Meta-modeling based efficient global sensitivity analysis for wastewater treatment plants – An application to the BSM2 model

Al, Resul; Behera, Chitta Ranjan; Zubov, Alexandr; Gernaey, Krist V.; Sin, Gürkan

doi:10.1016/j.compchemeng.2019.05.015

Cited by 64 publications

(33 citation statements)

References 43 publications

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“…Sobol global sensitivity analysis is a method based on variance decomposition, which can determine the sensitivity of individual factors and factor coupling by calculating the contribution of single and multiple factors to the total variance [29]. The most commonly used Sobol sensitivity indices are the first-order Sobol indices S i and the total-order Sobol indices S Ti [30]. S i shows the individual influences of each input variable on the output response, indicating that the contributions of each variable without considering the mutual effects of the variables, which is called the main effect.…”

Section: Beginmentioning

confidence: 99%

Bayesian Optimization Design of Inlet Volute for Supercritical Carbon Dioxide Radial-Flow Turbine

et al. 2021

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The radial-flow turbine, a key component of the supercritical CO2 ( S-CO2) Brayton cycle, has a significant impact on the cycle efficiency. The inlet volute is an important flow component that introduces working fluid into the centripetal turbine. In-depth research on it will help improve the performance of the turbine and the entire cycle. This article aims to improve the volute flow capacity by optimizing the cross-sectional geometry of the volute, thereby improving the volute performance, both at design and non-design points. The Gaussian process surrogate model based parameter sensitivity analysis is first conducted, and then the optimization process is implemented by Bayesian optimization (BO) wherein the acquisition function is used to query optimal design. The results show that the optimized volute has better and more uniform flow characteristics at design and non-design points. It has a smoother off-design conditions performance curve. The total pressure loss coefficient at the design point of the optimized volute is reduced by 33.26%, and the flow deformation is reduced by 54.55%.

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Section: Beginmentioning

confidence: 99%

Bayesian Optimization Design of Inlet Volute for Supercritical Carbon Dioxide Radial-Flow Turbine

et al. 2021

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“…Cooling time, cooling profile, seed mass, and seed size fraction (including mean and standard deviation) were the operational variables that were selected while yield and nucleation rates were defined as the process performance output. Two sensitivity analysis techniques of Morris screening [30][31][32] and Polynomial Chaos Expansions (PCE)-based Sobol's indices [33,34] as a variance decomposition model were employed.…”

Section: Sensitivity Analysis For Design Of the Validation Experimentsmentioning

confidence: 99%

Independent Validation of an In Silico Tool for a Pilot-Scale Pharmaceutical Crystallization Process Development

et al. 2021

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There are many published models for predicting crystal size distribution (CSD) in the literature. However, none of them have been independently and comprehensively tested, which is important for industrial acceptance and confidence of these models. Therefore, in this study, using solubility and kinetic data from the literature, an in silico tool for predicting the crystallization process performance of a model compound system (paracetamol in ethanol) was developed and challenged by independent experiments at the 50 L pilot scale. The solute concentration was tracked, and the final CSD was quantified using three measurement techniques including a novel image analysis tool. The reported parameter uncertainties were also addressed using Monte Carlo simulations. The results showed that, when the models were used within their validity range (e.g., suspended solids), they were able to describe the observed process trends/dynamics (CSD and solute concentration) under varying experimental conditions (cooling time and seed mass) with R2 ranging from 0.72 and 0.90. Overall, the results indicate that, using Monte Carlo simulations to account for known parametric uncertainties, the models can support model-based approaches for crystallization process development from scale-down to scale-up studies as well as control evaluation.

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“…Once this approximation of the model output is obtained, the total and partial variance of the function can directly be computed due to the orthogonality of the polynomials [47,51]. Once the variances are obtained, the Sobol sensitivity indices can be computed.…”

Section: Polynomial Chaos Expansionsmentioning

confidence: 99%

Comprehensive development, uncertainty and sensitivity analysis of a model for the hydrolysis of rapeseed oil

Forero-Hernandez

Jones

Sarup³

et al. 2020

Computers & Chemical Engineering

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Meta-modeling based efficient global sensitivity analysis for wastewater treatment plants – An application to the BSM2 model

Cited by 64 publications

References 43 publications

Bayesian Optimization Design of Inlet Volute for Supercritical Carbon Dioxide Radial-Flow Turbine

Bayesian Optimization Design of Inlet Volute for Supercritical Carbon Dioxide Radial-Flow Turbine

Independent Validation of an In Silico Tool for a Pilot-Scale Pharmaceutical Crystallization Process Development

Comprehensive development, uncertainty and sensitivity analysis of a model for the hydrolysis of rapeseed oil

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