Article ID Journal Published Year Pages File Type
4980903 Process Safety and Environmental Protection 2016 24 Pages PDF
Abstract
Due to the combustible nature of hydrocarbons, accurate safety information for their safe utilization and transport is essential. The autoignition temperature (AIT) is an important safety parameter. In this study, a quantitative structure-property relationship (QSPR) approach was utilized to present a predictive model for the estimation of the AIT of 813 hydrocarbons from 69 different chemical families. A unified three-parameter model was constructed combining multivariate linear regression (MLR) and a genetic algorithm (GA). In order to investigate the complex performance of the selected molecular parameters an optimized artificial neural network (ANN) model was developed. The resulting models showed good prediction ability. Although the ANN prediction results are more accurate than the GA-MLR model, the application of the latter is more convenient.
Related Topics
Physical Sciences and Engineering Chemical Engineering Chemical Health and Safety
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