Filtern
Dokumenttyp
Sprache
- Englisch (3)
Schlagworte
- Self-reactive substances (3)
- Organic peroxides (2)
- QSPR (2)
- Genetic algorithm (1)
- HAZPRED (1)
- Quantitative Structure-Property Relationships (1)
- SADT (1)
- Thermal stability (1)
Organisationseinheit der BAM
Predecitive methods for determining the thermal decomposition properties of hazardous substances
(2017)
For substance classes like organic peroxides and self-reactive substances a model should be developed to predict thermal decomposition properties like the Self-Accelerating Decomposition Temperature (SADT). The applied Quantative Structur-Property Relationship (QSPR) model correlates the molecular structur with the properties of the substances, whereby a consolidated database is a precondition for a reliable model and finally for the prediction.
Predictive Methods for Determining the Thermal Decomposition Properties of Hazardous Substances
(2019)
Due to the fast development and availability of computers, predictive approaches are increasingly used in the evaluation process of hazardous substances complementary to experiments. Their use was recommended as alternative to experimental testing by the REACH regulation to complete the lack of knowledge on properties for existing substances that must be registered before 2018 (upon quantities). Among the proposed predictive approaches, Quantitative Structure Property Relationships (QSPR) are powerful methods to predict macroscopic properties from the only molecular structure of substances. In that context, the HAZPRED project (2015-2018, founded by the SAF€RA consortium) aims to develop theoretical models (e.g. QSPR) and small-scale tests to predict complex physico-chemical properties (e.g. thermal stability, explosivity) of hazardous substances to complete the lack of knowledge on these hazardous substances quickly or to understand their decomposition behaviour better. In particular, this contribution will present the work done in this project on the physical hazards of organic peroxides and self-reactive substances: gathering of existing experimental data, new experimental campaigns, review of existing models and proposition of new estimation methods.
Self-reactive substances are unstable chemical substances which can easily decompose and may lead to explosion in transport, storage, or process situations. For this reason, their thermal stability properties are required to assess possible process safety issues and for classification purpose. In this study, the first quantitative structure–property relationships (QSPR) dedicated to this class of compounds were developed to predict the heat of decomposition of possible self-reactive substances from their molecular structures. The database used to develop and validate the models was issued from a dedicated experimental campaign on 50 samples using differential scanning calorimetry in homogeneous experimental conditions. QSPR models were derived using the GA-MLR methods (using a genetic algorithm and multi-linear regressions) using molecular descriptors calculated by Dragon software based on two types of inputs: 3D molecular structures determined using the density functional theory (DFT), allowing access to three-dimensional descriptors, and from SMILES codes, favoring the access to simpler models, requiring no preliminary quantum chemical calculations. All models respected the OECD validation guidelines for regulatory ac
ceptability of QSPR models. They were tested by internal and external validation tests and their applicability domains were defined and analyzed.