Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

Recent progress in gasoline surrogate fuels

Many industrial catalysts contain isolated metal sites on the surface of oxide supports. While such catalysts have been used in a broad range of processes for more than 40 years, there is often a very limited understanding about the structure of the catalytically active sites. This review discusses how Surface Organometallic Chemistry (SOMC) engineers surface sites with well-defined structures and provides insight about the nature of active sites in industrial catalysts, focusing in particular on olefin production and conversion processes

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Bridging the Gap between Industrial and Well-Defined Supported Catalysts

Machine learning algorithms are attracting significant interest for predicting complex chemical phenomenon. In this work, a model to predict research octane number (RON) and motor octane number (MON) of pure hydrocarbons, hydrocarbon-ethanol blends, and gasoline–ethanol blends has been developed using artificial neural networks (ANNs) and molecular parameters from 1H nuclear magnetic resonance (NMR) spectroscopy. RON and MON of 128 pure hydrocarbons, 123 hydrocarbon–ethanol blends of known composition, and 30 FACE (fuels for advanced combustion engines) gasoline–ethanol blends were utilized as a data set to develop the ANN model. The effect of weight percent of seven functional groups including paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic −CH═CH2 groups, naphthenic CH–CH2 groups, aromatic C–CH groups, and ethanolic OH groups on RON and MON was studied. The effect of branching (i.e., methyl substitution), denoted by a parameter termed as branching index (BI), and molecular w...

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Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks

An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spect...

Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

Based on an approach to finding structure-property relationships, we constructed models for calculating the octane and cetane numbers of individual hydrocarbons (alkanes and cycloalkanes). The models obtained for octane numbers are superior to well-known analogous models described in the literature in terms of the accuracy and predictive efficiency. This is the first attempt to develop a model of this kind for the cetane number. The results of the calculations of these characteristics for a number of unsynthesized and uncharacterized compounds are given.

Models for the calculation and prediction of the octane and cetane numbers of individual hydrocarbons

A new method proposed for solving QSPR tasks is based on transition from numerical values to topological equivalents (TEs) of physicochemical properties of chemical compounds. The TEs are unambiguously related to corresponding properties; for n-alkanes, they are linear functions of the number, n, of carbon atoms. Since the TE depends only on the corresponding physicochemical parameter, it can be calculated for any hydrocarbon using the same relationships as those known for n-alkanes. The optimal topological index (OTI) constructed using the chemical structure matrix for TEs usually has a much smaller basis compared to the topological index obtained by analogous procedure for the physicochemical property. An algorithm for modeling of physicochemical properties using the TEs was developed and evaluated taking the octane numbers of alkanes and cycloalkanes as examples.

Octane numbers (ONs) of hydrocarbons: a QSPR study using optimal topological indices for the topological equivalents of the ONs

1.

The addition of Ag2O, Cu2O, Fe, and Rh at 0.02 mole% to the acid catalysts H2SO4, (BF3·H2O)·(BF3·H3PO4), and a 3∶1 mixture of BF3·2CH2ClCOOH+BF3·2CH3COOH in the carbonylation of olefins and alcohols increased the conversion of them into carboxylic acids (3–8 times).




2.

On multiple use of the studied catalytic systems in the carbonylation of olefins and alcohols a fall in their activity was observed.




3.

To achieve a high (∿70%) yield of carboxylic acids in the carbonylation of olefins and alcohols with carbon monoxide at atmospheric pressure, the presence of acid catalysts with a Hammett acidity value Ho⩾−7.0 is necessary in addition to Cu2O and Ag2O activating CO.

Carbonylation of olefins and alcohols at atmospheric pressure in the presence of acid catalysts with added Ag2O or Cu2O

1.

The catalyst NiO-SiO2 acquires activity in the reaction of dimerization of C2H4 when Al2O3 is introduced into it.




2.

With increasing Al2O3 content in the catalyst, the conversion of C2H4 and i-C4H8 and the number of electron acceptor centers, recordable by ESR, pass through a maximum (1–3% Al), while the total acidity of the catalyst increases.

https://link.springer.com/content/pdf/10.1007/BF00922780.pdf

Influence of the composition of the catalytic system NiO-SiO2-Al2O3 on its catalytic properties in reactions of oligomerization of ethylene and isobutylene

1.

The most active and selective catalysts for carbonylation of L-menthol, D,L-menthol, p-menthene, and 2,6-dimethyl-2,7-octadiene are BF3·2C2H5COOH, BF3·2CH3COOH, BF3·2CH2C1COOH, BF3·2CH2C1COOH, and BF3·2CH2C1COOH-BF3·2CH3COOH (3∶1).




2.

The primary products of the carbonylation of L-menthol, D,L-menthol, and p-menthene-1 are trans- and cis-p-menthane-l-carboxylic and trans-p-menthane-8-carboxylic acids. 3. The carbonylation of 2,6-dimethyl-2,7-octadiene in the presence of BF3-complex catalysts proceeds by cyclization to cyclohexyl and cycloheptyl carbonium ions, reacting with CO and H2O with the formation of 1,2,2,6-tetramethylcyclohexane-1-carboxylic acid (70–75%), a mixture (3∶1) of 1,4,4-trimethyl- and 3,3,7-trimethylcycloheptane-1-carboxylic acids (10–12%), and also a mixture of cis-p-menthane-4-carboxylic and trans-p-menthane-8-carboxylic acids (15–20%).

T. N. Myshenkova

Papers

Models for the calculation and prediction of the octane and cetane numbers of individual hydrocarbons

Octane numbers (ONs) of hydrocarbons: a QSPR study using optimal topological indices for the topological equivalents of the ONs

Carbonylation of olefins and alcohols at atmospheric pressure in the presence of acid catalysts with added Ag2O or Cu2O

Influence of the composition of the catalytic system NiO-SiO2-Al2O3 on its catalytic properties in reactions of oligomerization of ethylene and isobutylene

Carbonylation of terpenes