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10.3390/catal12030347
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Article

Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches

by
Nirvana Delgado Otalvaro
,
Pembe Gül Bilir
,
Karla Herrera Delgado
*,
Stephan Pitter
and
Jörg Sauer
Institute of Catalysis Research and Technology (IKFT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
*
Author to whom correspondence should be addressed.
Catalysts 2022, 12(3), 347; https://doi.org/10.3390/catal12030347
Submission received: 7 February 2022 / Revised: 10 March 2022 / Accepted: 12 March 2022 / Published: 18 March 2022
(This article belongs to the Special Issue Advances in Catalytic Synthesis and Conversion of Methanol and Dimethyl Ether)
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Abstract

Hybrid kinetic models represent a promising alternative to describe and evaluate the effect of multiple variables in the performance of complex chemical processes, since they combine system knowledge and extrapolability of the (semi-)mechanistic models in a wide range of reaction conditions with the adaptability and fast convergence of data-based approaches (e.g., artificial neural networks—ANNs). For the first time, a hybrid kinetic model for the direct DME synthesis was developed consisting of a reactor model, i.e., balance equations, and an ANN for the reaction kinetics. The accuracy, computational time, interpolation and extrapolation ability of the new hybrid model were compared to those of a lumped and a data-based model with the same validity range, using both simulations and experiments. The convergence of parameter estimation and simulations with the hybrid model is much faster than with the lumped model, and the predictions show a greater degree of accuracy within the models’ validity range. A satisfactory dimension and range extrapolation was reached when the extrapolated variable was included in the knowledge module of the model. This feature is particularly dependent on the network architecture and phenomena covered by the underlying model, and less on the experimental conditions evaluated during model development.
Keywords: hybrid modeling; reaction kinetics; dimethyl ether hybrid modeling; reaction kinetics; dimethyl ether
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MDPI and ACS Style

Delgado Otalvaro, N.; Bilir, P.G.; Herrera Delgado, K.; Pitter, S.; Sauer, J. Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches. Catalysts 2022, 12, 347. https://doi.org/10.3390/catal12030347

AMA Style

Delgado Otalvaro N, Bilir PG, Herrera Delgado K, Pitter S, Sauer J. Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches. Catalysts. 2022; 12(3):347. https://doi.org/10.3390/catal12030347

Chicago/Turabian Style

Delgado Otalvaro, Nirvana, Pembe Gül Bilir, Karla Herrera Delgado, Stephan Pitter, and Jörg Sauer. 2022. "Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches" Catalysts 12, no. 3: 347. https://doi.org/10.3390/catal12030347

APA Style

Delgado Otalvaro, N., Bilir, P. G., Herrera Delgado, K., Pitter, S., & Sauer, J. (2022). Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches. Catalysts, 12(3), 347. https://doi.org/10.3390/catal12030347

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