Demand response for flat nonlinear MIMO processes using dynamic ramping constraints

Industry 4.0 has embraced process models in recent years, and the use of model-based digital twins has become even more critical in process systems engineering, monitoring, and control. However, the reliability of these models depends on the model parameters available. The accuracy of the estimated parameters is, in turn, determined by the amount and quality of the measurement data and the algorithm used for parameter identification. For the definition of the parameter identification problem, the ordinary least squares framework is still state-of-the-art in the literature, and better parameter estimates are only possible with additional data. In this work, we present an alternative strategy to identify model parameters by incorporating differential flatness for model inversion and neural ordinary differential equations for surrogate modeling. The novel concept results in an input-least-squares-based parameter identification problem with significant parameter sensitivity changes. To study these sensitivity effects, we use a classic one-dimensional diffusion-type problem, i.e., an omnipresent equation in process systems engineering and transport phenomena. As shown, the proposed concept ensures higher parameter sensitivities for two relevant scenarios. Based on the results derived, we also discuss general implications for data-driven engineering concepts used to identify process model parameters in the recent literature.

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Process Model Inversion in the Data-Driven Engineering Context for Improved Parameter Sensitivities

Selvarajan

Tappe

Heiduk

et al. 2022

Processes

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Demand response for flat nonlinear MIMO processes using dynamic ramping constraints

Cited by 1 publication

References 37 publications

Process Model Inversion in the Data-Driven Engineering Context for Improved Parameter Sensitivities

Process Model Inversion in the Data-Driven Engineering Context for Improved Parameter Sensitivities

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