The <scp>IDAES</scp> process modeling framework and model library—Flexibility for process simulation and optimization

Lee, Andrew; Ghouse, Jaffer; Eslick, John; Laird, Carl D.; Siirola, John Daniel; Zamarripa, Miguel; Gunter, Dan; Shinn, John H.; Dowling, Alexander W.; Bhattacharyya, Debangsu; Biegler, Lorenz T.; Burgard, Anthony P.; Miller, David C.

doi:10.1002/amp2.10095

Cited by 66 publications

(33 citation statements)

References 28 publications

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“…In prior work, both cubic equations of state and activity coefficient models have been utilized as analytic methods of representing solubility data, which is necessary for process modeling and optimization frameworks. ,− In this work, the Peng–Robinson (PR) equation of state (EOS) was used to model the solubility of HFCs in the ILs that were liquid at room temperature (i.e., [C 6 C 1 im][I], [C 6 C 1 im][Br], [C 6 C 1 im][Cl], and [P 6,6,6,14 ][Cl]). The PR EOS model is described in detail with eqs – . Two binary interaction parameters were regressed for each HFC/IL mixture, and plots of these fitted models are shown in Figures and .…”

Section: Methodsmentioning

confidence: 99%

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Phase Equilibria and Diffusivities of HFC-32 and HFC-125 in Ionic Liquids for the Separation of R-410A

Baca

Olsen

Valenciano

et al. 2021

ACS Sustainable Chem. Eng.

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Current legislation calling for the phase out of hydrofluorocarbon (HFC) refrigerants is driving a global market shift that has prompted industry and research institutions to investigate new refrigerant mixtures and sustainable separation techniques for recycling refrigerants. The recent American Innovation and Manufacturing (AIM) Act of 2020 requires an 85% phase down of HFC production over the next 15 years. To achieve this goal, azeotropic refrigerant mixtures, such as R-410A composed of 50 wt % HFC-32 (difluoromethane, CH 2 F 2 ) and 50 wt % HFC-125 (pentafluoroethane, CHF 2 CF 3 ), will have to be separated to recycle the lower global warming HFC-32 component. The present work investigates the solubility of HFC-32 and HFC-125 in six ionic liquids (ILs) with halogen anions for the purpose of developing the thermophysical property data required for designing extractive distillation recycling processes and understanding the choice of cation and anion type. A gravimetric microbalance was used to collect isothermal vapor liquid equilibrium data for each of the ILs at 298.15 K and pressures from 0.05 to 1.0 MPa. The Peng−Robinson equation of state was used to model the solubility of the HFCs in the ILs. The solubility of HFC-32 in the ILs showed small differences, while the solubility of HFC-125 had significant variations with respect to the anion type and the cation alkyl chain length. Fick's law was applied to calculate diffusion coefficients for each HFC/IL system. HFC-32 has a greater diffusivity than HFC-125 based on smaller molecular size. The 1-n-hexyl-3-methylimidazolium chloride and the trihexyl(tetradecyl)phosphonium chloride ILs have the highest HFC-125/HFC-32 selectivity at 298.15 K. Based on both the mass uptake and selectivity ratio, these two ILs are potential entrainers for the separation of R-410A using extractive distillation.

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Section: Methodsmentioning

confidence: 99%

“…15,33−44 The PR EOS model is described in detail with eqs 1−15. 45 Two binary interaction parameters were regressed for each HFC/IL mixture, and plots of these fitted models are shown in Figures 3 and 4. The binary interaction parameters are provided in Table 2.…”

Section: Introductionmentioning

confidence: 99%

Phase Equilibria and Diffusivities of HFC-32 and HFC-125 in Ionic Liquids for the Separation of R-410A

Baca

Olsen

Valenciano

et al. 2021

ACS Sustainable Chem. Eng.

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“…The uncertainty in calibrated ROK model parameters induces uncertainty in the objective function of the staged reactor problem. We use the Uncertainty Propagation Toolbox in the IDAES-PSE framework (Lee et al, 2021) to propagate this parametric uncertainty through Eq. 28 using the following first-order error propagation formula (Rooney and Biegler, 2001):…”

Section: Staged Reactor Designmentioning

confidence: 99%

Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information

2022

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Despite the success of multiscale modeling in science and engineering, embedding molecular-level information into nonlinear reactor design and control optimization problems remains challenging. In this work, we propose a computationally tractable scale-bridging approach that incorporates information from multi-product microkinetic (MK) models with thousands of rates and chemical species into nonlinear reactor design optimization problems. We demonstrate reduced-order kinetic (ROK) modeling approaches for catalytic oligomerization in shale gas processing. We assemble a library of six candidate ROK models based on literature and MK model structure. We find that three metrics—quality of fit (e.g., mean squared logarithmic error), thermodynamic consistency (e.g., low conversion of exothermic reactions at high temperatures), and model identifiability—are all necessary to train and select ROK models. The ROK models that closely mimic the structure of the MK model offer the best compromise to emulate the product distribution. Using the four best ROK models, we optimize the temperature profiles in staged reactors to maximize conversions to heavier oligomerization products. The optimal temperature starts at 630–900K and monotonically decreases to approximately 560 K in the final stage, depending on the choice of ROK model. For all models, staging increases heavier olefin production by 2.5% and there is minimal benefit to more than four stages. The choice of ROK model, i.e., model-form uncertainty, results in a 22% difference in the objective function, which is twice the impact of parametric uncertainty; we demonstrate sequential eigendecomposition of the Fisher information matrix to identify and fix sloppy model parameters, which allows for more reliable estimation of the covariance of the identifiable calibrated model parameters. First-order uncertainty propagation determines this parametric uncertainty induces less than a 10% variability in the reactor optimization objective function. This result highlights the importance of quantifying model-form uncertainty, in addition to parametric uncertainty, in multi-scale reactor and process design and optimization. Moreover, the fast dynamic optimization solution times suggest the ROK strategy is suitable for incorporating molecular information in sequential modular or equation-oriented process simulation and optimization frameworks.

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“…The simultaneous approach also leverages the capabilities of large‐scale numerical solvers, which can use exact first and second partial derivatives to solve the resulting parameter estimation problems with high efficiency, as gradient information is fully available. This approach is possible through equation‐oriented (EO) modeling tools such as the open‐source Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES), 20 which utilizes the Pyomo 21 algebraic modeling language 22 …”

Section: Introductionmentioning

confidence: 99%

“…The simultaneous approach also leverages the capabilities of large-scale numerical solvers, which can use exact first and second partial derivatives to solve the resulting parameter estimation problems with high efficiency, as gradient information is fully available. This approach is possible through equationoriented (EO) modeling tools such as the open-source Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES), 20 which utilizes the Pyomo 21 algebraic modeling language. 22 The objective of this work is to demonstrate the use of IDAES to create reaction models suitable for reliably predicting the performance of an iron-based OC over a wide range of process-relevant conditions of the reducer reactor of a CLC process during reactions with CO, H 2 , and CH 4 .…”

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confidence: 99%

Application of an equation‐oriented framework to formulate and estimate parameters of chemical looping reaction models

et al. 2022

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Accurate, predictive reaction models are critical for the design and optimization of chemical looping combustion (CLC) reactors. The formulation and estimation of kinetic parameters for these reaction models using a first‐principles equation‐oriented (EO) approach is particularly beneficial as large amounts of experimental data spanning process‐relevant conditions can be used to estimate parameters in a computationally tractable way. This work demonstrates the application of a novel EO framework to develop reduction reaction kinetic models of an iron‐based CLC oxygen carrier (OC). An optimization problem is formulated to estimate kinetic parameters that provide the best fit to the experimental data. The model predicts the state of the OC with mean square error values of 2.5%–4.4% across the full range of validation data, including multiple reduction cycles.

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The IDAES process modeling framework and model library—Flexibility for process simulation and optimization

Cited by 66 publications

References 28 publications

Phase Equilibria and Diffusivities of HFC-32 and HFC-125 in Ionic Liquids for the Separation of R-410A

Phase Equilibria and Diffusivities of HFC-32 and HFC-125 in Ionic Liquids for the Separation of R-410A

Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information

Application of an equation‐oriented framework to formulate and estimate parameters of chemical looping reaction models

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