model -basedoptimalexperimentaldesign (OED) is a well known tool for efficient model development. However, it is not used very often. A few reasons for that are: a lack of understanding on how to work with complex ...
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model -basedoptimalexperimentaldesign (OED) is a well known tool for efficient model development. However, it is not used very often. A few reasons for that are: a lack of understanding on how to work with complex OED methods and a small amount of ready -to -use tools available to directly apply OED methods. In the presented contribution OED and sampling strategies are used to categorize OED formulations as nonlinear programs. Different strategies and their combination are analyzed based on performance and robustness. Depending on the availability of measurements, control flexibility of the experimental setup, and model accuracy some strategies are more efficient than others. based on the proposed guidelines, engineers will have a better understanding about which NLP formulation should be used for their specific task. The methods described are available to the community as a part of open -source code developed in Python.
The transition toward green chemicals production requires new processes which are able to handle sustainable feedstock comprising unsaturated, long-chain hydrocarbons. The homogeneously rhodium-catalyzed tandem hydroa...
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The transition toward green chemicals production requires new processes which are able to handle sustainable feedstock comprising unsaturated, long-chain hydrocarbons. The homogeneously rhodium-catalyzed tandem hydroaminomethylation (HAM) is able to convert long-chain olefins to amines and, therefore, represents an example reaction of high interest for the next generation of chemical processes. This work improves upon an existing mechanistic reaction kinetic model for 1-decene in a methanol/dodecane thermomorphic multiphase system (TMS) by structural adjustment of the catalyst pre-equilibrium, the investigation of the water influence on the reaction equilibria as well as the re-estimation of kinetic and inhibition parameters to provide accurate predictions under a wide range of operating conditions. This is achieved by pairing model-based optimal experimental design (mbOED) with perturbed-chain statistical associating fluid theory (PC-SAFT)-based phase equilibrium calculations to account for experiment setup-specific limitations while ensuring monophasic operation throughout all experiments. The subsequent model identification is able to substantially improve the prediction quality of the hydroaminomethylation model in edge cases over the previous model while maintaining a quantitative agreement of experimental and simulated concentration profiles under nominal conditions.
New vapor-liquid equilibrium (VLE) data are continuously being measured and new parameter values, e.g., for the nonrandom two-liquid (NRTL) model are estimated and published. The parameter a , the nonrandomness parame...
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New vapor-liquid equilibrium (VLE) data are continuously being measured and new parameter values, e.g., for the nonrandom two-liquid (NRTL) model are estimated and published. The parameter a , the nonrandomness parameter of NRTL, is often not estimated but is heuristically fixed to a constant value based on the involved components. This can be seen as a manual application of a (subset selection) regularization method. In this work, the practical parameter identifiability of the NRTL model for describing the VLE is analyzed. It is shown that fixing a is not always a good decision and sometimes leads to worse prediction properties of the final parameter estimates. Popular regularization techniques are compared and Generalized Orthogonalization is proposed as an alternative to this heuristic. In addition, the sequential optimalexperimentaldesign and Parameter Estimation (sOED-PE) method is applied to study the influence of the regularization methods on the performance of the sOED-PE loop.
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