Model Based Parameter Estimation

Bock, Hans Georg; Carraro, Thomas; Jäger, Willi; Körkel, Stefan; Rannacher, Rolf; Schlöder, Johannes P.

doi:10.1007/978-3-642-30367-8

Cited by 27 publications

(1 citation statement)

References 21 publications

(48 reference statements)

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“…Finally, the NLP initialization can be chosen from, e.g., previous estimates of p while X can be initialized using the measurements ŷ and/or estimates of the state x. For further details refer to [41,43].…”

Section: Formulation Of Multiple Experiments Model Based Parameter Es...mentioning

confidence: 99%

Viability assessment of a rigid wing airborne wind energy pumping system

Licitra¹,

Koenemann²,

Horn³

et al. 2017

2017 21st International Conference on Process Control (PC)

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Airborne Wind Energy (AWE) refers to a novel technology capable of harvesting energy from wind by flying crosswind patterns with tethered autonomous aircraft. Successful design of flight controllers for AWE systems rely on the availability of accurate mathematical models. Due to the non-conventional structure of the airborne component, the system identification procedure must be ultimately addressed via an intensive flight test campaign to gain additional insight about the aerodynamic properties. In this paper, aerodynamic coefficients are estimated from experimental data obtained within flight tests using an multiple experiments Model-Based Parameter Estimation (MBPE) algorithm.

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Section: Formulation Of Multiple Experiments Model Based Parameter Es...mentioning

confidence: 99%

Viability assessment of a rigid wing airborne wind energy pumping system

Licitra¹,

Koenemann²,

Horn³

et al. 2017

2017 21st International Conference on Process Control (PC)

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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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Leveraging neural networks to estimate parameters with confidence intervals

Mathias,

Weir,

Corbett

et al. 2024

Can J Chem Eng

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This manuscript presents a proof of concept for the estimation of parameters in a bioprocess while providing reliable confidence intervals. Specifically, Bayesian inference is used to estimate the uncertainty in the prediction of a parameter due to the presence of measurement noise in the process. The resultant joint probability distribution is utilized to infer the confidence interval of the resultant estimates. This method is numerically applied using a technique known as nested sampling. This algorithm iteratively samples parameters from a pre‐determined range of values to compare model predictions and obtain a probability density function. One challenge typically associated with this algorithm is in the determination of the prediction error, especially when a high‐fidelity dynamic model is being utilized. For the motivating example in the present manuscript, where a high‐fidelity simulated bioprocess is being considered, the use of the high‐fidelity model provided by Sartorius AG as part of the estimation algorithm poses computational challenges. To overcome this challenge, a universal approximator such as a parameterized neural network is used. This neural network is designed to simulate the results of the first principles model (while also capturing the dependence of the model parameters on the output), and once trained can provide near instantaneous results making the use of nested sampling computationally tractable for the application. Simulation results demonstrate the feasibility and capability of the proposed approach.

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Model Based Parameter Estimation

Cited by 27 publications

References 21 publications

Viability assessment of a rigid wing airborne wind energy pumping system

Viability assessment of a rigid wing airborne wind energy pumping system

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

Leveraging neural networks to estimate parameters with confidence intervals

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