling functions
Improved output of stream tables for cases of multiple property packages with different state variables
Fixed bug in some property packages where enthalpy density was used instead of internal energy density in energy balances
Fixed underlying bug preventing cloning of models, which was necessary for GDP problems
Development activity and full source code can be found at our idaes-pse github repo.
For support questions and answers visit the new idaes-pse discussion forum.
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
IDAES PSE Framework version 1.9.0 released
For detailed instructions on how to get started using the Framework, as well as examples, tutorials and references, take a look at our online documentation.
Highlights for this release are:
Addition of a general-purpose IDAES dynamic power plant model library.
Improved generic properties API to support disjunctive programming solvers.
New option for including or excluding heat of formation from specific enthalpy calculations.
Functions for producing Txy diagrams of VLE systems.
Improved performance of units of measurement features.
Improved the look, feel, and functionality of the IDAES Flowsheet Visualizer or IFV.
New conda install packaging (in addition to pip install)
Development activity and full source code can be found at our idaes-pse github repo.
For support questions and answers visit the new idaes-pse discussion forum.
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
DOE’s IDAES Recognized With Prestigious R&D 100 Award
The U.S. Department of Energy’s (DOE’s) Institute for the Design of Advanced Energy Systems (IDAES) is the winner of a prestigious 2020 R&D100 award, which recognizes the developers of the 100 most technologically significant products introduced into the marketplace in the last year.
IDAES, which won in the category of software/services, develops next-generation computational tools for process systems engineering (PSE) of advanced energy systems, enabling their rapid design and optimization.
Click here to learn more.
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
Download Open-Source IDAES PSE Framework
The first-of-its-kind IDAES Process Systems Engineering (PSE) Framework is now available as open-source software accessible to a broad range of energy stakeholders seeking to accelerate and optimize the design of advanced energy systems.
The IDAES PSE framework source code is hosted by GitHub. Click here to learn more.
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
IDAES Online Workshop – May 2020
This workshop will cover the basics of IDAES, how to install, and a very brief introduction to Pyomo. Attendees will also learn how to set up a unit model, optimize flowsheets and estimate parameters associated with a property package within IDAES.
Registration: Please register for the workshop to receive additional learning materials and videos after the workshop concludes.
Tutorial 1 – 05/08/2020
Installation (Install Demo speaker notes)
Python and Pyomo Basics
IDAES 101
Tutorial video link
Tutorial 2 – 05/13/2020
Flash unit model
Parameter Estimation (NRTL)
Tutorial video link
Tutorial 3 – 05/14/2020
Flowsheet simulation and optimization
Visualization demo
Tutorial video link
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
IDAES Workshop, during FOCAPD Meeting, July 17, 2019
An IDAES workshop workshop was held during the Foundations of Computer-Aided Process Design (FOCAPD) meeting on July 17, 2019 at the Copper Mountain Resort, Colorado.
Spring Stakeholder Advisory Board Meeting, May 16-17, 2019
Our annual spring stakeholder advisory board meeting was held May 16-17 at the Bethesda North Marriott Hotel & Conference Center just outside Washington, DC.
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
Fall Stakeholder Meeting (co-located with AIChE), November 1, 2018
The IDAES Stakeholder Meeting was held in conjunction with the AIChE Meeting in Pittsburgh, November 1, 2018.
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
Learn About IDAES at the PSE 2018 Conference
The Process Systems Engineering Conference was held July 1-5, 2018 in San Diego, California. The IDAES team presented several talks and posters at the conference. If you are interested in learning more about IDAES, please check out our talks and posters. See this agenda for a list of our activities at the conference: IDAES at PSE 2018 (PDF)
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 Article
- 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
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.
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.
Please use the following to cite the IDAES project or software
Stinchfield, G., Morgan, J. C., Zamarripa, M., Laird, & C. D. MILP Approaches for Optimal Process Family Design. In progress, invited to FOCAPO special issue.
Cortes, N., Eslick, J., Noring, A., Susarla, N., Okoli, C., Zamarripa-Perez, M., Allan, D., Brewer, J., Iyengar, A., Burgard, A., Miller, D., & Dowling, A. W. (2023). Market Optimization and Technoeconomic Analysis of Hydrogen-Electricity Coproduction Systems. Submitted to Joule.
Ammari, B. L., Johnson, E. S., Stinchfield, G., Kim, T., Bynum, M., Hart, W. E., Pulsipher, J., & Laird, C. D. (2023). Linear Model Decision Trees as Surrogates in Optimization of Engineering Applications. Submitted to Computers and Chemical Engineering.
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
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
Stinchfield, G., Morgan, J. C., Zamarripa, M., Laird, & C. D. MILP Approaches for Optimal Process Family Design. In progress, invited to FOCAPO special issue.
Cortes, N., Eslick, J., Noring, A., Susarla, N., Okoli, C., Zamarripa-Perez, M., Allan, D., Brewer, J., Iyengar, A., Burgard, A., Miller, D., & Dowling, A. W. (2023). Market Optimization and Technoeconomic Analysis of Hydrogen-Electricity Coproduction Systems. Submitted to Joule.
Ammari, B. L., Johnson, E. S., Stinchfield, G., Kim, T., Bynum, M., Hart, W. E., Pulsipher, J., & Laird, C. D. (2023). Linear Model Decision Trees as Surrogates in Optimization of Engineering Applications. Submitted to Computers and Chemical Engineering.
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
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
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
Qi Chen, IE Grossmann. 2019. “Effective GDP optimization models for modular process synthesis.” Ind. Eng. Chem. Res. Available online. (accepted) (Task 3.1)
Chen, Q. and Grossmann, I.E. “Effective GDP optimization models for modular process synthesis”, Current Opinion in Chemical Engineering. Accepted (2018) (Task 2.7)
ES Rawlings, Qi Chen, IE Grossmann, JA Caballero. 2019. “Kaibel column: Modeling, optimization, and conceptual design of multi-product dividing wall columns.” Comp. Chem. Eng. 125, 31-39. (Task 3.1)
C.O. Okoli, A. Ostace, S. Nadgouda, A. Lee, A. Tong, A.P. Burgard, D. Bhattacharyya, D.C. Miller. 2019. “A framework for the optimization of chemical looping combustion processes.” Powder Technology, https://doi.org/10.1016/j.powtec.2019.04.035 (Task 3.2)
Thierry, D. and Biegler, L. T. (2019), Dynamic real‐time optimization for a CO2 capture process. AIChE J. doi:10.1002/aic.16511 (Task 2.3)
Hanselman, C.L., Tafen, D.Y., Alfonso, D.R., Lekse, J.W., Matranga, C., Miller, D.C., Gounaris, C.E. “Tuning Oxygen Desorption in a Doped BaFe1-xInxO3 Perovskite Oxygen Carrier”, Computers & Chemical Engineering. submitted. (Task 2.4)
David L. Woodruff, Andrea Staid, Bethany Nicholson, and Katherine Klise, “PARMEST: PARAMETER ESTIMATION VIA PYOMO”, submitted to Foundations of Computer Aided Process Design 2019. (Task 3.2.3)
Sitter, S., Chen, Q., and Grossmann, I.E. “An Overview of Process Intensification Methods”, Submitted for publication (2018) (Task 2.7)
Lara, C.L., Mallapragada, D., Papageorgiou D., Venkatesh, A., Grossmann, I.E. “Electric Power Infrastructure Planning: Mixed-Integer Programming Model and Nested Decomposition Algorithm”, European Journal of Operational Research, (2018), In press. (Task 2.8)
Sauk, B., Ploskas, N., Sahinidis, N. (2018). “GPU parameter tuning for tall and skinny dense linear least squares problems,” Optimization Methods and Software. doi:10.1080/10556788.2018.1527331. (Task 3.2.1)
Rodriguez, J.S., Nicholson, B., Laird, C.D., and Zavala, V.M. “Benchmarking ADMM in Nonconvex NLPs”, Computers & Chemical Engineering, accepted (2018) (Task 3.2.3)
Yu, M., D.W. Griffith, and L.T. Biegler, Nonlinear Programming Formulations for Nonlinear and Economic Model Predictive Control, In Handbook of Model Predictive Control, S. Rackovic and W. Levine (eds.), to appear. (in preparation)
D. Miller, J. Siirola, D. Agarwal, A. Burgard, A. Lee, J. Eslick, B. Nicholson, C. Laird, L. Biegler, D. Bhattacharyya, N. Sahinidis, I. Grossmann, C. Gounaris & D. Gunter, “Next Generation Multi-Scale Process Systems Engineering Framework”, Computer Aided Chemical Engineering, 44, 2209-2214 (2018)
Wan, W., J.P. Eason, B. Nicholson, and L.T. Biegler, Parallel Cyclic Reduction Decomposition for DynamicOptimization Problems, Computers and Chemical Engineering, accepted for publication. (2017)
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)
Nicholson, B.L., J.D. Siirola, J.-P. Watson, V.M. Zavala, and L.T. Biegler. pyomo.dae: A Modeling and Automatic Discretization Framework for Optimization with Differential and Algebraic Equations, Math Programming Computation (2017)
C.L. Hanselman and C.E. Gounaris. A Mathematical Optimization Framework for the Design of Nanopatterned Surfaces. AIChE Journal, 62(9):3250-3263. (2016)