Performance of different optimization concepts for reactive flow systems based on combined CFD and response surface methods

Rößger, Philip; Richter, Andreas

doi:10.1016/j.compchemeng.2017.09.008

Cited by 24 publications

(10 citation statements)

References 35 publications

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“…To solve the reactor model, the ANSYS Fluent computational fluid dynamics (CFD) software is utilized, which has found in recent years extensive application in the modeling of reactive flows. − To analyze for the UV radiation profiles in the reactor, we employ the Discrete Ordinates (DO) component in ANSYS Fluent to solve the RTE. In ANSYS Fluent, the geometry of the problem is divided into certain control volumes (cells) and the CFD software solves the transport/reaction equations (including the radiation transfer equation, eq ) in each control volume.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Siloxane Removal from Biogas Using an Ultraviolet Photodecomposition Technique

Divsalar¹,

Sun²,

Dods³

et al. 2018

Ind. Eng. Chem. Res.

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The goal of this study is to assess the technical feasibility of remediating siloxane contaminants in biogas via a photochemical process. Specifically, we studied in the laboratory a process that involves the use of an ultraviolet (UV) photodecomposition reactor (PhoR) to convert siloxane trace impurities, commonly found in biogas produced in water treatment plants and landfills, into silica particulates. These can then be effectively removed from the reactor effluent with the use of a downstream filter. High siloxane conversions were obtained, which demonstrates the effectiveness of the technique. The proposed technology is presently being field-tested in a California landfill.

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Section: Discussionmentioning

confidence: 99%

Feasibility of Siloxane Removal from Biogas Using an Ultraviolet Photodecomposition Technique

Divsalar¹,

Sun²,

Dods³

et al. 2018

Ind. Eng. Chem. Res.

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“…Rößger and Richter [25] have thoroughly compared state of the art CFD-based optimization methods based on a 2-parameter design problem. For that specific problem it has been demonstrated that direct optimization based on CFD simulation would require 1000 CFD simulations, which is computationally prohibitive.…”

Section: Discussionmentioning

confidence: 99%

“…They can address complex black-box problems, such as reactor geometry [16,17] or impeller configuration [18], but require a large number of function evaluations, which leads to a large number of computationally expensive CFD runs. To improve the computational efficiency of direct CFD simulations, hybrid methods have been proposed that replace direct CFD evaluation with simpler data-driven models [21][22][23][24][25], which are then integrated with GA. Successful implementation have been reported in the literature that use neural network [22,23], Gaussian process [24] and radial basis function (RBF) [25].…”

Section: Introductionmentioning

confidence: 99%

“…To improve the computational efficiency of direct CFD simulations, hybrid methods have been proposed that replace direct CFD evaluation with simpler data-driven models [21][22][23][24][25], which are then integrated with GA. Successful implementation have been reported in the literature that use neural network [22,23], Gaussian process [24] and radial basis function (RBF) [25]. However, building confidence in those data-driven models requires large number of CFD runs, and balancing computational efficiency with accuracy is non-trivial [26].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization

et al. 2018

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Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

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“…Additionally, computational fluid dynamics (CFD) is notorious for being computationally expensive. One CFD calculation can take days or even weeks to complete, even with modern computing power [1,2].…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Surrogate Models for Efficient Performance-Based Decoding of 3D Point Clouds

Cunningham

Simpson

Tucker

2019

Journal of Mechanical Design

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This work investigates surrogate modeling techniques for learning to approximate a computationally expensive function evaluation of 3D models. While in the past, 3D point clouds have been a data format that is too high dimensional for surrogate modeling, by leveraging advances in 3D object autoencoding neural networks, these point clouds can be mapped to a one-dimensional latent space. This leads to the fundamental research question: what surrogate modeling technique is most suitable for learning relationships between the 3D geometric features of the objects captured in the encoded latent vector and the physical phenomena captured in the evaluation software? Radial basis functions (RBFs), Kriging, and shallow 1D analogs of popular deep 2D image classification neural networks are investigated in this work. We find the nonintuitive result that departing from neural networks to decode latent representations of 3D objects into performance predictions is far more efficient than using a neural network decoder. In test cases using datasets of aircraft and watercraft 3D models, the non-neural network surrogate models achieve comparable accuracy to the neural network models. We find that an RBF surrogate model is able to approximate the lift and drag coefficients of 234 aircraft models with a mean absolute error of 1.97 × 10−3 and trains in only 3 seconds. Furthermore, the RBF surrogate model is able to rank a set of designs with an average percentile error of less than 8%. In comparison, a 1D ResNet achieves an average absolute error of 1.35 × 103 in 38 min for the same test case. We validate the comparable accuracy of the four techniques through a test case involving 214 3D watercraft models, but we also find that the distribution of the performance values of the data, in particular the presence of many outliers, has a significant negative impact on accuracy. These results contradict a common perception of neural networks as an efficient “one-size-fits-all” solution for learning black-box functions and suggests that even within systems that utilize multiple neural networks, potentially more efficient alternatives should be considered for each network in the system. Depending on the required accuracy of the application, this surrogate modeling approach could be used to approximate an expensive simulation software, or if the tolerance for error is low, it serves as a first pass which can narrow down the number of candidate designs to be analyzed more thoroughly.

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Performance of different optimization concepts for reactive flow systems based on combined CFD and response surface methods

Cited by 24 publications

References 35 publications

Feasibility of Siloxane Removal from Biogas Using an Ultraviolet Photodecomposition Technique

Feasibility of Siloxane Removal from Biogas Using an Ultraviolet Photodecomposition Technique

Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization

An Investigation of Surrogate Models for Efficient Performance-Based Decoding of 3D Point Clouds

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