Juncheng Jiang

A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of lower flammability limits (LFLs) of organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structures of compounds, such as topological, charge, and geometric descriptors. Genetic algorithm was employed to select optimal subset of descriptors that have significant contribution to the overall LFL property. The novel chemometrics method of support vector machine was employed to model the possible quantitative relationship between these selected descriptors and LFL. The resulted model showed high prediction ability that the obtained root mean square error and average absolute error for the whole dataset were 0.069 and 0.051vol.%, respectively. The results were also compared with those of previously published models. The comparison results indicate the superiority of the presented model and reveal that it can be effectively used to predict the LFL of organic compounds from the molecular structures alone.

Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices.

A quantitative structure-property relationship (QSPR) model was constructed to predict the impact sensitivity of 156 nitro energetic compounds by means of artificial neural network (ANN). Electrotopological-state indices (ETSI) were used as molecular structure descriptors which combined together both electronic and topological characteristics of the analyzed molecules. The typical back-propagation neural network (BPNN) was employed for fitting the possible non-linear relationship existed between the ETSI and impact sensitivity. The dataset of 156 nitro compounds was randomly divided into a training set (64), a validation set (63) and a prediction set (29). The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network [16-12-1], the results show that most of the predicted impact sensitivity values are in good agreement with the experimental data, which are superior to those obtained by multiple linear regression (MLR) and partial least squares (PLS). The model proposed can be used not only to reveal the quantitative relation between impact sensitivity and molecular structures of nitro energetic compounds, but also to predict the impact sensitivity of nitro compounds for engineering.

Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine.

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of auto-ignition temperatures (AIT) of organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structures of compounds, such as topological, charge, and geometric descriptors. The variable selection method of genetic algorithm (GA) was employed to select optimal subset of descriptors that have significant contribution to the overall AIT property from the large pool of calculated descriptors. The novel modeling method of support vector machine (SVM) was then employed to model the possible quantitative relationship existed between these selected descriptors and AIT property. The resulted model showed high prediction ability with the average absolute error being 28.88 degrees C, and the root mean square error being 36.86 for the prediction set, which are within the range of the experimental error of AIT measurements. The proposed method can be successfully used to predict the auto-ignition temperatures of organic compounds with only nine pre-selected theoretical descriptors which can be calculated directly from molecular structure alone.

Diesel oil pool fire characteristic under natural ventilation conditions in tunnels with roof openings

In order to research the fire characteristic under natural ventilation conditions in tunnels with roof openings, full-scale experiment of tunnel fire is designed and conducted. All the experimental data presented in this paper can be further applied for validation of numerical simulation models and reduced-scale experimental results. The physical model of tunnel with roof openings and the mathematical model of tunnel fire are presented in this paper. The tunnel fire under the same conditions as experiment is simulated using CFD software. From the results, it can be seen that most smoke is discharged directly off the tunnel through roof openings, so roof openings are favorable for exhausting smoke. But along with the decrease of smoke temperatures, some smoke may backflow and mix with the smoke-free layer below, which leads to fall in visibility and is unfavorable for personnel evacuation. So it is necessary to research more efficient ways for improving the smoke removal efficiency, such as early fire detection systems, adequate warning signs and setting tunnel cap.

Prediction of auto-ignition temperatures of hydrocarbons by neural network based on atom-type electrotopological-state indices

A quantitative structure-property relationship (QSPR) model was constructed to predict the auto-ignition temperature (AIT) of 118 hydrocarbons by means of artificial neural network (ANN). Atom-type electrotopological-state indices were used as molecular structure descriptors which combined together both electronic and topological characteristics of the analyzed molecules. The typical back-propagation (BP) neural network was employed for fitting the possible non-linear relationship existed between the atom-type electrotopological-state indices and AIT. The dataset of 118 hydrocarbons was randomly divided into a training set (60), a validation set (16) and a testing set (42). The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network [16-8-1], the results show that most of the predicted AIT values are in good agreement with the experimental data, with the average absolute error being 21.6 degrees C, and the root mean square error (RMS) being 31.09 for the testing set, which are superior to those obtained by multiple linear regression analysis and traditional group contribution method. The model proposed can be used not only to reveal the quantitative relation between AIT and molecular structures of hydrocarbons, but also to predict the AIT values of hydrocarbons for chemical engineering.

Nano-QSAR modeling for predicting the cytotoxicity of metal oxide nanoparticles using novel descriptors

Computational approaches have evolved as efficient alternatives to understand the adverse effects of nanoparticles on human health and the environment. The potential of using Quantitative Structure–Activity Relationship (QSAR) modeling to establish statistically significant models for predicting the cytotoxicity of various metal oxide (MeOx) nanoparticles (NPs) has been investigated. A novel kind of nanospecific theoretical descriptor was proposed by integrating codes of certain physicochemical features into SMILES-based optimal descriptors to characterize the nanostructure information of NPs. The new descriptors were then applied to model MeOx NP cytotoxicity to both Escherichia coli bacteria and HaCaT cells for comparison purposes. The effects of size variation on the cytotoxicity to both types of cells were also investigated. The four resulting QSAR models were then rigorously validated, and extensively compared to other previously published models. The results demonstrated the robustness, validity and predictivity of these models. Predominant nanostructure factors responsible for MeOx NP cytotoxicity were identified through model interpretation. The results verified different mechanisms of nanotoxicity for these two types of cells. The proposed models can be expected to reliably predict the cytotoxicity of novel NPs solely from the newly developed descriptors, and provide guidance for prioritizing the design and manufacture of safer nanomaterials with desired properties.

Analysis on oxidation process of sulfurized rust in oil tank

The paper focuses on the oxidation process of sulfurized rust in crude oil tank. Firstly, one sort of rust was put into the sulfurization and oxidation experimental apparatus. The chemical compositions and phase of sulfurized rust were analyzed by energy-dispersive X-ray spectrometer–scanning electron microscope technique. The result shows that the main contents are S, Fe and O and give a short length of side and diamond appearance, and a large pore size in structure. The oxidation of sulfurized rust at ambient temperature was investigated, which transferred from electrochemical reactions to chemical reactions. The result of thermal decomposition experiment indicates that the product of electrochemical reactions is ferrous sulfate. Hereafter, the thermo-gravimetric/differential scanning calorimetric (TG/DSC) technique was used to evaluate the self-heating hazards of pre-oxygenized sulfurized rust. The given TG/DSC curves at different heating rates are similar. Every curve consisted of three weightlessness stages and two weight gain stages. The corresponding apparent activation energy values, most probable kinetic model functions and pre-exponential factor values were calculated by the Flynn–Wall–Ozawa method, the Achar–Brindley–Sharp–Wendworth method and the Kissinger method. The final results described the complexity of oxidation process of pre-oxygenized sulfurized rust.

Effect of pipe length on methane explosion in interconnected vessels

A series of experiments have been conducted to study the influence of pipe length on methane‐air mixture explosion in linked vessels. Two kinds of setups, that is, a spherical vessel connected to a pipe and two spherical vessels connected by a pipe, are used. The characteristics of explosion pressure and flame propagation speed in linked vessels are obtained. The influence of flame propagation direction and the ignition position on explosion pressure and the flame propagation speed are also analyzed under different pipe lengths. The experimental results show that the maximum explosion pressure and the pressure rising rate in the secondary vessel increase with pipe length. The maximum explosion pressure and pressure rising rate increase most obviously when the small vessel is used as the secondary vessel. Moreover, the pressure oscillation is more violent. However, the primary vessel explosion pressure changes a little when pipe length changed. The flame propagation speed from the primary vessel to the secondary vessel increased with pipe length, but the flame propagation acceleration decreased with pipe length. When the pipe diameter and length is constant, bigger primary vessel causes higher initial flame propagation speed; smaller secondary vessel causes stronger blocking effect during the flame propagation.

The organic peroxides instability rating research based on adiabatic calorimetric approaches and fuzzy analytic hierarchy process for inherent safety evaluation

This article proposes a new method of instability classification of organic peroxides (ICOP) for assessing the risk of decomposition reaction of organic peroxides, based on the adiabatic calorimetric approaches and fuzzy analytic hierarchy process (FAHP). Tonset is set as instability possibility index. Maximal power density, adiabatic temperature rise, maximum pressure rate, and maximum pressure are set as instability severity index (ISI) with proper weightings by FAHP. Instability possibility index and ISI are converted into ICOP based on risk matrix. The organic peroxides instability can, therefore, be quantified and divided into four levels, acceptable, moderate risk, highly dangerous, and seriously dangerous. Thermal decomposition of di‐tert‐butyl peroxide 25 mass % and tert‐butyl hydroperoxide 68.4 mass % are tested with Vent Sizing Package 2 and Phi‐Tech 1 which has the function of Accelerating Rate Calorimeter, respectively. Thermal decompositions of other organic peroxides are presented from citation. The instability rating results of these organic peroxides are presented to illustrate the validity of the method.

Prediction of Impact Sensitivity of Nonheterocyclic Nitroenergetic Compounds Using Genetic Algorithm and Artificial Neural Network

A quantitative structure–property relationship model was built to predict the impact sensitivity of 186 nonheterocyclic nitroenergetic compounds. The genetic algorithm was employed to select an optimal subset of descriptors that significantly contribute to the impact sensitivity. A nonlinear artificial neural network was employed to fit a possible relationship between the selected descriptors and impact sensitivity. The results are satisfactory for prediction capability, robustness, and generalization. The proposed method can be used to predict the impact sensitivity of nonheterocyclic nitro compounds based on knowledge of the molecular structures.