A Systematic Approach to SMB Process Model Identification From SMB Process Data

Grosfils, Valérie; Levrie, C.; Kinnaert, Michel; Wouwer, Alain Vande

doi:10.3182/20050703-6-cz-1902.02235

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…On the other hand, the uncertainty of

K_{Glu}

increased slightly from

\pm 14.3

\pm 16.4

. This is consistent with Grosfils et al [ 11 ] where estimating the mass‐transfer coefficient was more difficult than the isotherm parameters,

H_{i}

and

b_{i}

.…”

Section: Resultssupporting

confidence: 89%

“…On the other hand, the uncertainty of K Glu increased slightly from AE14:3 to AE16:4. This is consistent with Grosfils et al [11] where estimating the mass-transfer coefficient was more difficult than the isotherm parameters, H i and b i . The most notable improvement in parameter reliability can be seen in the adsorption equilibrium constant b i , which was difficult to estimate from the batch experimental data.…”

Section: Parameter Analysissupporting

confidence: 92%

“…In general, the estimation problem of SMBs will be a large-scale nonlinear programming (NLP) problem, which may lead to ill-conditioning, [10] as addressed in many past studies. [11][12][13] Tie et al [14] modeled a lab-scale simulated moving bed reactor (SMBR) by Tikhonov regularization that alleviates the illconditioning by penalizing deviation of parameters from reliable reference values, that is, prior knowledge. The reference values, or the initial model parameters, were obtained from a batch experiment in their article.…”

Section: Introductionmentioning

confidence: 99%

“…For this objective, the framework of the model correction method and the errors-in-variable-measured (EVM) estimation formulation [17] are combined. The resulting model was analyzed in two approaches: by validating the model with test data sets, and quantifying parameter uncertainty based on the approximation of parameter distributions to Gaussian [11,18] using sensitivity information of the model [19] instead of rigorous quantification approaches. [20,21] In order to conduct parameter estimation for the complex PDAE model considering as many data sets as possible simultaneously, state-of-the-art computational methods are employed in this study.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Utilization of operation data for parameter estimation of simulated moving bed chromatography

Suzuki

Harada

Sato

et al. 2021

J Adv Manuf & Process

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Many model‐based optimization strategies for simulated moving bed (SMB) processes have been proposed to determine operating conditions efficiently; however, there has not been a technique to utilize operation data to enhance the reliability of a mathematical model. This study focuses on developing a parameter estimation method for SMBs to obtain a reliable model utilizing the operation data collected in SMB plants through daily runs, which may contain measurement errors. To use such data, Tikhonov regularization was employed where the regularization parameter is determined by the framework of the discrepancy principle. The potential of our estimation method has been demonstrated by sugar separation described by a nonlinear isotherm. The parameter estimation was conducted with 20 operation data sets from an SMB pilot plant. The prediction of the resulting model was validated against test data sets, and the confidence regions of the parameters were evaluated. These tests confirmed that the model has improved compared with the model initially obtained.

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“…On the other hand, the uncertainty of