Prediction of the Reactivity Hazards for Organic Peroxides Using the QSPR Approach

Abstract

Organic peroxides can thermally decompose and may lead to runaway reactions. These reactivity hazards have been reported as one of the main causes for fire and explosion in process industries. The risk associated with runaway reactions of organic peroxides can be minimized by employing the inherently safer design (ISD) principles: substitution and moderation of hazardous peroxides in chemical processes. However, the application of ISD concepts requires lengthy evaluations and classification of reactivity hazards of organic peroxides, which are impractical to be done through experimental approach due to the large number of peroxide members and great variations in their reactivity hazards. In this work, the quantitative structure-property relationship (QSPR) was used to predict the detected onset temperature (T 0 ) and heat of decomposition (Q d ) of organic peroxides for the estimation of thermal stability and severity of runaway reactions. In addition to the conventional descriptors used in QSPR, we used other properties of the peroxides such as concentration and the number of peroxide functional group as additional descriptors to construct accurate and economic models to predict reactivity hazards. Partial least-squares method (PLS) and multiple linear regression method (MLR) were used to model T 0 and Q d . It was found that the regression model derived from the PLS method give better prediction for reactivity hazards of organic peroxides. Sensitivity analyses were also performed to identify important descriptors contributing to the reactivity hazards of organic peroxides. The proposed study describes a new approach to identify the reactivity hazards of organic peroxides that may lead to safe practices in the process industries.

Thermal decompositions of dialkyl peroxides studied by DSC

“…The heats of decomposition and the exothermic onset temperatures of DTBP, DAPO, and DCPO were computed by quantitative structure-property relationship (QSPR) disregarding activation energy and frequency factor [33]. In this study, most of the decomposition enthalpies were consistent with experimental results and data published in literature 16.…”

“…Nevertheless, the exothermic onset [6]. The bond dissociation energy of dimethyl peroxide from 158 to 167 kJ mol -1 was reported and very close to those of DTBP reported in literature [7,33]. The lower bond dissociation energy at about 125 kJ mol -1 was estimated in diacyl peroxide with the suggestion of more complicated homolysis related to molecular orbitals of ground and excited states [7].…”

“…1-5, respectively. A quantitative structure-property relationship (QSPR) method was used to predict the onset temperature (T 0 ), the lowest temperature at which the instrument detected exothermic activity, and the heat of decomposition of organic peroxides was proposed by Lu et al [33]. Comparison of experimental values of reactivity hazards of di-alkyl peroxides with literature 33 is listed in Table 4.…”

“…The regression models of PLS (partial least-square) and MLR (multiple linear regression) were used to calculate the detected onset temperature and enthalpy of decomposition in Ref. [33]. For DTBP, the detected onset temperatures were calculated to be 141.6 (MLR), 145.9°C (PLS), and 139.7°C (experimental) and the heat of decomposition was calculated to be -149.6 kJ mol -1 (MLR), -155.1 kJ mol -1 (PLS), and -158.3 kJ mol -1 (experimental), respectively.…”

Self-accelerating decomposition temperature and quantitative structure–property relationship of organic peroxides

“…Morrill and Byrd (2008) and Fayet et al (2009) studied energetic materials such as nitro-aromatic compounds, and established QSPR model with a satisfying predicting result. Lu et al (2011) studied 16 organic peroxides, and derived two QSPR models to predict onset temperature (T o ) and heat of decomposition (Q d ), both of them had good predictive ability. But there are only 16 samples in their data sets which cover little part of organic peroxide groups, and the descriptors included in the model are too many and complicated, e.g., 13 descriptors are used in the model to predict T o. T o and Q d are also not commonly used in industrial and transportation processes.…”