Integrating Rate Based Models into a Multi-Objective Process Design & Optimisation Framework using Surrogate Models

Teske, Sinan L.

doi:10.5075/epfl-thesis-6302

Cited by 1 publication

(2 citation statements)

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“…Alternately, machine learning methods can be used to train surrogates such as polynomial regression models (Wang et al, 2018), artificial neural networks (ANNs) (Teske, 2014;Miriyala et al, 2016;Kotidis and Kontoravdi, 2020), or distribution models (Hutter et al, 2021) to emulate the behavior of MK models. For example, Wang et al (2018) correlated combustion reaction data at different operating conditions using neural network surrogate models.…”

Section: Machine Learning Emulatorsmentioning

confidence: 99%

“…However, these strategies have yet to be demonstrated on complex reaction networks such as oligomerization that consider O(10 3 ) elementary reaction steps and multiple products. Machine learning approaches (Teske, 2014;Miriyala et al, 2016;Kotidis and Kontoravdi, 2020), on the other hand, use simulations or experimental data libraries to train neural networks and similar surrogate models to predict the reactor effluent. Although these approaches are not restricted by the MK model size, they require large amounts of data to train and lack the ability to emulate kinetic or thermodynamic behavior outside the range of validation (Miriyala et al, 2016) which can be problematic for reactor optimization.…”

Section: Introductionmentioning

confidence: 99%

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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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