Parameter estimation with improved model prediction for over-parametrized nonlinear systems

Chen, Weifeng; Wang, Baojia; Biegler, Lorenz T.

doi:10.1016/j.compchemeng.2021.107601

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…Complex mathematical models with Michaelis-Menten kinetics are often used to understand how the ingredients react in a batch reactor when it comes to making pharmaceutical drugs. 27 The model equations characterized by ODE 28 are shown in Equation (19). Detailed information on parameters and state variables are listed in Tables 1 and 2, respectively.…”

Section: Batch-reactor Models With Michaelis-menten Kineticsmentioning

confidence: 99%

“…Also, with the aim of designing experiments sequentially and unifying the experimental objective function (design one initial concentration at a time), the initial concentration of the first data batch is the same as in the literature. 28 Then, the initial concentration of second, third, and so forth batches are sequentially designed and conducted in the experimental device until the stopping criterion in Equation ( 16) is satisfied. Index Variable Initial value 28 Designed range Unit…”

Section: Batch-reactor Models With Michaelis-menten Kineticsmentioning

confidence: 99%

“…28 Then, the initial concentration of second, third, and so forth batches are sequentially designed and conducted in the experimental device until the stopping criterion in Equation ( 16) is satisfied. Index Variable Initial value 28 Designed range Unit…”

Section: Batch-reactor Models With Michaelis-menten Kineticsmentioning

confidence: 99%

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Integrated Bayesian parameter estimation with model‐based design of experiments for dynamic processes

Cao,

Chen,

Biegler

2024

AIChE Journal

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Integration of Bayesian parameter estimation (BPE) with model‐based design of experiments (MBDoE) aims to estimate process parameters efficiently and systematically. To achieve more efficient use of resources by iteratively selecting informative experiments based on current knowledge about the parameters, an integrated method with BPE and MBDoE is proposed in this article. A built‐in Markov Chain Monte Carlo is also developed to yield not only the distribution profile of the estimated parameters but also aid in the determination of stopping criteria and the estimation of noise. Two applications of dynamic batch reactor systems are presented. Comparative analysis substantiates the superior performance of this approach in terms of parameter accuracy and the efficiency of experimentation.

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Section: Batch-reactor Models With Michaelis-menten Kineticsmentioning

confidence: 99%

Section: Batch-reactor Models With Michaelis-menten Kineticsmentioning

confidence: 99%

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Integrated Bayesian parameter estimation with model‐based design of experiments for dynamic processes

Cao,

Chen,

Biegler

2024

AIChE Journal

Self Cite

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“…When modelers cannot reliably estimate all of the unknown parameters in their fundamental models, they may choose to estimate a subset of the model parameters, ,,,− or they may adopt a Bayesian approach, where prior information is provided about some or all of the model parameters. − When modelers wish to obtain more accurate estimates for model parameters, they may choose to perform new experiments to obtain additional information. Performing additional experiments can be time-consuming and expensive, so researchers want to carefully select operating conditions so that as much information as possible is gleaned from the new experimental runs.…”

Section: Introductionmentioning

confidence: 99%

Simple Method for Incorporating Prior Parameter Information during Model-Based D-Optimal Design of Experiments for Multi-Response Chemical Process Models

Gibson,

Liu,

Harris

et al. 2024

Ind. Eng. Chem. Res.

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Fundamental models are important for design, control, and optimization of chemical systems but often contain many unknown parameters that require estimation from experimental data. Model-based design of experiments (MBDoE) can be used to plan experiments, leading to accurate parameter estimates and improved model predictions. Computation of MBDoE objective functions is challenging for nonlinear models with many parameters due to a singular Fisher information matrix. Bayesian approaches can be used to overcome this problem. We propose a simplified Bayesian D-optimal objective function and minimum volume ellipsoid (MVE) calculation to test the effectiveness of the Bayesian approach. A pharmaceutical case study is used to show estimates obtained from sequential Doptimal experiments that occupy a smaller MVE than estimates from old data or corner-point experiments, confirming that the proposed approach leads to improved parameter estimates. Estimates from A-optimal and D-optimal experiments are compared, confirming that estimates from D-optimal experiments are better in a D-optimal sense.

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“…is technology not only inherits the connotation of traditional artistic beauty but also integrates multiple factors such as three-dimensional and thinking space. Some industries even integrate touch and smell [11]. And the enterprise is the main body of innovation, to stand firm in the increasingly fierce market competition must have the core technology and intellectual property rights based on continuous innovation.…”

Section: Introductionmentioning

confidence: 99%

Application of Patent Right and Trademark Right in Packaging Design Based on Computer Nonlinear Prediction Systems for Virtual Reality Technology

Xin

Yan

Song

2022

Scientific Programming

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Comprehensively strengthening the protection of intellectual property rights (IPRs) and stimulating the vitality of innovation to foster a new development paradigm, innovation is the primary driving force behind development, and protecting IPR is equal to protecting innovation. China is changing from Chinese products to Chinese brands, the need to comprehensively strengthen IPR protection from the perspective of national strategy, so as to promote the building of a modernized economy, stimulate the innovation vitality of the whole society, and foster a new development paradigm. This paper is based on application of patent right and trademark right in packaging design based on computer nonlinear prediction systems for virtual reality technology; from the perspective of intellectual property protection and innovation promotion, combining with several cases of packaging design in computer nonlinear prediction system, this paper expounds different forms of intellectual property creation in packaging design practice, and the irreplaceable important role that intellectual property protection plays in the follow-up market operation of products. At the same time, the combination of computer nonlinear virtual reality technology and packaging design can provide more powerful support for the creation and experience of commodity packaging virtual world, emphasizing the importance and urgency of the creative industry to improve the knowledge level of intellectual property, and the ability to create, apply, and protect intellectual property.

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Parameter estimation with improved model prediction for over-parametrized nonlinear systems

Cited by 8 publications

References 24 publications

Integrated Bayesian parameter estimation with model‐based design of experiments for dynamic processes

Integrated Bayesian parameter estimation with model‐based design of experiments for dynamic processes

Simple Method for Incorporating Prior Parameter Information during Model-Based D-Optimal Design of Experiments for Multi-Response Chemical Process Models

Application of Patent Right and Trademark Right in Packaging Design Based on Computer Nonlinear Prediction Systems for Virtual Reality Technology

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