Determination of acidity in metal incorporated zeolites by infrared spectrometry using artificial neural network as chemometric approach

Juybar, Maryam; Khorrami, Mohammadreza Khanmohammadi; Garmarudi, Amir Bagheri; Zandbaaf, Shima

doi:10.1016/j.saa.2019.117539

Cited by 13 publications

(6 citation statements)

References 60 publications

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“…Additionally, the broad band below 700 cm −1 (marked in the figure with arrows), which only appeared in the spectra of iron-modified samples, was deemed to be due to the stretching mode of Fe-O [ 45 ]. The peaks at 810 and 790 cm −1 can be assigned to the stretching vibrations of O-Si-O in SiO 4 2− tetrahedra, respectively [ 46 , 47 ]. Furthermore, the band observed at 1090 cm −1 was due to the stretching modes of the internal M-O bonds in the MO 4 tetrahedra [ 13 ].…”

Section: Resultsmentioning

confidence: 99%

“…The most distinct shape of this peak detected for M36-SiAlFe suggested that the introduction of three types of pillars could slightly change the internal interactions between the components of the framework. Additionally, the stretching bands of M-O-M were confirmed with the peak at 1245 cm −1 [ 47 ]. Last, but not least, the peak at 1630 cm −1 was determined to be related to water molecules physically bonded to the zeolitic framework [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

“…Last, but not least, the peak at 1630 cm −1 was determined to be related to water molecules physically bonded to the zeolitic framework [ 48 ]. The second characteristic region, with broad bands between 4000 and 3000 cm −1 , respectively, indicated the presence of hydroxyl groups attached to the MWW framework [ 47 ]. Thus, the intense peak at 3640 cm −1 was attributed to Brönsted acid sites originating from Si(OH)Al in the supercages of the 10MR channels [ 49 , 50 ], while the one at 3445 cm −1 appeared due to the H-O-H stretching vibrations of the chemisorbed H 2 O molecules [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

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Synthesis, Characterization, and NH3-SCR Catalytic Performance of Fe-Modified MCM-36 Intercalated with Various Pillars

et al. 2023

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Two series of MCM-36 zeolites intercalated with various pillars and modified with iron were synthesized, analyzed with respect to their physicochemical properties, and tested as catalysts for the NH3-SCR process. It was found that the characteristic MWW morphology of MCM-36 can be obtained successfully using silica, alumina, and iron oxide as pillars. Additionally, one-pot synthesis of the material with iron resulted in the incorporation of monomeric Fe3+ species into the framework positions. The results of catalytic tests revealed that the one-pot synthesized sample intercalated with silica and alumina was the most efficient catalyst of NO reduction, exhibiting ca. 100% activity at 250 °C. The outstanding performance of the material was attributed to the abundance of Lewis acid sites and the beneficial influence of alumina on the distribution of iron species in the zeolite. In contrast, the active centers originating from the Fe2O3 pillars improved the NO conversion in the high-temperature range. Nevertheless, the aggregated particles of the metal oxide limited the access of the reacting molecules to the inner structure of the catalyst, which affected the overall activity and promoted the formation of N2O above 300 °C.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Synthesis, Characterization, and NH3-SCR Catalytic Performance of Fe-Modified MCM-36 Intercalated with Various Pillars

et al. 2023

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“…Another application is in the determination of acidity in zeolites with data from FTIR spectroscopy [43]. FF-ANNs were used for analyzing multivariate base on the characteristic absorbance of 11 zeolite samples after metal substitution (Zn, Cu, Ga, and Ag) in the ~3612 cm −1 region.…”

Section: Current Applications Of Anns To Thermodynamics and Transport Phenomenamentioning

confidence: 99%

Application of Artificial Neural Networks to Chemical and Process Engineering

Cavalcanti¹,

Kozonoe²,

Pacheco³

et al. 2021

Deep Learning Applications

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The accelerated use of Artificial Neural Networks (ANNs) in Chemical and Process Engineering has drawn the attention of scientific and industrial communities, mainly due to the Big Data boom related to the analysis and interpretation of large data volumes required by Industry 4.0. ANNs are well-known nonlinear regression algorithms in the Machine Learning field for classification and prediction and are based on the human brain behavior, which learns tasks from experience through interconnected neurons. This empirical method can widely replace traditional complex phenomenological models based on nonlinear conservation equations, leading to a smaller computational effort – a very peculiar feature for its use in process optimization and control. Thereby, this chapter aims to exhibit several ANN modeling applications to different Chemical and Process Engineering areas, such as thermodynamics, kinetics and catalysis, process analysis and optimization, process safety and control, among others. This review study shows the increasing use of ANNs in the area, helping to understand and to explore process data aspects for future research.

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“…A computational speed improvement of 348 times was achieved for stable energy results generated by the NN compared to those calculated by molecular dynamics based on the MMFF force field under the same dataset. Juybar et al 24 employed an NN to predict the zeolite acidity changes loaded with multiple metals. The acidity changes predicted through the NN were compared to those determined by Fourier transform infrared spectroscopy (FTIR).…”

Section: Introductionmentioning

confidence: 99%

Structure–Energy Relationship Prediction of the HZSM-5 Zeolite with Different Acid Site Distributions by the Neural Network Model

Yang,

Zhang,

Chen

et al. 2024

ACS Omega

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Zeolites are a very important family of catalysts. The catalytic activity of zeolites depends on the distribution of acid sites, which has been extensively studied. However, the relationship between the acid site distribution and catalytic efficiency remains unestablished. An onerous computational burden can be imposed when static calculations are applied to analyze the relationship between a catalyst structure and its energy. To resolve this issue, the current work uses neural network (NN) models to evaluate the relationship. By taking the typical HZSM-5 zeolite as an example, we applied the provided atomic coordinates to predict the energy. The network performances of the artificial neural network (ANN) and high-dimensional neural network (HDNN) are compared using the trained results from a dataset containing the identical number of acid sites. Furthermore, the importance of the feature is examined with the aid of a random forest model to identify the pivotal variables influencing the energy. In addition, the HDNN is employed to forecast the energy of an HZSM-5 system with varying numbers of acid sites. This study emphasizes that the energy of zeolites can be rapidly and accurately predicted through the NN, which can assist our understanding of the relationship between the structure and properties, thereby providing more accurate and efficient methods for the application of zeolite materials.

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Determination of acidity in metal incorporated zeolites by infrared spectrometry using artificial neural network as chemometric approach

Cited by 13 publications

References 60 publications

Synthesis, Characterization, and NH3-SCR Catalytic Performance of Fe-Modified MCM-36 Intercalated with Various Pillars

Synthesis, Characterization, and NH3-SCR Catalytic Performance of Fe-Modified MCM-36 Intercalated with Various Pillars

Application of Artificial Neural Networks to Chemical and Process Engineering

Structure–Energy Relationship Prediction of the HZSM-5 Zeolite with Different Acid Site Distributions by the Neural Network Model

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