The sulfuric-acid-catalyzed esterification reaction of 2-butanol and propionic anhydride is a vital industrial process. In this paper, several experiments are conducted via reaction calorimetry to validate that both sulfuric and propionic acids have discernible catalytic effects on the reaction. This finding complicates the accurate description of the reaction kinetics through traditional methods. So this paper turns to a kinetic-free black-box model, Gaussian process regression (GPR) model via 24 experiments, as a more adaptable approach. Besides, the best GPR model is combined with traditional heat balance model to generate a hybrid gray-box model, which can give complete knowledge of reaction process. The hybrid gray-box model finally achieves maximum root-mean-square error (RMSE) of 0.0069 for conversion, 0.6535 for temperature, and 10.6087 for heat flow rate, underscoring its pretty good predictive ability.
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http://dx.doi.org/10.1021/acs.jpcb.4c00248 | DOI Listing |
J Phys Chem B
May 2024
Tianjin Key Laboratory of Chemical Process Safety, Hebei Provincial Key Laboratory of Green Chemical Technology and High Efficient Energy Saving, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China.
The sulfuric-acid-catalyzed esterification reaction of 2-butanol and propionic anhydride is a vital industrial process. In this paper, several experiments are conducted via reaction calorimetry to validate that both sulfuric and propionic acids have discernible catalytic effects on the reaction. This finding complicates the accurate description of the reaction kinetics through traditional methods.
View Article and Find Full Text PDFJ Math Biol
June 2023
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, USA.
We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs)-and the closures that lead to them- from high-fidelity, individual-based stochastic simulations of Escherichia coli bacterial motility. The fine scale, chemomechanical, hybrid (continuum-Monte Carlo) simulation model embodies the underlying biophysics, and its parameters are informed from experimental observations of individual cells. Using a parsimonious set of collective observables, we learn effective, coarse-grained "Keller-Segel class" chemotactic PDEs using machine learning regressors: (a) (shallow) feedforward neural networks and (b) Gaussian Processes.
View Article and Find Full Text PDFJ Chem Phys
June 2023
Fritz Haber Institute of the Max Planck Society, Berlin, Germany.
The increasing popularity of machine learning (ML) approaches in computational modeling, most prominently ML interatomic potentials, opened possibilities that were unthinkable only a few years ago-structure and dynamics for systems up to many thousands of atoms at an ab initio level of accuracy. Strictly referring to ML interatomic potentials, however, a number of modeling applications are out of reach, specifically those that require explicit electronic structure. Hybrid ("gray box") models based on, e.
View Article and Find Full Text PDFBioelectrochemistry
August 2023
Georgia Tech Manufacturing Institute, Georgia Institute of Technology, Atlanta 30332, United States; School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta 30332, United States; H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta 30332, United States.
This paper presents the development of low-cost, disposable impedance-based sensors for real-time, in-line monitoring of suspension cell culture. The sensors consist of electrical discharge machining (EDM) cut aluminum electrodes and polydimethylsiloxane (PDMS) spacers, both of which are low-cost materials that can be safely disposed of. Our research demonstrates the capability of these low-cost sensors for in-line, non-invasive monitoring of suspension cell growth in cell manufacturing.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
February 2019
Accurate modeling of electrochemical cells is nowadays mandatory for achieving effective upgrades in the fields of energetic efficiency and sustainable mobility. Indeed, these models are often used for performing accurate State-of-Charge (SoC) estimations in energy storage systems used in microgrids or powering pure electric and hybrid cars. To this aim, a novel neural networks ensemble approach for modeling electrochemical cells is proposed in this paper.
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