No abstract

No abstract

No abstract

No abstract

Flash point is the most important variable used to characterize fire and explosion hazard of liquids. Herein, partially miscible mixtures are presented within the context of liquid-liquid extraction processes. This paper describes development of a model for predicting the flash point of binary partially miscible mixtures of flammable solvents.To confirm the predictive efficacy of the derived flash points, the model was verified by comparing the predicted values with the experimental data for the studied mixtures: methanol + octane; methanol + decane; acetone + decane; methanol + 2,2,4-trimethylpentane; and, ethanol + tetradecane. Our results reveal that immiscibility in the two liquid phases should not be ignored in the prediction of flash point. Overall, the predictive results of this proposed model describe the experimental data well. Based on this evidence, therefore, it appears reasonable to suggest potential application for our model in assessment of fire and explosion hazards, and development of inherently safer designs for chemical processes containing binary partially miscible mixtures of flammable solvents.

No abstract

No abstract

Flash point (FP) is the primary property to evaluate fire hazards of a flammable liquid. In most countries regulations for safe handing, transporting, and storage of liquid chemicals mainly depend on the FPs of liquid chemicals. Due to the advancement of technology in discovery or synthesis of new compounds, FP data are desirable for related industries, but there is often a significant gap between the demand for such data and their availability. Thus, a reliable method to predict the FPs of flammable compounds seems very important in this regard. In the present work a predictive model of FP for organosilicon compounds is proposed via the structure group contribution (SGC) approach. This model is built up by using a training set of 184 organosilicon compounds with the fitting ability (R 2) of 0.9330, the average error of 8.91 K, and the average error in percentage of 2.84%. The predictive capability of the proposed model has been demonstrated on a testing set of 46 organosilicon compounds with the predictive capability (Q 2) of 0.8868, the average error of 11.15 K, and the average error in percentage of 3.66%. Because the known error for measuring FP by experiment is reported to be about 6–10 K, the proposed method offers a reasonable estimate of the FP for organosilicon compounds. Moreover, the proposed SGC model requires only the molecular structure of a compound to estimate its FP, so it also offers an effective way to approximate the FP of a novel chemical for which its quantity is still not readily available for measuring its FP by experiments.